DeepEval is an open-source framework that unit-tests LLM outputs against metrics. BRIDGE dispatches your content to 6 independent models that debate each other and returns a corrected document — not a test report.
Test assertions catch known failure modes. Adversarial debate catches what you did not think to test for.
| Capability | BRIDGE | DeepEval |
|---|---|---|
| Adversarial Consensus | 6+ models debate and resolve disagreements | Assert-based evaluation metrics |
| Multi-Model Verification | 6 models verify simultaneously | Tests one model at a time |
| Corrected Document Output | Returns verified, corrected documents | Returns pass/fail test results |
| Cryptographic Audit Trail | HMAC-signed PDF audit certificates | Test logs only |
| Format-Preserving | .py in, verified .py out | JSON test results |
| Adversarial Debate Rounds | Multi-round structured debate | No debate mechanism |
| MCP Tool Integration | 14 MCP tools for model participation | No MCP tools |
| Pricing Model | Pay-per-verification from $0.05 | Open-source (free) |
DeepEval checks if an output passes predefined metrics — hallucination, relevancy, toxicity. BRIDGE discovers problems you never wrote a test for. When 6 models argue about your content, they find risks that no test suite anticipated.
DeepEval tells you whether an output passed or failed. BRIDGE gives you the corrected output. Submit a contract, receive a verified contract. Submit code, receive verified code. The verification IS the deliverable.
DeepEval belongs in your CI pipeline. BRIDGE belongs in your production workflow. Every document, every contract, every report your AI generates can be verified by 6 independent models before it reaches a human — with cryptographic proof attached.
Submit your first document and get multi-model adversarial consensus in under 60 seconds. Format-preserving. Audit-certified.