AI-accelerated development
AI-accelerated. Engineer-verified.
AI makes software faster to build, and easier to build badly. At devmonks we use it to move faster, and an engineer reads, understands, tests and lints every line before it ships. I hold OpenAI's API Deployment Practitioner and Technical Practitioner certifications, and devmonks is a Select-tier member of the OpenAI Partner Network.
Credentials
Credentials you can check
- OpenAI API Deployment Practitioner
- 2026
- OpenAI Technical Practitioner
- 2026
- devmonks, OpenAI Partner Network
- Select tier
- Odoo LLM, commits as its largest contributor
- 1,492
- Rajshahi AI Summit, panel on AI agents and workflow automation
- 2026
What it means
Faster, without shipping what nobody read
AI-accelerated means models do the parts of the work they are good at: drafting code, reading an unfamiliar codebase, writing tests, checking edge cases. Engineer-verified means none of it ships on trust. Every change is read, understood, tested and linted by an engineer, and the bar does not drop because a deadline is close.
The speed shows up in the estimate. We estimate from the total effort with AI-accelerated work built in, so projects move much faster than with a traditional dev shop, and the estimates stay conservative on purpose: finishing early builds trust, finishing late destroys it.
AI products
AI products we build
- Assistants that answer from a company's own knowledge, with retrieval over its documents and data.
- Language models inside existing business software, as in Odoo LLM: providers, retrieval, knowledge bases and agent tooling across thirty-four modules.
- Consumer AI products: a social platform built around user-created AI characters, with a generation pipeline, persistent chat state and subscription billing.
- Agents and workflow automation that act on real systems, with the guard rails they need.
- AI features added to an existing web or Flutter app, with the costs and failure cases planned for.
Own tooling
We build our own AI tooling
AI-accelerated work is only as good as the way it is run. I built claude-garage, an open-source Rust tool that puts every Claude Code session on one live terminal wall, flags which one needs input, isolates each in its own worktree and keeps diff review in one place, all running locally. It is public on GitHub and npm.
Because AI accelerates the routine work, we choose the stack that fits the problem rather than the one we are stuck with: Python, TypeScript, Dart, Swift or Rust.
How we keep it safe
Every line owned
Code written with AI is still our code. We review it the way we review any pull request: does it do what it should, does it fail safely, will the next engineer understand it. Tests and linting run on every change, and a fresh build reaches staging every couple of days so you can see the result yourself.
For AI features, the same discipline applies to the model: which one to use, what it costs at your volume, what happens when it is wrong, and what data it is allowed to see.
Questions
Questions founders ask
What is AI-accelerated software development?
Building software with AI models doing much of the drafting, reading and testing, while engineers own every decision and review every change before it ships.
Does AI-written code mean lower quality?
Only if nobody reads it. Every change we ship is read, understood, tested and linted by an engineer.
Is devmonks an OpenAI partner?
Yes. devmonks is a member of the OpenAI Partner Network at the Select tier, and I hold OpenAI's API Deployment Practitioner and Technical Practitioner certifications.
Can you add AI to a product we already have?
Yes. We start by finding where a model genuinely helps, then build it into the existing web or mobile app with the costs and failure cases planned for.
Build it faster, and own it.
Tell me what you want to build or where AI should fit. A first call is free and you get an honest read on scope, cost and risk.