Introducing DeepOptimize: Buildform’s Always‑On AI for Form Optimization

Why do some forms convert visitors into customers effortlessly while others collect dust? For most of us, it’s a mystery. You pour effort into designing a form – whether it’s a lead-gen signup, a customer onboarding flow, or a survey – and still see users bail out halfway. In fact, the average web form converts only about 1.7% of its visitors into completions . Even among those who start filling out a form, barely 45% actually finish and submit . The drop-off is real: for something as simple as a contact form, as few as 1 in 10 visitors end up clicking “Submit” . The frustrating part? Traditional analytics might show where people drop off, but not why. Is it a confusing question, too many fields, slow load times, or something else entirely? Most form creators are flying blind, unsure how to fix these leaks.

Enter DeepOptimize – an AI co-pilot for your forms that never sleeps. DeepOptimize is Buildform’s upcoming always-on optimization layer, designed to watch over your form’s performance like a personal conversion expert. Think of it as an autopilot for form conversions: it constantly observes real user behavior, identifies patterns behind drop-offs or poor responses, and then suggests – or even auto-applies – improvements to fix the issues in real time. Instead of guessing why users stall, you have an AI agent doing the detective work and tuning for you. The result? More people completing your forms, higher-quality answers, faster iteration on improvements, and a system that gets smarter with every interaction.

The Problem: Blind Spots in Form Performance

Every form on the internet faces a hidden gauntlet of user decisions. One extra required field or a poorly worded question can silently kill your conversion rate. Studies show that removing just one form field (say, going from 4 fields to 3) can boost conversions by almost 50% . Conversely, adding friction like a CAPTCHA can sink completion rates by up to 40% . Unfortunately, many businesses realize these insights only after painful trial and error – if at all. Nearly 36% of marketers never run user tests or optimizations on their forms , meaning over a third of forms are never improved after launch. Those who do run A/B tests see about 10% higher conversion rates on average , proving that systematic optimization pays off.

The core issue is lack of visibility and bandwidth. Traditional form analytics might tell you “70% of users dropped off at the payment step” – useful, but not actionable by itself. Why did they drop off? Was the credit card field too slow to validate? Was the form too long up to that point? Did mobile users struggle with the layout? Answering these questions usually requires a skilled analyst or a lot of manual digging. Most teams either don’t have the time or the expertise, so the mystery remains unsolved and the form remains under-optimized. In a world where forms often mean the difference between a lost lead or a new customer, these blind spots are costly.

Meet DeepOptimize: Your Always-On Form AI Agent

DeepOptimize is built to shine a light on those blind spots and fix them in a fraction of the time. It’s not a one-time form checker, but an always-on AI agent living inside your Buildform forms. The moment your form goes live, DeepOptimize starts observing how real people interact with it – where they click, how long they linger on each question, where they get confused or give up. It’s as if you hired an expert to watch every user’s session, take notes on their behavior, and improve the form continuously. Except this tireless expert is an AI that scales to thousands of users and never takes a coffee break.

What exactly does DeepOptimize do? In simple terms, it finds and fixes the leaks in your form funnel. If most users drop off at question #5, DeepOptimize will spot that pattern and might suggest making that question optional or rephrasing it. If it notices that mobile users scroll furiously (a sign your layout isn’t mobile-friendly), it flags the layout issue. If users are submitting gibberish in a text field, it prompts you to add validation or clearer wording. The AI isn’t just reactive; it also proactively experiments with improvements. DeepOptimize can run small-scale tests by adjusting wording or form flow for a subset of users (with your permission or automatically in “autopilot” mode) to see if conversions improve. Over time, it learns what works best, both for your specific form and by drawing on patterns learned from optimizing many forms across the Buildform platform.

Four Core Goals of DeepOptimize

DeepOptimize’s design is guided by four primary goals that benefit every form creator and respondent:

• Lift Completion Rates: Get more people across the finish line. The AI’s first job is to increase the percentage of visitors who actually fill out and submit your form. Whether it’s by shortening the form, simplifying questions, or streamlining the user journey, DeepOptimize is laser-focused on driving that conversion rate upward.
• Improve Response Quality: It’s not just about more responses, but better responses. DeepOptimize aims to ensure the answers you collect are meaningful and high-quality. That could mean catching one-word answers and nudging users to elaborate, or filtering out spammy submissions. The end result is data you can trust and act on.
• Shorten Iteration Loops: No more waiting weeks for results or burning time on lengthy A/B tests. DeepOptimize dramatically accelerates the testing cycle for form improvements. It diagnoses issues and suggests changes in near-real-time, so you can iterate on your form design in days, not months. It’s like having an optimization team working 24/7, turning each day’s data into actionable tweaks.
• Learn Across the Fleet (with Privacy Protection): Perhaps the most groundbreaking aspect: DeepOptimize learns from trends across all Buildform forms to benefit each user, while strictly protecting privacy. If a particular phrasing or layout trick is boosting conversion on many similar forms (e.g. event registration forms across different clients), the system can suggest it to everyone – without ever revealing any private data from those forms. It’s a network effect for conversion optimization: every form’s improvement can inform another, anonymously and securely.

In short, DeepOptimize is like an intelligent co-pilot constantly fine-tuning your form to meet these four goals. It lifts your conversion rate, boosts the quality of data you get, speeds up your optimization workflow, and continuously gets smarter by learning what works across countless scenarios.

How It Works: Data, AI, and Privacy by Design

To demystify what’s under the hood, let’s walk through DeepOptimize’s architecture. At a high level, it has three layers: telemetry collection, a privacy-first data pipeline, and an AI engine with multiple specialized modules. These pieces work in tandem to monitor and improve your form in a safe, scalable way.

Telemetry – Capturing Real User Behavior

DeepOptimize starts by gathering rich telemetry data from every form interaction (with the user’s consent and in compliance with all regulations, of course). When a user visits a Buildform form, a lightweight TypeScript SDK embedded in the form springs into action. This SDK records granular events such as:

• Clicks & Keystrokes: Every button click, field focus, keypress, and even hesitations (pauses in typing) can be logged. This helps DeepOptimize see, for example, if users are repeatedly backspacing (maybe the field is confusing) or if they never click the “Next” button.
• Scroll Depth & Time Spent: It watches how far users scroll and how long they spend on each section. A user scrolling up and down might indicate they’re looking for something or are confused by the layout. Time spent can reveal which questions cause thinking delays or frustration.
• Device & Context Info: The SDK notes the device type (mobile vs desktop), browser, and other context like screen size or slow network conditions. These factors often explain certain drop-offs (e.g. mobile users dropping due to an unoptimized layout, or old browsers struggling with a script).
• Form Copy and Inputs: DeepOptimize looks at the content of your form itself – the text of questions, help text, etc. – as well as the responses users input (without storing any personally identifiable info, which we’ll cover in privacy). Knowing the wording of a question or the type of input (email, number, essay response) is crucial for the AI to suggest better copy or validation. It also tracks partial responses (Buildform already supports partial submissions), so even if a user doesn’t finish, we know what they had filled in before dropping out.

