# AI Coding Transformation for Engineering Teams

AI-native development for engineering teams, done safely. I roll out Cursor and Claude Code, train developers, and rebuild review and CI/CD around AI-generated code so output stays fast and trustworthy in production.

## Overview

Key areas:
- Cursor and Claude Code rolled out across your teams
- Hands-on training on your own codebase
- CI/CD and PR review rebuilt for AI-generated code
- Evals and guardrails that keep output trustworthy

## Solutions

### AI Dev Tooling Rollout

Cursor and Claude Code set up across your teams with shared rules, prompts, and project context, so every developer starts from the same baseline.

- Cursor and Claude Code configuration
- Shared rules, prompts, and context files
- Background and agentic workflows

### Team Training & Workshops

Hands-on sessions on driving AI tools against your real codebase, not toy examples.

- Live workshops on your repositories
- Prompting and review patterns that hold up
- Per-team playbooks

### CI/CD for AI Codegen

Pipelines restructured so AI-generated changes are tested, typed, and verified before they reach main.

- Test and type checks tuned for codegen volume
- Fast feedback on AI-authored branches
- Automated checks that block silent regressions

### PR Review & Quality Gates

A review process built for AI-written code, so larger, faster diffs still get human and automated scrutiny.

- Review checklists for AI-authored PRs
- Automated review with CodeRabbit
- Merge gates on lint, types, and tests

### Evals & Guardrails

Evaluation and classification systems that catch bad AI output before users do : the same approach I used on millions of chatbot events at Dashly.

- Eval suites for AI-driven features
- Classification and grading pipelines
- Regression tracking over time

### Codebase AI-Readiness Audit

Find what makes AI assistance unreliable in your codebase, then prioritise the fixes that unlock the biggest gains.

- Structure and type-safety review
- Context and documentation gaps
- Prioritised remediation plan

## Fixed-scope offers

Scope, price, and turnaround settled before the work starts.

### Claude Code on Your VPS

Your own coding agent server, set up on your machine with your keys and ready by end of day.

- Deliverable: Running agent server
- Turnaround: 1 day
- Price: $290

Includes:

- Tailscale private networking
- tmux sessions that survive disconnects
- Caddy with HTTPS
- Your machine, your keys, no middleman

### AI-Native Repo Audit and Setup

I go through your repo the way an agent has to, then build the missing pieces, so coding agents follow your conventions instead of inventing their own.

- Deliverable: Findings, then the setup merged
- Turnaround: 1 week
- Price: $1,200 (20 h × $60/h)

Includes:

- CI, linters, and checks reviewed as the gates agents run against
- The loops agents use to verify their own output
- Context burned before the first useful step, measured and cut
- `CLAUDE.md` and `AGENTS.md` rules, skills, and reusable workflows, merged

### Claude Code on Your VPS + Remote Control

The same agent server plus Telegram control, so you can start and steer runs from your phone.

- Deliverable: Agent server, phone-controlled
- Turnaround: 1 day
- Price: $490

Includes:

- Everything in the VPS setup
- Telegram bot wired to your sessions
- Start, steer, and read runs from your phone
- Access locked to you

### Personal AI Assistant for Your Business

A personal assistant in your team's Telegram that runs on your own server, knows your business context, and works with the services you already use.

- Deliverable: Running assistant, wired to your business
- Turnaround: 2 days
- Price: $590

Includes:

- Telegram, where your team already talks
- Connected to the accounts you use: Notion, GitHub, Vercel, PostHog
- Voice notes answered, reminders and recurring jobs on your schedule
- Your own server — business data never leaves your hands

### Evals for an AI Feature

A test set and scoring harness for a prompt already in production, so you can change it without guessing.

- Deliverable: Eval suite running in CI
- Turnaround: 2 weeks
- Price: $1,440 (32 h × $45/h)

Includes:

- Test set built from your real traffic
- Scoring harness for your quality bar
- Regression run wired into CI
- A session with the team who owns it

### Team AI-Native Onboarding

One day with your team on your own codebase, setting up the tools and the rules the way I run them day to day.

- Deliverable: One day, up to 10 developers
- Turnaround: 1 day on site or remote
- Price: $1,200 (12 h × $100/h)

Includes:

- Claude Code, Codex, and custom agent harnesses set up per developer
- Rules written for your repo, together
- Session monitoring, so you can see what the agents are doing
- An honest read on how far automation goes in your codebase

### Advisory Retainer

Async access and a weekly call for a founder or engineering lead who needs decisions checked and blockers cleared.

- Deliverable: Standing advisory access
- Turnaround: Starts within a week
- Price: $1,000/mo (10 h × $100/h per month)

Includes:

- Async questions answered same day
- One call a week
- Code or a prototype when your team is stuck
- Architecture and hiring decisions reviewed

## Reliability

### PR review for AI-written code

A review process for larger, faster diffs : human checklists plus automated review, so AI-authored changes still get real scrutiny.

### CI/CD restructured for codegen

Pipelines tuned for the volume AI tooling produces : tests, types, and checks catch regressions before anything reaches main.

### Security & quality gates

Merge gates on linting, types, and tests, plus dependency and security checks : faster output never lowers the bar.

### Evals that keep output trustworthy

Evaluation and classification systems for AI-driven features, drawn from evaluating millions of chatbot events at Dashly.

## Featured Work

- https://andrey-markin.com/projects/recruit-ai

## Tech Stack

Cursor, Claude, OpenAI, Gemini, Code Rabbit, TypeScript, ESLint, Biome, OXC, opencode

## FAQ

### How does an engagement work?

It starts with a call about what you're trying to solve. From there it's flexible : Q&A sessions, a hands-on codebase review, workshops on practices that work, or building CI pipelines and review bots for your team. Sharing your codebase is optional. I work alongside your developers on whatever moves the needle, never just a slide deck.

### What team size and format do you work with?

Single squads up to engineering orgs of several teams. Engagements mix live workshops, pairing on your codebase, and async setup of tooling, pipelines, and guardrails.

### Which AI tools do you set up?

Primarily CLI coding agents : Claude Code, Codex, opencode, and Pi, alongside Cursor. Automated review is flexible : CodeRabbit, custom pipelines, or security-review harnesses, whatever fits. Plus quality tooling like TypeScript, ESLint, and Biome. I pick the right tools for your stack, not a fixed list.

### How do you measure impact?

Developer productivity tracked against a pre-engagement baseline. Across 5+ teams that has meant +25%, alongside review-cycle time and how much AI-assisted code ships without rework.

### How do you keep AI-written code safe in production?

Through review processes built for AI-authored diffs, CI/CD restructured for codegen volume, security and quality gates on merge, and eval systems that flag bad output, the same approach I used to evaluate millions of events at Dashly.

### How is this priced?

Consulting is scoped per engagement based on team size and the depth of the rollout. Book a free call and I will put together a proposal after the initial audit.

## Contact

- Website: https://andrey-markin.com/services/ai-consulting-organizations
- Contact form: https://andrey-markin.com/#contact