Most AI builders spin up a brand-new toy from a blank page. Your business doesn't run on a toy — it runs on real systems, real data, and real consequences. Deft AI brings safe, guardrailed AI development to those systems, legacy or modern.
Your team describes what they want in plain English. A structured workflow builds, tests, and reviews it. A human approves every change before it ships.
Lovable, v0, and Bolt are great at starting from nothing. But the software your business depends on already exists — often a decade of accumulated logic no one wants to rewrite. Deft AI meets that system where it lives.
| Greenfield AI builders | Deft AI | |
|---|---|---|
| Starting point | A blank page — a brand-new app | Your existing production system, as-is |
| Environment | Their sandbox, their stack | A safe, prod-like sandbox of your stack |
| Who can build | Whoever writes the prompt | Your non-coders — with a human owning every merge |
| Safety | Hope it works | Auto-tests, adversarial review, permission guardrails |
| Output | A demo you rebuild for production | A reviewed pull request against your real codebase |
Start with the delivery workflow, or bring me a broader AI problem. It's the same principle throughout: AI does the heavy lifting; humans stay in control.
A structured pipeline where your team describes features in plain English and the AI builds, tests, and self-reviews them — ending in a pull request a human approves. Set up on your codebase, tuned to your stack.
I stand up an isolated, prod-like copy of your existing system — scripted, repeatable, and cheap to run — so AI-driven work happens safely, far away from production and its real data.
Custom agents, internal automations, API and data integrations, and hands-on help adopting AI across your team — scoped to a concrete outcome, not a science project.
Every feature moves through the same observable pipeline. You only step in at three points — describe it, test it, and respond to review. In between, AI codes, runs the tests, reviews its own work, and invites independent AI reviewers to second-guess it.
Before you ever see the work, two independent AI reviewers check it. One sees only the code and describes what it does — blind to the plan. The other compares that description to the plan — blind to the code. Mismatches surface before a human ever looks.
Take a large, legacy ASP.NET line-of-business system — the kind of codebase that scares off greenfield tools. I stood up a scripted cloud sandbox of it and wired in the guardrailed workflow, so the people who know the business best — product managers, operations staff, the owner, none of them developers — could describe the features they needed in plain English and get them shipped against the real system. Automated tests and a human-reviewed pull request keep every change safe.
The workflow runs on a state-of-the-art AI coding agent plus a layer of custom skills, slash commands, and permission hooks I've built and hardened on real, messy production codebases — not toy repos.
If it can be made to work safely on a decade-old system, it can work on yours.
Tell me about the system you'd love to move faster on. I'll tell you honestly whether this approach fits — and where it doesn't.