Verify your AI systems.

Runtime verification for AI systems operating in environments where the wrong action has real consequences.

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agent_capabilities system_prompt model inference_engine hardware / routing what changes context engineering memory files · skills recurring tasks · tool access what changes guardrails · constitutions · soul.md sampling parameters output constraints what changes weights · version · fine-tuning distillation · quantization precision · tokenizer what changes vLLM · TensorRT · Transformers kernels · caching speculative decoding what changes GPU type · batch size load balancing · provider routing multi-region AI STACK

The problem

AI systems fail differently than traditional software — silently, probabilistically, and without a stack trace.

01

Provenance is unverifiable

A model claims an identity, a lineage, a benchmark score. Without behavioral evidence, there is no independent way to confirm any of it.

Cursor / Meta, 2025–2026

Cursor marketed its Composer 2 model as in-house before acknowledging it was built on Moonshot AI's Kimi K2.5.

Meta submitted a chat-optimized variant of Llama 4 Maverick to the LMArena leaderboard while the publicly released model performed differently.

Unknown provenance makes an organization vulnerable to security and compliance risks with unbounded costs.

02

Endpoints drift silently

Providers update, quantize, route, or swap models behind stable API names. Standard health checks see nothing. Applications break.

Anthropic, Mar–Apr 2026

Three overlapping silent changes to Claude — a reasoning-effort downgrade, a caching bug, and a system-prompt word limit — degraded coding performance for six weeks before Anthropic published a postmortem.

OpenAI's GPT-5.3-Codex was caught routing Pro subscribers to GPT-5.2 while the CLI displayed the wrong model name.

Output quality drops, retry rates climb, guardrails erode — and none of it shows up in uptime dashboards.

03

Unexpected agent behaviors

Skill files, memory files, MCP tool descriptions, and behavioral configs directly steer agent actions. A single malicious edit persists across sessions.

Cisco / Multiple, 2025–2026

Cisco researchers demonstrated that injected instructions in Claude Code's memory file silently altered agent behavior across sessions and projects.

Malicious MCP configurations in repositories could execute code with developer permissions across four major coding agents.

As agents proliferate across the enterprise, a single compromised agent can delete databases, exfiltrate credentials, and take unauthorized actions that may not be recoverable.

The platform

Verification infrastructure for every deployment

Provenance → Fingerprinting

Behavioral Fingerprinting

Provenance claims are only as good as the evidence behind them. Behavioral Fingerprinting extracts a semantic fingerprint from any model's input-output behavior. Compare fingerprints to reveal fine-tuning relationships, distillation lineage, quantization variants, and false identity claims before they become license, compliance, or security liabilities.

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Endpoint drift → Stability

Stability Monitoring

Continuously monitoring endpoints to detect changes — model swaps, version updates, quantization changes, inference stack shifts, and parameter drift. Produces an audit trail of stability periods and change events usable by infrastructure ops, security, and compliance.

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Agent behavior → Trajectory

Agent Behavior Tracking

Track agent behavior tendencies as they adapt from within the production environment. Ensure agents operate within scope of their expected tasks and workloads. Know right away if an agent is going rogue.

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Who we serve

Verification for environments where mistakes carry real consequences

Defense & National Security

Adversarial environments demand cryptographic guarantees

Ensure the right models are deployed in mission-critical systems. Detect & defend against adversarial AI before they tamper with the information supply chain.

Security & Compliance

Continuous verification is the new security posture

AI systems expand the attack surface for every organization. Verifiability infrastructure ensures system integrity and robustness to the new AI attack vectors.

AI Infrastructure & Platforms

Demonstrate the integrity of your inference stack

Integrate continuous stability monitoring into your platform. Provide inference customers with data to instill trust in their production workloads.

Research

Peer-reviewed research

All publications →

ICML '26 · AIWILD Workshop

Tracking the Behavioral Trajectories of Adapting Agents

A methodology for measuring agent behavioral traits as directions in embedding space, applied to diffs of agent skill files over time. 91.2% sign classification accuracy on data-seeking trait detection.

ACM CAIS '26 · System Demo

Behavioral Fingerprints for LLM Endpoint Stability and Identity

Stability Monitor: black-box stability monitoring that detects changes to model family, version, quantization, and behavioral parameters.

ICDS '25

Hardware-Rooted Trust Anchors for Sovereign AI Processing

Cryptographic verification of location, identity, and confidentiality in cloud environments for sovereign AI processing.

See it working.

Schedule a 30-minute briefing to see behavioral fingerprinting and runtime verification on your own models.

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