BACON-AI isn't another AI coding toy. It's a battle-tested framework born from 30 years of enterprise project delivery and 2 years of empirical AI research. It orchestrates teams of 100+ parallel AI agents under deterministic governance that gets smarter with every project.
Built on 30 years of enterprise project delivery — SAP, ERP, large-scale transformations. Real-world governance, not theoretical frameworks.
Not a simple 3-step process. A complete enterprise methodology — from problem definition to deployment and continuous learning.
Define the problem/objective. Check knowledge base for existing solutions. Spawn parallel research agents across Claude, GPT, Gemini. User stories, empathy maps, competitive landscape. Architecture design, solution specs. Six Thinking Hats risk evaluation.
API docs and guides generated. Sprint planning with resource allocation. 100+ parallel AI agents implement with TDD. Each receives a context propagation package with role, scope, and governance rules. Peer reviews by competing AI models.
Deploy to production. Post-deploy real-world validation. TUT → FUT → SIT → RGT quality gates. Performance optimisation. Evidence is the deliverable — code is the supporting artifact. No shortcuts.
SSC retrospectives with 5 specialised AI agents. Self-annealing rules evolve through evidence (OBSERVE → ENFORCE). Lessons learned feed the next project. The system gets smarter with every delivery.
Each phase runs governed agents in parallel • Cross-model peer reviews at every gate • Full audit trail • Read the NPSL White Paper → • Explore Interactive BPMN Diagram →
Other tools give you a single AI writing code. BACON-AI gives you a governed team with quality gates, audit trails, and self-improving rules.
| Capability | Bolt.new | Lovable | Cursor | Devin | BACON-AI |
|---|---|---|---|---|---|
| Multi-agent orchestration | ✗ | ✗ | ✗ | Limited | 100+ agents |
| Governance framework | ✗ | ✗ | ✗ | ✗ | 8 SA rules |
| Quality gates (TUT/FUT/SIT/RGT) | ✗ | ✗ | Basic | Basic | 5-phase pipeline |
| Self-improving rules | ✗ | ✗ | ✗ | ✗ | Self-annealing |
| Enterprise methodology | ✗ | ✗ | ✗ | Basic | 12-phase + PDCA |
| Provider agnostic | ✗ | ✗ | Multi-model | ✗ | Any LLM |
Real projects built and governed by the BACON-AI framework. Access codes available on request.
A complete ERP documentation portal generated by the BACON-AI Coding Engine — including BPMN diagrams, Mermaid flowcharts, and interactive guides. Built with full governance and quality gates.
View DemoReal-time monitoring dashboard for multi-agent AI project orchestration. Track agent status, quality gates, and governance compliance.
Coming SoonInteractive visualization of the self-annealing governance pipeline. Explore rule maturity stages, evidence metrics, and drift detection.
Coming SoonThe governance layer that prevents AI agents from silently eroding their own quality constraints.
When an agent is rewarded for "task marked complete," it optimises for that signal — not for quality. NPSL breaks this loop with structural controls.
Rules mature through measured quality signals, not time. OBSERVE → CHALLENGE → ENFORCE → IMMUTABLE. Promotion requires statistical evidence.
Don't Trust, Verify. Proximity Beats Priority. Impossible Beats Improbable. Three structural principles that prevent agent drift.
DIAGNOSE → FIX → ANNEAL → SYNC. The framework detects drift, fixes it, promotes the fix to a rule, and propagates across all agents.
Get early access to the BACON-AI framework and be among the first to deploy deterministic AI development at enterprise scale.