Is this the same as "vibe coding" or letting AI commit to production?

No. AIDD is built on hard governance rules: no auto-merge, feature branches only, transparent actions before every write, and least-privilege tool access. Every merge requires human review.

What's the difference between AIDD and just giving developers Copilot or ChatGPT?

Copilot and ChatGPT operate at the level of individual prompts and tasks. AIDD operates at the level of your full SDLC, reading your repo, issues, standards, tests, and CI, then producing reviewed artifacts (plans, code, tests, MR notes) with an audit trail, not just suggestions.

Will this replace our developers?

No. AIDD changes work allocation, not accountability. Architects and developers keep decision authority over architecture, review, prioritization, and release. AI removes repetitive, context-heavy, reviewable work, not ownership.

What tools does this integrate with?

Claude Code is the execution hub; the surrounding tooling stays flexible. We've integrated with GitHub/GitLab/Bitbucket, Jira/Azure DevOps, Confluence, Figma/Sketch, TestRail/qTest/Xray, GitHub Actions/GitLab CI/Azure Pipelines, and Playwright/Cypress/Selenium, among others.

How long until we see results?

Assessment typically takes 3–5 days. A scoped pilot on one workflow runs 2–4 weeks, with before/after metrics defined upfront so impact is measurable from day one.

Engagements are scoped around outcomes, not seat licenses, fixed-fee for assessment, per-use-case for the pilot, and retainer/outcome-linked once you scale. Get a custom estimate on a scoping call.

AI-driven software development services

Built on Claude Code

No auto-merge, ever

Enterprise governance by design

Accelerating development with AI, while keeping results human-driven. We help engineering teams at large enterprises implement Claude Code as a regulated participant at every stage of the software development life cycle (SDLC), not as an experiment, but as a controlled operational model.

Book a strategy call See how it works

The delivery challenge that never goes away

Most engineering organizations aren't short on ideas; they're constrained by repeatable delivery friction: slow cycles, inconsistent quality across teams, late-discovered gaps, and unpredictable timelines that make planning harder than it should be.

01

Slow cycles

Specification, scaffolding, tests, reviews, and documentation add manual effort to every single feature.

02

Quality variance

Standards differ across teams, repositories, and individual interpretations of guidelines.

03

Late rework

Gaps in requirements, design coverage, or test cases are usually found after implementation, when fixing them costs the most.

04

Delivery risk

Unpredictable cycle times and handoffs make planning, budgeting, and stakeholder commitments harder to keep.

This isn't about “AI for AI's sake.” It's about using AI to eliminate recurring obstacles within the software development life cycle (SDLC) you're already using.

Many of these constraints trace back to systems that weren't built to support AI-assisted workflows in the first place.
See what AI modernization readiness looks like

AI as a governed contributor, not an autonomous replacement

The term “AI-driven development” is often used loosely. We draw a clear distinction between two very different things:

AI feature development ("AI as a product")

AI-powered functionality built into the client application: chatbots, smart search, auto-classification, recommendations, and document processing.

Focus: what the product does for end users.

AI-driven development ("AI as a team member") — our focus

AI helps produce requirements, code, tests, documentation, review notes, and operational feedback within a controlled workflow.

Focus: how the team builds software.

AIDD treats AI as a governed contributor across the delivery lifecycle, with humans retaining decision authority at every step.

Where AIDD creates measurable value

The right question isn't "How much code can AI write?", but rather "What time constraints can AI actually reduce?"

Speed

Cycle time from requirement to reviewed implementation

Quality

Test coverage, review defects, escaped defects

Predictability

Scope clarity, estimation confidence, fewer late gaps

Cost efficiency

Lower manual effort per feature, less rework

Capacity

More throughput with the same team composition

How we measure it

Before/after pilot metrics we track with every client:

Cycle time per selected workflow

Number of review iterations and rework items

Test coverage and generated test quality

Defect leakage and CI failure patterns

Developer time saved on repetitive tasks

AIDD does not strip engineers of their ownership of the project. It frees them from low-value manual tasks involving repetitive, highly context-dependent, and verifiable tasks, while your architects and developers retain decision-making authority.

AIDD changes how your team builds software. If the underlying system itself needs to become easier for AI to understand and work with — legacy code, fragmented data, unclear architecture — that's a separate but complementary service

AI-Ready Software Modernization

Powered by Claude Code, built around your existing stack

Claude Code — Anthropic’s official AI-powered programming assistant, which runs locally on the developer’s computer, serves as the central hub for task execution. The accompanying tools remain flexible, adapt to each client’s needs, and can be replaced.