All this telemetry is streamed in real-time through an event pipeline (powered by Apache Kafka on the backend for reliable, scalable streaming of events). But before any of this data hits our servers, DeepOptimize employs a privacy-first pipeline to safeguard user information.

Privacy-First Data Pipeline

When you hear “we track keystrokes,” you might cringe – isn’t that invasive? We agree! Privacy is non-negotiable, so DeepOptimize was architected with a “privacy by design” ethos. Here’s how we ensure the optimization magic never compromises user trust:

• On-Device PII Stripping: The first gate is right on the user’s device. The Buildform SDK includes an on-device privacy shim that detects and removes personally identifiable information (PII) from telemetry before it ever leaves the browser. For instance, if a user types an email or phone number, that raw data doesn’t get sent to our servers in the telemetry stream. Instead, we might send a hashed or categorized version (e.g. “user entered an email in this field”) without the actual email content. This way, sensitive data stays in the user’s browser or is immediately anonymized. Modern telemetry tools allow redacting or hashing sensitive content midstream – we’ve built that principle in from day one.
• Differential Privacy & Anonymization: On the server side, DeepOptimize aggregates and analyzes behavior across many users, and we apply differential privacy techniques to these aggregates. In plain English, differential privacy adds a small amount of statistical “noise” to the data analysis such that no individual’s data can be isolated . The patterns still shine through (e.g. “20% of users drop Q4”), but you can’t pinpoint one specific user’s actions. This is the same kind of privacy technology used by the U.S. Census and other heavy-duty data stewards to allow insights while guaranteeing individual anonymity.
• Regional Data Isolation: We respect data residency. DeepOptimize is deployed in regional data centers/endpoints, ensuring that user data stays within the region it originated (e.g. EU users’ data stays in EU servers) to comply with GDPR and other local data laws. Telemetry data is encrypted in transit and at rest, and all processing happens in secure, compliant infrastructure. Essentially, your European customers’ form interactions won’t be whisked off to a U.S. server or vice versa – we process it locally to both reduce latency and honor privacy regulations.
• Aggregate Learning Only: When we mentioned “learning across the fleet,” we strictly mean learning from anonymous patterns, not sharing raw data. DeepOptimize might learn that “Long dropdown questions cause higher abandonment on mobile” by looking at thousands of form sessions in aggregate. It will use that insight to help all customers, but it will never share anything like “User John Doe on Form X didn’t finish question Y” with anyone. In fact, even our engineers can’t see that level of detail – it’s all aggregated or masked by the time it enters the AI engine.

In short, DeepOptimize acts like an insightful coach who is blind to personal details. It knows the plays and what works broadly, but not the names on the jerseys. You get the benefit of an AI that’s trained on vast amounts of usage data, while your users’ privacy remains fully intact.

The AI Engine – Five Modules Working in Harmony

At the heart of DeepOptimize is a robust AI engine with specialized components, each trained to tackle a different aspect of form optimization. Rather than one monolithic AI trying to do everything, we have five key modules (plus a shared knowledge base) that collaborate like an ensemble, each feeding into the next. Here’s a breakdown of these modules:

• 1. Form-LLM (AI Copywriter): “Words that Convert.” This module is powered by a large language model (LLM) specialized in form language. Its job is to analyze and improve the wording of your form – question text, descriptions, error messages, CTAs, etc. It looks for things like jargon, complexity, or ambiguity that might confuse users. For example, if your form asks “Please provide details of any prior domiciles”, the Form-LLM might suggest a clearer copy: “Have you lived at any other addresses in the past? If so, list them.” Clarity and brevity are key to keeping users engaged. This AI copywriter has been trained on tons of successful form phrasing and can even tailor suggestions to your audience tone (formal vs. casual) or reading level. Essentially, it’s your built-in UX writer, ready to rewrite a confusing question or add a helpful hint to nudge users along.
• 2. Flow-RL (Form Flow Optimizer): “The Journey Orchestrator.” Flow-RL is a reinforcement learning module that focuses on the sequence and logic of your form. Long or poorly ordered forms are conversion killers – research shows multi-step forms can have 86% higher completion rates than one long page . Flow-RL’s goal is to find the optimal way for users to navigate your questions. It experiments with things like: Should we break a single long form into two steps? Is the drop-off mainly happening after we ask for a phone number – if so, what if we asked for it later or optional? Flow-RL uses an approach similar to how AI might solve a maze: it tries different “paths” (form flows) on small percentages of traffic and learns which path leads to the goal (a completion) most often. Over time, it figures out an ideal flow. For example, it might learn to present easy or engaging questions first to hook users, and postpone more tedious parts until later when they’re invested. If certain logic (like a conditional question) is causing confusion, it may simplify that logic. All of this is done carefully – Flow-RL won’t just randomly reshuffle your form; it generates hypotheses based on observed pain points, and any major restructuring can be reviewed by you unless you fully trust Autopilot mode. But when enabled, it’s like having a smart CRO specialist constantly rearranging and tweaking the form’s steps for maximum completion, using real-time data as its guide.
• 3. Visual-CV (Layout & UX Assessor): “Design Eye.” This module uses computer vision and UI/UX heuristics to evaluate the visual layout of your form. Why computer vision? Because it literally “looks” at your form pages as a user would see them. It can detect things like: Is the font too small? Are form fields or buttons cut off on a certain screen size? Does the color contrast make a field hard to see? How is the form spaced – is it overwhelming, like a big wall of text, or nicely broken up? Visual-CV combines design best practices with models that predict readability and attention. For instance, it knows forms should ideally be single-column on mobile (to avoid zooming and confusion), so if your design accidentally created a two-column layout on small screens, it will flag that. It might spot that your “Submit” button is below the fold (users have to scroll to see it) and recommend making the page shorter or the button more prominent. Essentially, Visual-CV is an automated UX reviewer ensuring the form isn’t just logically sound, but also pleasant and easy on the eyes. It can even compute an “visual complexity score” for your form and correlate that with drop-offs – if high complexity correlates with lower completion, it will push suggestions to simplify the design.
• 4. Abandon-Predictor (Bounce Predictor): “Early Warning System.” This is a predictive model trained to detect when a user is likely to abandon the form before completing it. It’s like a crystal ball that watches a user’s behavior (in a session that’s in progress) and gauges their likelihood of dropping off. The model uses signals such as rapid cursor movement (or no movement at all), repeated errors on a field, long idle times, or scrolling up and down repeatedly without progress. For example, if a user has been stuck on one question for 2 minutes, or filled half the form then started scrolling back up – these are strong predictors they might quit. When the Abandon-Predictor fires an alert, DeepOptimize can take action in real time. One response is triggering an intervention: perhaps a subtle prompt like “Need help? Have a question about this form?” or a progress reminder (“You’re 70% done!”) to encourage them to continue. Or, if Autopilot is aggressive, it might even dynamically skip an optional section for that user (“We’ll just gather the essentials for now”). At the very least, the predictor’s insights are logged in the analytics – e.g., “Users who abandon often hesitate at field X” – which feeds into the Diagnose step we’ll discuss later. This module essentially ensures fewer users reach the frustration point of no return, by either rescuing the session or learning from it to prevent future abandonments.
• 5. Spam-Filter & Quality Checker: “The Bouncer and Editor.” Not all submitted responses are equal – some are outright spam or nonsense. This module evaluates the quality of responses, especially in free-text fields, and filters out junk. It uses natural language processing to flag responses that look like spam (e.g. a lead gen form that gets submissions like “Buy cheap sunglasses http://spam.link” – it will catch that). It also looks at things like answer length and relevance. For instance, if a survey question asks “What did you enjoy about our event?” and the answer is just “No”, the quality checker notes that as a low-quality response. This might prompt a suggestion to tweak the question wording (via Form-LLM) or to enable a clarification prompt like Typeform’s AI follow-up approach (Typeform’s Interaction AI will ask for detail if someone answers too briefly ). The Spam-Filter ensures you don’t have to wade through garbage data; combined with Buildform’s existing fraud prevention, it can auto-block known bad actors or gibberish submissions. For legitimate users who give minimal answers, it might even integrate with Flow-RL to insert a follow-up question: “You answered ‘No’. Could you tell us a bit more about why you felt that way?” – thereby boosting data richness. All of this helps maintain a high signal-to-noise ratio in your form results, saving your team time and improving decision-making based on the data.

