What we build and how we prove it works.
Five verification systems, each with published source code, test results, and cryptographic evidence. Every metric below comes from actual test runs on local hardware.
Provenance Layer
IRONROOTWrite-once audit trails with cryptographic integrity for every AI decision.
Every input, output, model version, and configuration parameter gets a tamper-evident record. SHA-256 hash chains link each decision to the one before it, creating a verifiable sequence that auditors and regulators can independently validate.
The provenance layer generates AI bills of materials with traceable, auditable rationales: the exact characteristics NIST defined for trustworthy AI in critical infrastructure. Each record is write-once. Modification breaks the chain, and the break is detectable.
Runs entirely on local hardware. No cloud dependency, no data leaves your infrastructure. Five capability layers cover ingestion, hashing, chain validation, query, and export.
Adversarial Verification
AASMSRed and Blue teams stress-test every component in Docker-isolated sandboxes. Only verified survivors deploy.
The Red team proposes mutations, fault injections, and adversarial inputs. The Blue team evaluates, patches, and validates. Each cycle runs in isolation, and every proposal must pass the full test suite before integration.
Six evolution cycles completed with zero errors. 24 proposals verified and applied. The system enforces a 100% test pass requirement: no exceptions, no overrides. If a component fails under adversarial conditions, it does not ship.
This is not simulated testing. Each Red team attack targets real failure modes: edge cases in parsing, race conditions, malformed inputs, and prompt injection variants. Documentation of each attack and its resolution creates the adversarial robustness record that regulators require.
Contradiction Detection
ABESSurfaces uncertainty instead of hiding it. 0% hallucination rate in controlled runs.
Standard LLMs present high-confidence answers even when the underlying reasoning is contradictory. Contradiction detection uses entropy scoring and belief auditing across a 14-phase agent pipeline to identify and flag inconsistencies before they reach the end user.
In controlled evaluation across 1,000 prompts, the system maintained a 0% hallucination rate. When the model is uncertain, the system says so. When sources conflict, it surfaces the conflict with confidence scores rather than picking a side.
822 tests validate the pipeline end to end, from prompt ingestion through belief extraction, entropy calculation, contradiction resolution, and final output verification.
Decision Auditing
RLFUSIONMulti-path retrieval with full trace logging for every query.
Every query enters a multi-path retrieval pipeline: RAG, CSWR, and CQL policy evaluation. Each path produces a candidate response. The orchestrator evaluates, selects, and records the full decision trace from input to verified output.
3,000+ test iterations across 6 test suites with a 100% pass rate. Latency averages approximately 10 seconds on an RTX 5070 running Llama 3.1 8B via Ollama. No cloud calls, no external API dependency.
The trace log captures every decision point: which retrieval paths fired, what the policy evaluated, which candidate was selected and why. This creates the "traceable, auditable rationales" record that compliance frameworks require.
Multi-Agent Orchestration
SNRESwarm coordination with consensus verification for complex workflows.
When a task exceeds what a single agent can reliably handle, the orchestration layer distributes work across specialized agents. Each agent operates within defined boundaries, and the coordination layer verifies consensus before committing results.
Agents negotiate, validate each other's outputs, and produce a consensus record. Disagreements are surfaced, not suppressed. The orchestration trace captures which agents participated, what each contributed, and how the final output was assembled.
Built for enterprise workflows where auditability matters: document processing pipelines, multi-step analysis, and complex decision chains where a single point of failure is unacceptable.
See these systems in action.
Verification artifacts, test telemetry, and audit trail demos with real data from real test runs.
View the Evidence