Form Flow is a B2B SaaS for enterprise and financial teams where AI handles the full form logic. I built the AI layer that turns plain English into working software behavior.

The rule engine sends an admin's plain-English input to the OpenAI API, which generates executable JavaScript handlers from the description. Those handlers run inside a vm2 sandbox on Node.js, so AI-generated code executes without touching the rest of the app. It works like a locked box: code runs inside, nothing escapes. Rules chain in order and trigger status changes, alerts, and display actions. Getting output consistent enough to run in production without malformed handlers took real iteration, the careful side of AI integration.

A RAG layer reads the surrounding form structure and the exact field a user is on, then sends a prompt scoped to that field, so the AI assistant returns an answer that fits the question instead of something generic. On financial intake forms, this cut abandonment with no human agent involved.

Branding automation pulls colors, logos, and typography from any URL for white-label deployment. Identity verification runs through IDmission with live face matching for compliance.

What the client gets: non-technical admins define complex form logic in plain English, an AI assistant guides users field by field, and AI-generated code runs safely inside a sandbox.

Perfect

Most AI projects do not fail because the model is not smart enough.

They fail because there is no real system around it.

A lot of businesses start with the wrong question:

"How can we use ChatGPT?"

The better question is:

"What workflow are we trying to automate, improve, or scale?"

That difference matters.

A prompt can give you a useful answer once. A productized AI workflow can produce useful results repeatedly.

When I build AI-powered apps, automations, or Webflow-integrated tools, I usually focus on three things before writing the actual solution:

Clear inputs What data, context, or user action should trigger the workflow?

Clear acceptance criteria What does a correct result look like?

Clear verification How do we check the output before it reaches the user or customer?

Without these three pieces, AI projects become unpredictable and expensive.

With them, AI becomes much more practical:

lead qualification

internal tools

content workflows

client portals

CRM automations

Webflow + AI integrations

full-stack AI products

The best AI products are not built by babysitting prompts.

They are built by designing reliable workflows around the model.

I help founders and teams turn AI ideas into reliable full-stack products, automations, and Webflow-powered workflows.