These five modules collaborate behind the scenes. They share a common feature store and feedback loop so they aren’t working with siloed information. For example, if the Spam-Filter flags a lot of nonsense answers on a particular question, that info feeds back to Form-LLM and Flow-RL: the Form-LLM might reconsider the phrasing of that question (perhaps it’s inviting misinterpretation), and Flow-RL might experiment with changing its position or making it optional. Similarly, if Visual-CV finds a layout problem causing confusion on mobile, that insight might feed into the Abandon-Predictor to update its model of what signals indicate frustration. All insights funnel into a central knowledge base so the AI as a whole learns which changes led to improvements or not. This virtuous cycle means the longer DeepOptimize runs, the smarter it gets at recommending effective optimizations.

The Optimization Pipeline: From Observation to Improvement

Understanding the modules is great, but you might wonder: how do these actually translate into changes on my form? DeepOptimize follows a 6-step optimization pipeline that repeats continuously. It’s an ongoing loop of observing, diagnosing, and improving. Here’s how the process works, step by step:

1. Observe – Data Collection in Real Time: As soon as your form is live, DeepOptimize observes user behavior through the telemetry we described. It’s passively watching clicks, scrolls, drop-offs, and submissions. At this stage, it’s just collecting signals. Think of it as building the “analytics story” of your form: conversion rate, funnel drop-off points, average time on each field, etc., all in real time. This is the foundation – a comprehensive dataset of what’s happening.
2. Diagnose – Identify Patterns & Pain Points: Next, the AI engine diagnoses issues by analyzing the telemetry data. This is where the patterns pop out. The system might identify that “Question 7 has a 60% abandonment rate” or “On mobile devices, only 20% of users who reach page 2 complete the form, versus 80% on desktop.” It looks for statistically significant signals of friction: e.g., a spike in error corrections on one field, or a time-to-complete that’s much longer than average at a certain step. The Diagnose phase is essentially automatic root-cause analysis. DeepOptimize will generate findings like a human analyst would: “Users often drop off after seeing the pricing question,” or “The form loads slowly on Safari browsers leading to early exits.” These findings get added to an Insights Feed (more on that in the UX section) so you can see what the AI believes are the key issues. Diagnose is about pinpointing where and why users struggle, using the combined wisdom of all modules. It might correlate external factors too, like “traffic from campaign X converts 15% better than campaign Y” if relevant to form flow.
3. Hypothesize – Generate Optimization Ideas: Once a problem area is identified, DeepOptimize doesn’t stop at admiring the problem – it immediately brainstorms solutions. In the Hypothesize step, the AI modules (especially Form-LLM and Flow-RL) cook up potential fixes for the diagnosed issues. For a given drop-off point, it might generate multiple hypotheses. For example: “Maybe question 7 is too wordy – suggest a simpler rewording,” and “Maybe question 7 would perform better if moved to later in the form,” and “Maybe make question 7 optional.” For a slow-loading form, a hypothesis might be “Optimize image sizes on the form” or “Split the form into two pages so first page loads faster.” Each hypothesis is essentially a proposed experiment. The key here is creativity backed by data – the AI uses what it’s learned (including that multi-step insight or field count stats) to propose changes likely to improve things. It may even recall that “in similar forms in the past, making the email field optional until later improved completion by X%” and use that as a hypothesis for you. You, as the human in the loop, can also feed in ideas – and the AI will integrate those into its hypothesis list as well via the chat interface. At the end of this phase, we have a set of candidate optimizations to try.
4. Rank – Prioritize the Best Ideas: If there are multiple possible fixes, which one do we try first? The Rank step uses a combination of AI prediction and your preferences to sort the hypotheses by likely impact. DeepOptimize’s scoring algorithm considers factors like: expected conversion lift (the AI can estimate, say, “making Q7 optional might boost completion by ~15% based on similar patterns ”), the confidence level of the prediction, the ease of implementation (e.g. changing copy is easier than overhauling layout), and any constraints you’ve set (perhaps you don’t want to change your branding or a specific question due to internal reasons). It then produces a ranked list: for example, 1) Rewrite question 7 text (high impact, easy), 2) Move question 7 to end (high impact, moderate disruption), 3) Make question 7 optional (medium impact, easy) and so on. This ensures DeepOptimize tackles the highest ROI changes first. If you’re using Autopilot, it will use this ranking to decide which experiment to deploy. If you’re in manual mode, this ranking is a recommendation for you to consider. Either way, it makes the optimization process efficient – no guesswork about what to try next.
5. Deploy – Test and Implement Changes: Now comes action. In the Deploy step, DeepOptimize actually applies the top-ranked optimization (or multiple, if they don’t conflict) in a controlled manner. If you have Autopilot turned on, the AI might automatically roll out the change to a subset of your form’s traffic as an A/B test or phased rollout. For example, 50% of new visitors see the form with Question 7 reworded, while 50% see the original – and we compare the outcomes. If you’re not on full autopilot, DeepOptimize will present the suggestion in the Buildform dashboard for your approval. With one click, you could accept the change (Buildform will update the form’s content or settings accordingly). Deploy is all about taking the hypothesis live and doing it in a way that we can measure its effect. Under the hood, Buildform’s infrastructure (TypeScript SDK, etc.) allows dynamic changes, so things like reordering questions or tweaking text can be done without you manually rebuilding the form. Some changes, like layout tweaks, might involve shipping a slight style update. Others, like different question sequences, might use Buildform’s logic engine. Importantly, every deployment is logged as an “experiment” – so we know exactly what change was made, when, and who saw it.
6. Measure – Evaluate Results & Learn: After or during deployment, DeepOptimize rigorously measures the impact. Did the change improve the target metric (e.g. completion rate, time to complete, quality of answer)? The system will compare the performance of the variant vs. original. For instance, maybe the reworded Question 7 variant saw completion go from 40% to  Fifty forms that were changed had a 44% completion – a solid lift. Or perhaps no significant change, or even a drop (occasionally a hypothesis might backfire – e.g. making Q7 optional could reduce answer quality drastically). DeepOptimize uses statistically sound methods (it can even do a sequential test or Bayesian bandit approach) to determine if a change is a win, loss, or inconclusive. This result feeds back into the knowledge base. If it’s a win, DeepOptimize can roll out the change to all users (or prompt you to do so) – effectively locking in the gain. If it’s a loss or negligible, it will roll back and note that this hypothesis wasn’t effective, learning from that outcome. The Measure step closes the loop by updating the AI’s understanding: it might adjust how it ranks future similar suggestions, or flag that maybe the issue wasn’t solved and needs new hypotheses. And then… the cycle starts over. DeepOptimize moves on to the next issue or continues fine-tuning the same one with a new idea, continuously striving for the best possible performance.

