Because 90% of AI startups are opaque about their unit economics.

Vendors often show curated demos (“happy paths”) that hide real-world costs and failure rates. We act as a “Consumer Reports for AI,” running independent stress tests to tell you if a project is viable or vaporware.

• Investors: To perform technical due diligence before investing.
• Hedge Funds: To track public AI stocks (e.g., MSFT, GOOGL) by measuring their underlying model quality.
• Enterprises: To select the right AI vendor based on real production reliability, not marketing claims.

We offer two primary tiers based on your needs:

• Terminal Tier ($2K/month): For individual analysts. Includes full dashboard access, portfolio tracking, and basic report generation.
• Institutional Tier ($50K/year): For funds and organizations. Includes full API access (10k calls/hour), custom “Secret Shopper” missions, and priority support.

We currently offer 30-day free pilots for qualified investment firms.

Standard benchmarks (like MMLU) measure potential in a lab setting. We measure actual production reality.

Our agents test for:

• Net Burn: Is the model profitable per task, or does it burn cash?
• Reliability: Does it work 99% of the time, or does it hallucinate under load?
• Safety: Can it be jailbroken in a real-world attack scenario?

This platform is not just software—it’s a data accumulation problem. Our advantage comes from proprietary execution logs, real unit-economics data, and cross-company benchmarks that individual funds or cloud providers cannot legally or economically aggregate. The moat compounds with every audit run.

Audits are opt-in and primarily requested by capital providers during diligence. High-quality teams benefit from third-party validation, faster closes, and better terms. Companies that refuse audits signal elevated risk—which itself becomes valuable information for investors.

No. It’s both!. Audits generate data, and data products generate revenue. Our compute costs per analysis are dwarfed by other recurring ASI revenue. As validator participation scales via our Governance Protocols, our data portfolio increases, and our marginal costs will trend to zero.

We provide risk intelligence, not investment advice. Scores are probabilistic, transparent, and accompanied by methodology disclosures. Similar to credit ratings or ESG data, the platform informs decisions rather than replaces judgment.

We do not publish public rankings. Access is restricted to paying institutional clients, and companies are evaluated on reproducible, objective execution metrics. This significantly reduces defamation and reputational risk.

The “Black Box” is our core data processing pipeline built on FastAPI and PostgreSQL. It processes raw event logs through four stages:

1. Ingest: Captures `event_stream.json` from our OpenHands sensors.
2. Cleanse: Normalizes inconsistent LLM outputs and timestamps.
3. Lineage: Assigns a cryptographic SHA-256 hash to every log for auditability.
4. Calculate: Applies our proprietary risk formulas (Net Burn, Vaporware Probability) to generate the final “Golden Record.”

We use a Cryptographic Lineage system. Every metric in our database is linked back to the original raw log file via a SHA-256 hash.

This means if a “Safety Score” looks suspicious, you can click through to audit the exact raw JSON log file that generated that score. The `data_lineage` table in our schema ensures that no metric exists without a proven source.

These are our two primary stress-test methodologies:

• Vibe Coding (Economic Stress): We give the model ambiguous coding tasks (e.g., “Make this better”) to measure loop counts. High loops with low output quality indicate “thrashing”—a key indicator of high burn rate and low intelligence.
• Jailbreak (Safety Stress): We attempt to bypass safety guardrails using adversarial prompts. This measures the robustness of the model’s safety alignment under attack.

Net Burn measures the economic viability of an AI performing a task. The formula is:

Efficiency = min(1.0, Expected_Loops / Actual_Loops)
Adjusted_Revenue = Base_Revenue * Efficiency
Net_Burn = Adjusted_Revenue - Total_Cost

If an agent takes 50 loops to do a 5-loop task, its efficiency drops to 10%, destroying its revenue potential. This metric exposes inefficient “brute force” AI models.

• Frontend: Next.js 14 (React) with Recharts/D3 for visualization.
• Backend: Python 3.11 with FastAPI (async).
• Database: PostgreSQL 15+ (v2.0 Schema) with extensive JSONB support.
• Agents: Dockerized OpenHands runtime.
• Infrastructure: Docker Compose for orchestration, Redis for caching.

We validate signals through repeated agent runs, benchmarked task outcomes, and unit-economics consistency across time. Scores are not opinion-based—they are derived from ground-truth execution data (cost, success rate, alignment drift). As the dataset grows, we continuously backtest signals against post-investment outcomes to improve predictive accuracy.

Gaming increases operational cost and reduces real-world performance, which shows up immediately in Net Burn, behavioral variance, and alignment drift. Because signals are derived from live execution—not static disclosures—optimization attempts are detectable and penalized.