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Crnogochi is Bild's AI-enabled software delivery methodology. It puts a dedicated AI assistant on every phase of a build - discovery, design, user stories, code, and QA - and coordinates them through a single orchestrator and a governance tool that keeps humans in command.
The premise is simple: AI code generation is fast, but speed without governance is a liability. Crnogochi keeps that speed aligned with visibility, control, and operational discipline - and writes every decision into a shared memory bank the whole team can see.
It has been validated across four deliberately different projects and proven on a live commercial engagement - delivered 31% under its traditionally-sized estimate, with change requests absorbed seamlessly into scope along the way.
Traditional software delivery runs as five sequential steps across two phases - discovery and build. Requirements, high-level design, low-level design, development, deployment. Every handoff and every artifact between them is manual.
AI code generators promised to collapse that timeline. But raw generation introduced a new problem: you gain speed and lose the things that make delivery safe - a clear view of what's being built, control over architecture and lifecycle, and the discipline that keeps quality predictable across every handoff.
So the real question was never "can AI write code faster?" It was "how do we scale speed without losing visibility, control, and manageability?"
Crnogochi keeps AI speed aligned with governance by attaching intelligence to the methodology itself - not bolting a chatbot onto the side of it.
Phase 1 - Five assistants, one tool
A dedicated assistant attaches to each phase - ai-discovery, ai-tech-story, ai-code, ai-qa, ai-deploy - adding AI speed without breaking the process. All five are managed from one Governance Tool.
Phase 2 - Orchestrated pipeline
A dedicated ai-orchestrator coordinates four agents and automates every handoff. Context flows continuously from discovery to production deployment - nothing is re-explained between steps.
Phase 3 - Built into existing tools
No new environment. Crnogochi runs inside Jira, Confluence, GitHub, Azure DevOps and Figma. A project-scoped memory bank plus rules keep output consistent, and reasoning models are swappable per task.
Governance by design
The developer approves every handoff. Every session is tracked and written to a shared memory bank visible to the whole team. Transparent by design - self-learning, never a black box.
Crnogochi began as a proof of concept on HillMetrics in 2025 and is now live across four deliberately different projects - a commercial plugin, a research migration, and two more in analysis. The work is backed by a grant from Montenegro's Innovation Fund.
Next, Bild is broadening the assistant set and tightening the orchestrator so more of its delivery runs through one transparent, self-learning pipeline - faster every quarter, and still fully under human control.
If AI speed only counts when you can see, govern, and trust what's being built, that's exactly the problem Crnogochi was built to solve. Let's talk about bringing it to your project.