This Observe → Diagnose → Hypothesize → Rank → Deploy → Measure loop is continuous. Over a form’s lifetime, you might see many small tweaks and improvements accumulating. One week DeepOptimize might focus on boosting completion rate; another week, after that’s pretty optimized, it might focus on improving answer length for a free-response question (thus targeting response quality). It’s an endless virtuous cycle, much like an athlete training: always watching the tape, finding areas to improve, trying new techniques, measuring performance, and iterating. Except here, the coach and the athlete are rolled into one AI-driven system constantly working to make your form the highest-converting, smoothest experience it can be.

The Buildform Dashboard: How You’ll Use DeepOptimize

All this high-tech wizardry remains user-friendly and under your control through the Buildform interface. DeepOptimize is woven into the Buildform dashboard in a way that caters to both non-technical users who just want results and power users who love to dig into data. Here’s what you can expect when you enable DeepOptimize on your forms:

Insights Feed – Real-Time AI Observations

When DeepOptimize is active, your form gets an Insights Feed – a running timeline of findings and suggestions the AI has generated. Picture a news feed, but instead of friend updates, it’s updates about your form’s performance. For example, an insight might say:

💡 Mobile users are 30% less likely to complete the form than desktop users – possibly due to layout. (Detected by Visual-CV), or

💡 5 users in a row dropped off at Question 10 after spending >1 min – consider simplifying this question. (Diagnosis)”.

Each insight is written in plain English (with technical details available if you click in). Think of these as data-driven tips. Some insights might be purely informational (e.g. identifying a trend), while others will come with a suggested action (generated in the Hypothesize phase). You can scroll through and immediately see what’s going on with your form. It’s like having an expert whispering in your ear: “Psst, this field might be scaring people away… let’s tweak it.” For busy folks, this feed surfaces the critical things you need to know without having to pour over charts yourself.

Experiment Dashboard – Control and Results

All the optimizations DeepOptimize tries are tracked as Experiments. The Experiment Dashboard is where you can see and manage these tests. Each experiment card will show: what change was made, what metric it aimed to improve, how long it’s been running, and the current results (e.g. “Variant A is converting 12% better than control, p-value 0.03” or “No clear winner yet”). You’ll have the ability to approve, roll back, or tweak experiments. For example, if DeepOptimize suggests shortening a headline and you approve it, you’ll see that A/B test running. If it quickly shows a positive result, you might choose to end the test early and apply to 100%. Or if you’re curious, you let it run longer for confidence. If an experiment isn’t performing well, you can stop it. The dashboard thus gives you full visibility into what the AI is doing on your form’s behalf – no black boxes. It’s also an archive of what changes have been made, which is great for learning and reporting. You can even tag experiments with your own notes, so you remember context like “We launched this change before the holiday campaign.” For those who prefer more manual control, you could use DeepOptimize purely in a suggest-and-report mode: it finds issues, you manually create experiments from those suggestions in this dashboard. It’s flexible.

Autopilot Mode – Optimization on Cruise Control

One of DeepOptimize’s hallmark features is Autopilot. In the Buildform UI, there will be an Autopilot tab or toggle for your form where you can enable the AI to act on its own recommendations automatically. This is the set-it-and-forget-it mode: you trust DeepOptimize to not only find opportunities but also to execute changes and steer experiments without needing your constant input. Autopilot is especially useful for teams that don’t have a dedicated person to babysit form optimizations – essentially, most teams! You can configure Autopilot with guardrails: for instance, “Only make changes that are low-risk and reversible” or “Don’t modify anything involving our legal disclaimer text.” You can also set thresholds like “If an experiment is confidently +5% or more, you can roll it out; if it’s worse, auto-revert it.” When Autopilot is on, you’ll get notifications of what it’s doing – e.g., “DeepOptimize adjusted the wording of Q3 for 50% of users. Monitoring results now.” – but you won’t have to click approve every time. This is truly the AI auto-pilot experience: your form is self-optimizing. You can relax and watch conversion metrics rise, or focus on other tasks while DeepOptimize handles the nitty-gritty. Of course, you can pause or override Autopilot at any time. And even in Autopilot, everything is logged in the Experiment Dashboard so you can audit later. The confidence we have in Autopilot comes from lots of testing – by the time we suggest that mode, we’re ensuring the AI’s choices are sound. It’s somewhat analogous to self-driving car modes: some prefer to keep a hand on the wheel, others let the car do the driving on straight highways. We provide both options. Many early users love Autopilot for mundane forms (like an internal feedback form they don’t want to manage), while they might manually oversee critical high-impact forms. The choice is yours.

