Your AI systems are making decisions.
Can you prove what they decided?

DarkMatter is an independent audit layer for AI agent pipelines. It records every agent decision as a verifiable, tamper-evident chain, outside your infrastructure, so you can prove it to someone who wasn't there.

The problem
⚠️
Agent failures are invisible
When a multi-agent pipeline produces a bad result, there's no clean way to trace which step went wrong or why. You restart from scratch and hope for the same conditions.
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Self-reported logs aren't credible
Audit logs stored inside your own infrastructure have the same credibility problem as auditing your own books. A regulator or auditor needs evidence they can independently verify.
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Context is locked inside frameworks
LangGraph, OpenAI threads, CrewAI, each stores state internally. When you mix them, there's no unified picture of what happened across the whole pipeline.
⚠️
Experimentation is destructive
Trying a different approach at step 2 of a 6-step pipeline means rerunning everything. There's no way to branch from a checkpoint without touching the original run.
What DarkMatter does
✓
Every decision is immutable
Each agent action becomes a context commit, an append-only node in a chain. Once committed, it cannot be modified. SHA-256 hash chaining makes tampering detectable.
✓
Independent of your infrastructure
DarkMatter is an external layer. Your agents commit context to an independent system outside your control, giving the audit trail the same credibility as a third-party auditor.
✓
Works across any framework
One API key. Commit from LangGraph, raw API calls, CrewAI, or any local model. The lineage chain spans your whole pipeline regardless of what's running underneath.
✓
Fork without destroying the original
Branch from any checkpoint like a Git branch. Try a different approach at step 2 without touching the original chain. Both branches are preserved with full lineage.
Who uses DarkMatter
Engineering teams
Building multi-agent production pipelines
Teams chaining Claude, GPT-4o, and local models together in production workflows that need reliable handoffs, debugging capability, and the ability to replay what went wrong.
AI platform teams
Managing internal agent infrastructure
Platform engineers who need a cross-system observability and lineage layer that works across multiple frameworks, teams, and models without requiring framework-specific instrumentation.
Compliance and legal
Demonstrating AI accountability to regulators
Teams subject to EU AI Act, US state AI laws, financial sector regulations, or healthcare AI requirements that need tamper-evident records of high-risk AI decisions.
CTO and VP Engineering
Reducing AI system risk before it becomes a problem
Leaders who understand that as AI systems take on more consequential decisions, the ability to prove what happened is a business necessity, not just a technical nicety.
Regulatory context

AI regulation is accelerating. DarkMatter's independent, tamper-evident audit trail is designed to support compliance with the following frameworks:

EU AI Act (Art. 12 and 19), requires high-risk AI systems to automatically generate logs kept for at least 6 months, stored by the provider or deployer.
US state AI laws, Colorado, California, and other states are enacting AI accountability laws requiring explainability and audit trails for consequential AI decisions.
Financial sector, institutions using AI for credit, trading, or risk decisions face explainability requirements from regulators including the SEC, FCA, and MAS.
Healthcare AI, AI-assisted clinical decisions increasingly require documentation of the reasoning chain for liability and regulatory purposes.

Ready to add a verifiable audit trail to your AI pipeline?

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