Integrates with

GitHub

GitLab

Jira

Figma

TestRail

GitHub Actions

Playwright

Specialized agents, not one generic assistant

Each agent has a focused system prompt, specific tool access, and the right model for the task, like a specialist on your team:

Backend developer

server-side: API, business logic, data layer, migrations

Frontend developer

client-side: UI components, state management, routing

Test automation

all test types: unit, integration, API, E2E

Security reviewer

auth, injection, access control, OWASP Top 10

DB migration

schema change safety: reversibility, data loss risk

Business analyst

plain-language requirements for product owners and managers

Design analyst

screens, UX flows, design system coverage mapping

AIDD works best when Claude Code can actually understand your codebase. If your system is large, undocumented, or fragmented, our AI-readiness assessment can close that gap first.

AI-readiness assessment

How it works: AI proposes - humans approve

Enterprise AIDD has to be designed for governance before it's designed for scale. People set goals, analyze plans, approve changes, and take responsibility for the results. AI streamlines the work between these stages. So it is not “programming based on mood.”
Four TYMIQ principles:

01

No auto-merge

AI-generated code never enters production without developer review and explicit approval.

02

Feature branches only

AI commits only to controlled feature branches — never directly to main or develop.

03

Transparent actions

Before any write operation, AI shows the intended changes and payload for approval.

04

Least privilege

Tools, repositories, and environments are exposed only when the workflow requires them.

Hard rule: No write operations without a visible plan. No merges without human review. No deployments without approval.

Five-step flow

Understand

Plan

Implement

Review

Merge

Approval gates are part of the workflow, not an afterthought.

Find out what AIDD could save your team.

Share your current SDLC bottlenecks, and we'll show you where the numbers move first.

You will talk to our leadership

Andrei Zhukouski

Chief Strategy Officer

Kanstantsin Miranovich

Co-Founder / CTO

Live us a message

Thank you! Your submission has been received!

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What changes for the team?

Before

write boilerplate by hand
scaffold tests manually
review code line by line

After

trigger AI workflow and review the plan
AI generates tests automatically
focus on architecture & logic

Developers

Product owners

Before

wait for the analysis documents
write requirements manually
discover scope gaps late

After

AI delivers finalized requirements
review and approve analysis docs
open questions surfaced upfront

QA / Testers

Before

write test cases after delivery
manual E2E test authoring
test management updated manually

After

AI generates cases from requirements
E2E scenarios automated live
test plans created automatically

Best first use cases for low-risk adoption

Start where work is repeatable, reviewable, and measurable.

Pilot workflow

Why it's safe

Value created

Success metrics

Test generation

Tests are reviewed and run in CI

Higher coverage, faster QA prep

Coverage, defects found early

Code review support

AI comments, humans decide

Faster review, standard checks

Review time, defect categories

Documentation sync

No production risk

Less drift between code and docs

Outdated docs reduced

Recommended starting point: a real-world workflow with a backlog, accompanied by clear “before” and “after” metrics, rather than a general experiment in the field of artificial intelligence.

Implementation roadmap

We scale up only after a pilot project has demonstrated high quality, control, and a measurable impact on productivity. At the same time, we tailor our services to your workflows and the value they deliver.

Get a custom estimate

01

Assess (3–5 days)

Identify candidate workflows, repo/tool access, risks, data policy, and readiness gaps.

02

Pilot (2–4 weeks)

Configure Claude Code workflows, rules, and approval gates for one selected use case.

03

Enable (1–2 weeks)

Train teams, refine prompts/rules, define review standards, and an operational playbook.

04

Scale (ongoing)

Extend to additional workflows, teams, and repositories with KPI tracking and governance.

Consider more services

AI-driven software development services

The delivery challenge that never goes away

AI as a governed contributor, not an autonomous replacement

AI feature development ("AI as a product")

AI-driven development ("AI as a team member") — our focus

Where AIDD creates measurable value

How we measure it

Powered by Claude Code, built around your existing stack

Specialized agents, not one generic assistant

How it works: AI proposes - humans approve

Five-step flow

Find out what AIDD could save your team.

What changes for the team?

Best first use cases for low-risk adoption

Implementation roadmap

Consider more services

Custom software development

Software reengineering

DevOps solutions

Mobile development

IT outsourcing

AI development

Software maintenance

Dedicated teams

Let’s explore what an AI-ready transformation could look like for your product

FAQ