Natural Language Chat Interface – Converse with Your Form’s AI

One of the most exciting and user-friendly aspects is the DeepOptimize Chat. This is a conversational UI (accessible in the Buildform dashboard) where you can literally chat with an AI assistant that knows everything about your form’s performance. Not only is this handy, it’s also a bridge between technical data and human understanding. For example, you could type: “Why are users dropping off on page 2?” and the AI might respond: “It looks like page 2 contains a long text field that 60% of users skip. Many spend over 90 seconds on that page and still abandon. I suspect the question might be too open-ended or daunting. Shall I suggest ways to simplify it?”. You can follow up with “Any difference between mobile and desktop?” and it will provide a breakdown. Essentially, the chat turns your form analytics and DeepOptimize’s brain into an interactive analyst you can query. You don’t have to hunt for the right chart or report; just ask in plain English (or any supported language, likely) and get insights.

But it’s not just Q&A. You can also issue commands or brainstorm: “How can I improve my conversion rate?” and the AI will summarize the top issues and potential fixes (drawing from the Hypothesize engine). Or “Implement the best suggestion for mobile users” – and if Autopilot is allowed, it might go ahead and do just that, or guide you through doing it. It’s like having a conversation with your form’s personal doctor and mechanic at once. This lowers the barrier for non-technical users to harness powerful optimization techniques. A marketing manager might never write a SQL query to analyze form drop-offs, but they can certainly ask, “Which field takes users the longest to fill out, and is it affecting completion?” and get an instant answer. We believe this natural language interface makes DeepOptimize’s advanced capabilities accessible to anyone, regardless of technical skill. It also makes the experience a bit magical – you’re essentially talking to an AI that’s watching your users and improving your conversion, in real time.

Integrating with Your Workflow

DeepOptimize is part of Buildform, so it also plays nicely with other features. For example, if you use Buildform’s Partial Submission Analytics, DeepOptimize leverages that data (and likely you’ll see partial submissions info right in the Insights). If you’re using the AI Form Builder to create the form, DeepOptimize picks up from there to continuously refine what the builder created. The transition from form creation to optimization is seamless – it’s all in one platform. Also, expect notifications – if a big insight or win happens, you can get an email or Slack ping: “DeepOptimize increased Form X’s completion rate from 50% to 57% this week after trying 3 improvements 🎉.” This keeps your team in the loop on wins. And of course, you maintain the ability to manually edit your form at any time; DeepOptimize will adapt to any changes you make and continue its work (and will treat your changes as new baseline to further experiment from, if needed).

In summary, using DeepOptimize will feel less like wrestling with a complex analytics tool and more like collaborating with a smart teammate. You have the constant feed of insights, a clear view of experiments, the option to hand over the reins to Autopilot, and an AI assistant at your beck and call to discuss your form’s performance. Our goal is that Buildform + DeepOptimize not only gives you better numbers, but also makes the process of optimization intuitive and even enjoyable.

Real-World Impact: Show Me the Numbers (ROI Examples)

All the fancy AI talk is worth nothing if it doesn’t move the needle for your business or project. So let’s talk about the tangible impact DeepOptimize can have with a few realistic scenarios.

1. Lifting Conversion = More Revenue: Imagine you’re a SaaS company using a Buildform lead form on your website to gather trial signups. You currently convert 5% of website visitors into a trial signup, which brings in about $30M in annual revenue once those leads convert to paying customers. Now, with DeepOptimize’s help, suppose over a couple of months it incrementally improves that conversion rate to 5.5% (that’s a 10% lift, which is very achievable – recall many forms can improve by ~10% just by A/B testing ). That seemingly small increase – 0.5 percentage points – actually translates to 10% more customers in your funnel. Suddenly, your potential annual revenue goes from $30M to about $33M, a $3 million boost, simply because more people are getting through the form and into your product. That’s like turning a few knobs on a form and adding millions in value. Traditionally, to get that kind of lift, you might invest in hiring conversion specialists or pumping more money into ads to get more traffic. DeepOptimize aims to give you that uplift practically for free by making better use of the traffic you already have.

2. Reducing Abandonment = Saving Marketing Spend: Perhaps you run an e-commerce site and use Buildform for a multi-step checkout. You notice many users drop off at the shipping details step. DeepOptimize figures out that the step asking for a phone number is scaring people (some users are privacy-conscious or don’t want to give a phone number for a simple purchase). The AI tests making phone number optional and adding a note “(optional, for delivery updates only)”. Completion rate of checkout goes up from, say, 60% to 66%. That’s a relative 10% reduction in abandonment (i.e., 6 more people out of 100 now finish checkout). If your average order value is $100, that’s an extra $600 for every 100 visitors. Scale that up to thousands of visitors and you’re recovering revenue that was previously slipping through the cracks. In effect, DeepOptimize just made your marketing spend more efficient – you don’t need to pay to reacquire those lost customers, because they’re not lost anymore.

3. Better Quality Data = Better Decisions: Consider a company running an employee feedback survey. Often, these surveys suffer from incomplete or low-effort answers (“N/A”, “everything’s fine” etc., which aren’t very actionable). Using DeepOptimize’s response quality enhancements (like clarifying questions or prompting longer answers when safe to do so), suppose the survey now gets 30% more detailed responses to open-ended questions. This means instead of, say, 100 one-word answers, you get 100 meaningful paragraphs of feedback. The ROI here is qualitative but huge: your HR team can actually implement changes based on rich feedback, potentially improving employee satisfaction and retention. Hard to put a dollar value, but if even one good idea from that improved feedback prevents a few employees from leaving, you’ve saved tens of thousands in re-hiring costs. DeepOptimize essentially acted as a facilitator, coaxing more value out of each response.

4. Spam Reduction = Efficiency Gains: Many businesses suffer from spam or bot submissions, especially if forms are public. Let’s say without DeepOptimize you get 100 form submissions a day, but 15 of them are junk (spam, trolls, or just unusable). Your team has to manually sift these out, which is a waste of time. After deploying DeepOptimize’s Spam-Filter and quality checks, spam is automatically blocked or filtered so that maybe only 1-2 junk submissions get through per day. Now your team deals with almost only real, quality leads or responses. If handling each form entry (qualifying a lead, responding to an inquiry) takes 5 minutes, you just saved about 65-70 minutes of labor per day by eliminating 13-14 spam entries. That’s over 20 hours per month of time saved – which your sales team can use to follow up with real customers instead. In monetary terms, if those 20 hours would cost, say, $50/hour in employee time, that’s $1,000/month saved, or $12,000/year in efficiency gains. Plus, the morale boost of not dealing with spam.

5. Faster Iteration = Cost Savings in Dev/Design: Let’s not forget the often hidden cost of iteration. Traditionally, if you wanted to improve a form, you’d involve a designer, maybe a copywriter, then a developer to implement changes, then QA to test, etc., for each round of changes. That’s a lot of human hours. DeepOptimize automating many of these suggestions and even implementations can cut down on those cycles. If an AI copy tweak saves you needing a content writer for a day, or an automated layout fix means your front-end dev doesn’t have to manually trial 3 different versions, those are real savings. It also means you deploy improvements faster, potentially capturing revenue sooner. Essentially, DeepOptimize democratizes optimization – you don’t need to be a big company with a full optimization team to reap these benefits. Even a solo entrepreneur or a small startup can see significant ROI because the AI lowers the cost and time barrier.

