NK-01 / AI-NATIVE DEVELOPMENT / CONSULTING · COURSE / WAITLIST OPEN

SIGNALS → SPEC → SHIP → SELF-REVIEW ↻

Software that builds itself.

Then it improves the way it builds.
I set this up for companies — and teach it to specialists.

My AI support agent turns real feedback into specs, defends them against rival models, ships them through quality gates — and then rewrites the process it was built with. Companies started asking me how. Here’s the short answer, and how we can work together.

  • 41 hone unattended run of the loop
  • 18user-facing features shipped in that run
  • 2rival AI models every spec has to survive
  • ~100support requests a day handled by the agent, per typical client

It builds itself. Then it rebuilds the builder.

LEVEL 1 · THE PRODUCT LOOP

It finds and ships its own features

  1. 01

    Listen. Feedback from the support team (“no, we don’t answer it like that, because…”), my feature requests, anything odd in the production logs.

  2. 02

    Propose. Whatever matters becomes a change request against itself — and an entry on its own roadmap.

  3. 03

    Defend. A request grows into a full spec only after surviving cross-examination by GPT and Grok.

  4. 04

    Ship. A long pipeline in which every quality gate has to pass: tests, independent reviews, security, docs.

LEVEL 2 · THE PROCESS LOOP

It improves the way it builds itself

After every run, an LLM council reviews the machine, not the product: where the cycle lost time, what slipped past a gate, which rule would have prevented it. Then it rewrites its own harness — the skills and rules its agents work by.

  • Every process change is a hypothesis, tracked over the next runs until it is confirmed — or refuted and reverted.
  • At most two small, evidence-backed changes per run. Deleting a rule beats adding one.
  • No evidence, no finding.

Control engineers call this a self-tuning regulator: one loop runs the plant, a second loop re-tunes the first. I’ve spent twenty years building the first kind. This one has both.

HUMANS IN THE LOOP

Every support engineer has a page showing what became of each piece of feedback they gave — plus a weekly e-mail on the fate of their own suggestions.

Three things I could talk about forever.

01 DEVELOPMENT

AI-automated development

How to let agents take work from spec to production — with quality gates, rival-model review and a process that keeps improving itself.

IN PRODUCTION

  • 41 h unattended run, 18 user-facing features shipped
  • The product loop and the process loop: see above ↑
02 KNOWLEDGE

A knowledge base that fills itself

Why a company needs one, what it’s for, and how to build it so nobody has to write docs: decisions, how-tos and context captured from chats, mail and meetings — and put to work.

IN PRODUCTION

  • SmartBot Cloud ↗ turns team chats into a searchable knowledge base
  • An AI product manager reads a project’s mail, chats, meeting transcripts, Confluence, Jira and code — and writes the tickets
03 SUPPORT

AI tech support

How to automate first-line support on every channel your customers use — with humans in the loop where judgment matters.

  • In-product chat
  • E-mail
  • Voice · phone

IN PRODUCTION

  • ~100 support requests a day per typical client, handled by the agent
  • Telegram support bot ↗ for a family of HVAC controllers

Two ways in.

Companies have already been coming to me for advice on AI-driven development — so far only by word of mouth. From early 2027 it’s an open offer: for teams, and for specialists who want to build like this themselves. The waitlist is open now.

FOR COMPANIES · CONSULTING

AI automation, set up in your team

  • AI-automated development. Agents that take a task from spec to tested, reviewed, documented code — with quality gates, rival-model review and a process that improves itself. Your engineers steer; the pipeline grinds.
  • A knowledge base that fills itself. Decisions, how-tos and context captured from your chats, mail, meetings, Confluence, Jira and code — and put to work.
  • AI tech support. In-product chat, e-mail and voice over the phone, with humans in the loop where judgment matters.
  • Team adoption. Hands-on sessions with your engineers, on your codebase, until the new way of working sticks.

HOW WE START

  1. 01You send a few lines about your team
  2. 02A short call: where AI pays off first
  3. 03A concrete plan — or a pilot on one real loop
Join the waitlist Next slots: early 2027

FOR SPECIALISTS · COURSE

Intro to AI-native development

From your first agent to your own pocket IT department.

An author’s course for developers, product people and engineers who want to build like this themselves: the essentials, plus recipes that work in practice.

INSIDE THE COURSE

  1. 01AI-automated development: agents, specs, quality gates
  2. 02A knowledge base that fills itself
  3. 03AI tech support: chat, e-mail, voice
  4. 04Putting it together: your pocket IT department

SELF-PACED

Materials & recipes

Concepts, practical recipes and templates, published right here on the site.

In preparation · early access

Get early access

1:1

Personal mentoring

A direct line to me. We work on your real projects, step by step, until you run your own pocket IT department.

From early 2027 · limited spots

Apply for mentoring

Let’s build
your loop.

A few lines are enough: who you are, what you build, and where AI should take the load. I reply personally — in English or Russian.

Nikita Kapitonov

Nikita Kapitonov

Full-stack AI product engineer. MIPT-trained spacecraft control engineer, two decades of HVAC controllers and monitoring products — now building software that builds itself.

Full résumé