We build the spec-native environment between AI coding tools and real software delivery.

fabriqa.ai is not another single-model chat shell or isolated agent runner. It is a unified system for teams who use multiple AI coding tools and still need specs, durable context, structured execution, and delivery guardrails.

What we build

Keep intent, agents, and delivery in one place.

01 · Intent

Shared intent becomes managed work.

fabriqa.ai turns product intent into versioned specs, work items, decisions, and review state. Git is where code lives; fabriqa.ai is where the team keeps the context agents need to work safely.

02 · Tools

Use the agents your team already trusts.

Route planning, implementation, testing, and review across Claude Code, Codex, GitHub Copilot, Cursor, Kiro, OpenCode, ACP-compatible agents, and BYOK models without making one vendor the center of the workflow.

03 · Delivery

Review agent work across the whole solution.

Real product changes rarely live in one repo. fabriqa.ai keeps multi-project work, provider context, approvals, diffs, costs, and audit trails in one reviewable surface so humans stay in control.

Founder-led

Building in public with the teams shaping agentic development.

fabriqa.ai is led by founder Cengiz Han around a simple thesis: as agents write more code, teams need a new place to hold intent, specs, work items, decisions, and review state.

The product is still early and deliberately founder-close. We are building with teams who already use multiple AI coding tools and need provider-open workflows, managed specs and work items, multi-project context, and review against the original intent.

If you are evaluating agentic development for a real team, designing spec-backed delivery workflows, or exploring early investment in the post-IDE coordination layer, we are open to thoughtful conversations.

Where to start

Read the foundations behind the loop.

— Foundation

Agentic Development Environment →

How fabriqa.ai defines the category around specs, agents, and reviews.

— Foundation

Spec-driven development →

Why specs are the native operating layer for reliable AI coding.

— Surface

Unified AI coding tools →

Every ACP-compatible agent in one spec-native workflow.