To sum up, DeepOptimize drives ROI in multiple dimensions: increased top-line (more conversions, leads, sales), decreased waste (abandonment, spam), improved decision-making (better data), and reduced costs of optimization itself. We believe these improvements aren’t just minor tweaks – they can change the trajectory of a business. After all, if your form conversion doubles (not uncommon when going from a poorly optimized form to a well-optimized one), that could potentially double your business if the form is your main lead funnel. And even smaller gains compound over time. Ten percent more leads every month, quarter over quarter, can transform your growth curve.

Under the Hood: Tech Stack Powering DeepOptimize

How does Buildform make all this happen reliably at scale? The tech stack behind DeepOptimize brings together the best of modern data engineering, scalable infrastructure, and machine learning ops. Here are the key components that work behind the scenes to enable this always-on AI layer:

• TypeScript SDK (Buildform Client) – At the frontlines, our TypeScript SDK is embedded in the form page. It efficiently captures user events and handles on-device processing (like PII stripping) before sending data out. It’s optimized for performance so it won’t slow down the form; events are batched and sent asynchronously. This SDK is the bridge between the user’s browser and our cloud, designed to be robust across different browsers and devices.
• Apache Kafka – All those telemetry events (clicks, keystrokes, etc.) stream into our backend through Kafka, a distributed event streaming platform. Kafka acts as the high-throughput messaging backbone – it can handle millions of events per second, ensuring no data is lost and that our AI modules get the data in near real-time. By queueing events, it also decouples the ingestion from processing, so we can scale each independently. In short, Kafka lets DeepOptimize digest a firehose of data reliably and in order.
• ClickHouse – We use ClickHouse, a blazingly fast open-source columnar database for analytical queries . All the telemetry and experiment data lands in ClickHouse clusters, which allows us to run complex analytics (funnels, segment analysis) incredibly quickly. ClickHouse is optimized for exactly these kinds of use cases (event data analysis) – it can scan billions of rows in milliseconds. This powers the quick insights and breakdowns you see in the dashboard or chat responses. When DeepOptimize is diagnosing, it often performs queries like “count how many users dropped at step X” or “average time on field Y by device type” – ClickHouse makes that happen almost instantaneously on the backend.
• Redis (JSON store) – We utilize Redis (with the Redis-JSON module) as a fast, in-memory data store for session data and the shared feature store. The various AI modules share features (like “user spent X seconds on Q7” or “last action was backspace”) via this store in real time. Redis gives sub-millisecond read/writes, which means our models can retrieve the latest user state or experiment config without delay. It’s also used for caching recent results, storing user-specific flags (like if a user is in variant A or B of a test), all in a structured JSON format. This helps coordinate between the front-end and backend – e.g., if Autopilot decides this user should see variant text, Redis stores that and the SDK will fetch it when rendering.
• DuckDB – While ClickHouse handles heavy analytics, we also leverage DuckDB for quick on-the-fly queries, especially in our Python-based AI environment. DuckDB is an embedded analytics database (often called “the SQLite of analytics”). Suppose an AI module wants to do a more complex computation on a subset of data (like training a small model on the last week of data for a specific form) – it can pull those events into an in-memory DuckDB instance and crunch it without round trips to external databases. It’s great for ad-hoc analysis and transforms in our pipeline, and it plays nicely within our machine learning code.
• BentoML – All our machine learning models (the five modules like Form-LLM, Abandon-Predictor etc.) are packaged and served using BentoML. BentoML is an ML model serving framework that makes it easy to turn trained models into scalable microservices. Each module is essentially a service (or a set of them) containerized with BentoML, exposing APIs that DeepOptimize’s orchestrator can call. For instance, there’s a service for the Form-LLM that given a prompt (like a form question) returns a rewritten suggestion. BentoML ensures these models load quickly, have all their dependencies, and can autoscale. It’s critical for when we roll out new versions of models or need to serve many requests concurrently.
• Kubernetes + KEDA – Our infrastructure runs on Kubernetes, and we use KEDA (Kubernetes Event-Driven Autoscaling) to dynamically scale the different components of DeepOptimize based on demand. KEDA allows us to scale, say, the Abandon-Predictor service based on the length of the Kafka queue or the number of incoming events. If there’s a sudden surge of traffic (maybe one of our customers has a viral form), KEDA will detect the increased event rate and automatically spawn more pods for the necessary services to handle it, then scale down when the load subsides . This means DeepOptimize remains real-time and responsive even under heavy load, without wasting resources during quiet periods. Essentially, the whole system is elastic.
• Prefect (Workflow Orchestration) – Prefect is our choice for orchestrating scheduled jobs and data pipelines within DeepOptimize. While a lot of optimization is real-time or streaming, some tasks are better suited as batch jobs – for example, a nightly retraining of a model on fresh data, or a job to compute weekly summary reports. Prefect allows us to define these workflows with dependencies and schedules, and it handles their execution reliably (with retries, monitoring, etc.). We use it for things like recalculating the global “learning across the fleet” insights with differential privacy, or syncing data to long-term storage. It’s basically our cron on steroids that keeps the gears turning on periodic tasks.
• MLflow (Experiment Tracking & Model Management) – DeepOptimize’s AI modules are constantly being trained, evaluated, and updated (by us, the Buildform team). We use MLflow to track machine learning experiments – every time we train a new model (say a new version of the Abandon-Predictor with more data or a tweak in architecture), MLflow logs the parameters, metrics, and resulting model artifacts. It provides a model registry, so we know which model version is in production versus staging, and we can easily roll back if needed. This is more on the development side, but it ensures that the AI you rely on is well-versioned and evaluated. If we find a new improvement (like a new LLM that’s better at copy suggestions), we train it, log it in MLflow, and when it’s deployed via BentoML, we know exactly what’s running. In short, MLflow helps our engineers continuously improve DeepOptimize in a safe, trackable way.

All these technologies work together to make DeepOptimize fast, scalable, and reliable. What’s remarkable is that just a few years ago, this kind of real-time ML-driven system would have been very hard to build – but today, tools like ClickHouse, Kafka, and KEDA let us do it relatively efficiently. The bottom line for you as a user is that DeepOptimize can handle huge amounts of data and complex AI logic without breaking a sweat. Whether your form gets 100 submissions a month or 100,000 a day, the system adapts and keeps optimizing. And because we stand on the shoulders of proven open-source and enterprise-grade tech, you can trust that the performance insights and improvements are derived from a solid engineering foundation.

Vision & Roadmap: What’s Next for DeepOptimize

DeepOptimize is launching soon, but we’re already dreaming up the next wave of capabilities to make it even more powerful throughout 2025 and beyond. Our vision is to make every aspect of form optimization intelligent and proactive. Here’s a sneak peek at some features and ideas on our roadmap:

• Layout Auto-Tweaks (2025 Q1) – Today, Visual-CV flags layout issues internaly (rolling out for all users soon) and suggests improvements; in the future, we want it to fix them on the fly. We’re working on auto-tweak features for form design. For example, the AI could automatically adjust font sizes, or contrast if it detects the current style is hurting readability or mobile usability. If a question is too long, it might automatically rewrite to be multiple pages (with your approval or via Autopilot). Essentially, the form will subtly reformat itself for optimal UX. These will be minor, almost unnoticeable changes (no dramatic redesign without your input), but lots of tiny design improvements can add up to a smoother experience. Our endgame: you design the form initially, and then the AI keeps polishing the UX details continuously – a bit like how Tesla sends software updates to improve your car after you bought it.
• Beyond the Submit Button – Optimizing Non-Submission Goals – Right now, DeepOptimize’s primary “goal” for a form is a successful submission. But forms can have other valuable user actions: maybe you want people to reach a certain section, or click a link (like a terms of service link), or maybe filling the form is optional but you want them to at least interact with an embedded video. We’re planning to let you define custom micro-goals or alternative goals and have DeepOptimize optimize for those as well. For example, if a large portion of users never click “Submit” but do interact up to page 3, you might consider that a partial success. The AI could then optimize to maximize people getting to page 3 (while still ultimately nudging towards full completion). Or imagine an onboarding flow where even if users don’t complete, you want them to perform a secondary action like signing up for a newsletter – DeepOptimize could detect an abandon and prompt that as a fallback goal. We’ll also tie into external goals: say a user submits a form and then also needs to confirm email or make a purchase; DeepOptimize could loop in conversion data from your other systems to see the full picture. The idea is to broaden what “success” means for a form and get the AI to optimize the whole journey, not just the submit rate.
• Embed Mode Enhancements – Buildform forms can be embedded in your website or app, not just as standalone pages. In the coming updates, DeepOptimize will become embed-aware. This means it will consider the context around the form when it’s embedded. For example, if your form is sitting in a blog page, the AI might track how the content above/below the form influences engagement (are users actually seeing the form? do they scroll past it?). We plan to allow optimization of the embed trigger as well – e.g. if your form opens as a popup or appears after a delay, DeepOptimize can experiment with the timing or call-to-action text that leads users into the form. Essentially, when your form lives inside another page, DeepOptimize will treat the integration as part of the funnel. This could involve, say, suggesting changes to the button that launches the form (“Get Started” vs “Open Form” might yield different click rates). We’ll ensure that all this is easy to set up by simply adding some attributes to your embed code. The goal is that no matter where your form resides, DeepOptimize can maximize its engagement.
• Federated Learning & On-Device Models – To push privacy and performance further, we’re exploring federated learning for some aspects of DeepOptimize. In a federated setup, the model training can happen on the user’s device (in a sandboxed way) and only send back summary updates to improve the global model. This could allow, for instance, personalization models (maybe one day DeepOptimize could subtly personalize form wording for different user segments) that learn from user data without that raw data ever leaving the device. It’s a complex frontier, but one we have our eyes on. Even before full federated learning, we’re considering shipping slim versions of certain models directly in the SDK (for example, a tiny JavaScript ML model that can predict abandonment within the browser and trigger a prompt instantly, without even a round trip). This would make some optimizations faster (truly real-time on the client side) and even more privacy-preserving.
• Deeper Integration with Buildform AI Builder – Buildform already has an AI Form Builder (and upcoming AI Logic Builder as teased). Over time, DeepOptimize will loop back insights to improve the creation phase. Imagine starting a new form and Buildform’s AI says, “Forms like yours (webinar sign-ups) typically see 20% higher conversion if they have 5 or fewer fields and a progress bar. Let’s start with that best practice.” This kind of knowledge transfer from optimization to creation will mean new forms start off on the right foot. As more forms are optimized, the AI builder will get smarter about recommending the optimal structure from the get-go. It’s the virtuous cycle: build with AI, optimize with AI, then build even better next time with the lessons learned.
• Analytics Deep-Dive and Reporting – We know larger organizations will want to extract reports and maybe plug DeepOptimize data into their own BI tools. On the roadmap is the ability to export detailed analytics, or even connect a BI dashboard (via an API or integration) to the data that DeepOptimize is collecting (all with proper aggregation and privacy). Additionally, we plan to add more visualizations in the Buildform UI itself: heatmaps of form drop-off, session replays for outlier cases (with sensitive data masked), and trend charts over time to show how your form’s metrics are improving post-DeepOptimize. The vision is that Buildform becomes not just where you create forms, but where you continuously monitor and improve them with full visibility.
• Community & Template Learning – With user permission, we might enable an opt-in “community learning” mode where anonymized learnings from certain industries or template types are shared as insights with others. For example, “We noticed that non-profit donation forms with a story paragraph at the top have 15% better completion – consider adding a short story to your form.” This would be leveraging the “fleet learning” concept in a more content-oriented way. It’s kind of like crowdsourcing best practices (again, without sharing any private data or specific designs). Templates could come with recommended optimization settings out of the box as well.

Our ultimate vision is ambitious: zero-effort optimal forms. One day, you might simply specify your goal (e.g. “collect 100 high-quality leads per week”) and the AI handles everything from form creation to constant optimization to hit that goal. DeepOptimize is a big step in that direction – making forms self-improving entities.

For 2025 specifically, our focus is on fine-tuning the core algorithms with feedback from real use cases, and rolling out these new features gradually. We’ll be listening closely to our beta users: What insights are most useful? Where do you want more control or more magic? Each feature in the roadmap will be shaped by that feedback to ensure it truly makes form optimization easier and better for you.

DeepOptimize vs. The Rest: How We Compare

You might be wondering how DeepOptimize stacks up against other form solutions or optimization tools out there. Let’s candidly compare with a few names you might know – Typeform, Jotform, and Tally – and highlight what makes DeepOptimize different. All of these are great form builders in their own right, but DeepOptimize gives Buildform an edge in AI-driven optimization, logic tuning, and privacy that’s hard to find elsewhere.

• Typeform – Typeform is famous for its one-question-at-a-time conversational forms that often yield higher engagement than traditional forms. They’ve recently launched an AI suite focusing on form creation and response analysis (e.g. AI that helps build forms, auto-follow-up on short answers, and analyze results) . However, when it comes to optimization, Typeform’s approach still largely relies on you designing a great form up front. Their AI might prompt a user for a better answer (“Can you tell us more?”) which is cool for response quality, but Typeform isn’t automatically rearranging your questions or suggesting form changes in real time to boost conversions. DeepOptimize, on the other hand, is deeply proactive. It doesn’t assume your form is perfect once built – it treats it as a living thing to improve continuously. Also, Typeform’s strength (the conversational interface) can be a limitation (though buildform’s default is also this 😅)– not every use case fits that style, and even in Typeform you can get drop-offs that are hard to diagnose (e.g. which specific question lost them?). With Buildform + DeepOptimize, you have flexibility of form design and an AI watching conversion analytically. Another big difference is privacy: Typeform of course cares about data security, but features like differential privacy and on-device PII stripping are not publicly touted by them. Buildform is taking privacy to the next level with DeepOptimize’s design (e.g., anonymized cross-form learning), which is crucial for enterprise clients or any sensitive use case.
• Jotform – Jotform is a powerhouse with a vast feature set and recently introduced Jotform AI including “AI Agents” that can turn forms into chatbots or even fill out forms on behalf of users . That’s an interesting take: basically, Jotform can let an AI guide the user through the form (like a conversation) or answer user questions while filling the form. While this can improve completion by providing help, it’s a different philosophy. It’s more of a chatbot assistant overlay for forms. DeepOptimize’s approach is to improve the form itself and the user experience structurally, rather than adding an AI chatbot to talk to the user (though we do have the chat for the creator, not the end-user). Also, Jotform’s AI doesn’t seem to focus on analyzing aggregate behavior or doing automatic experiments – it’s more about enhancing the form-filling experience in the moment. In terms of logic tuning, Jotform (and others) require you to set up conditional logic manually. Buildform already simplified logic, and with our upcoming AI Logic Builder and Flow-RL, we aim to outpace Jotform in making form logic smart and self-optimizing. On privacy, Jotform is a mature product with HIPAA compliance etc., but DeepOptimize’s extra steps like differential privacy are a differentiator for clients who need that mathematical guarantee of privacy. In short: Jotform offers AI as an add-on to help users fill forms; Buildform offers AI to help you optimize forms. These approaches can complement, but if you want the latter, DeepOptimize is in a league of its own.
• Tally (and others like Google Forms, etc.) – Tally is a newer player that prides itself on a super easy form creation (Notion-like interface) and generous free features. Tally does have some basic AI for form building (like interpreting a prompt to create a form), but it doesn’t provide advanced analytics or AI optimizations after that point. Simplicity is both Tally’s strength and its limitation. If you just want a quick form and don’t care about optimizing it, Tally is fine. But for anyone who values conversion, Tally lacks the kind of tooling DeepOptimize provides. There’s no always-on AI watching your form or suggesting improvements in Tally; you’d have to manually use other tools (like Google Analytics, etc.) to even see how your form performs. Buildform with DeepOptimize is targeted at users who want that next level of intelligence without sacrificing ease of use. We’ve made the conscious choice to integrate AI deeply rather than just for form creation. Other form builders like Typeform, Formstack, Formsort, Wufoo, etc., each have their niche, but none (to our knowledge) offer a holistic AI optimization agent like DeepOptimize. Some might have A/B testing features or analytics, but those still rely on you to define the tests and interpret the data. DeepOptimize automates and interprets much of that for you.
• Privacy & Data Use Differences – A major differentiator worth re-emphasizing: learning across forms with privacy. Most form platforms treat each form’s data in isolation due to privacy – and rightly so, they don’t mix your data with others. What Buildform is doing with DeepOptimize is pioneering a way to get the benefits of learning from aggregate data while still not compromising privacy. Techniques like differential privacy and only sharing model weights (not raw data) mean we can spot industry-wide best practices and offer them to everyone. Competitors haven’t really touched this domain yet. If they share any data across users, it’s usually only within templates or so, not dynamic optimization knowledge. We believe this will be a key advantage: as our user base grows, every Buildform customer’s forms get smarter at an accelerated rate. It’s almost analogous to how Tesla gathers driving data from all cars to improve the autopilot for everyone – but we do it in a privacy-safe way. If privacy is crucial (think medical forms, financial data collection), DeepOptimize’s approach might be the only one that satisfies strict requirements while still providing AI-driven improvements.
• Customization and Control – One might compare DeepOptimize to using standalone tools like Google Optimize (now sunsetting) or Optimizely for A/B tests, Hotjar for behavior analytics, etc., on top of a form. That stack can indeed achieve some similar outcomes (you could manually set up experiments, watch recordings, tweak text). But Buildform’s proposition is that it’s all integrated and largely automated. No need to juggle multiple tools or be an analytics expert. This is a different philosophy than most traditional form builders that assume if you want heavy optimization, you’ll bring your own toolkit. We built it in, because we think optimization is not a luxury for the few, but a necessity for all.

In summary, DeepOptimize sets Buildform apart in a crowded form builder market by offering an intelligent, self-improving form experience. Typeform and Jotform have added AI in creative ways, but largely around form creation and user interaction. Only Buildform (with DeepOptimize) is tackling the full lifecycle – from creation to continuous optimization – using AI at every step. And we’re doing it while raising the bar on privacy and giving you fine-grained control. If you’re a business that relies on forms for anything important (leads, sales, feedback), this is a game changer. Instead of static forms that you hope work well, you get forms that actively work to be better, day by day.

Closing Thoughts: Form Optimization, Reinvented

We started with the observation that most people don’t know why their forms convert or stall. With DeepOptimize, that’s about to change. It transforms form optimization from a dark art into a data-driven, AI-powered process – one that runs continuously, learns from every visitor, and shares wisdom across the entire Buildform community without ever sharing the raw data. It’s like having a personal conversion rate expert, UX designer, copywriter, and data scientist all rolled into one, on call 24/7, dedicated to your forms.

For businesses, this means higher conversions and better data with less effort. For users filling out forms, it means smoother, more intuitive experiences that respect their time (and privacy!). If you’re a technical reader, we hope the deep dive into our architecture shows the rigor and innovation under the hood. If you’re a non-technical reader, the bottom line is simple: smarter forms = better results.

DeepOptimize is not just a feature; it’s a philosophy of continuous improvement. We’re incredibly excited to roll it out and help you unlock the untapped potential in your forms. Early tests have shown double-digit percentage gains in completion rates in just weeks, and we’re just scratching the surface. As we integrate more feedback and roll out new capabilities, we envision a future where creating a form is not the end of your work – it’s just the beginning of a collaborative journey between you and an AI that wants your form to succeed as much as you do.

Stay tuned for the launch of DeepOptimize inside Buildform (coming very soon). We’ll be sharing more updates on our blog and inviting users to beta test the Autopilot mode. We can’t wait to see how you use it, what it teaches us, and the amazing conversion wins you achieve. Here’s to taking the guesswork out of form performance and ushering in a new era of intelligent, high-converting forms with DeepOptimize. Your forms will never be the same – and that’s a promise.