Your AI is confidently wrong.
COS catches it.

Every AI predicts the next token. No AI can verify its own output. COS is the external layer that catches false certainty — with receipts.

Raw AI
Through COS
52.4%
Raw AI confirmed fabricated citations as real. Confident. Wrong.
0%
COS confidently wrong. Zero. Every uncertain case was flagged, not guessed.
Confirmed a fake citation as real Missed — gave uncertain answer Correctly identified Caught by COS Flagged as uncertain (honest)

The architectural gap no model update will close.

Every AI — GPT, Claude, Gemini, Mistral — is built on one method: predict the next token. That's powerful. It's also why no AI can know when it's wrong. Self-verification from inside a probabilistic system is a structural impossibility — not an engineering gap waiting for the next model update. COS is the external architecture that fills it. The layer your AI runs through before answers reach a human.

One layer. Four things.

Catches

False certainty, fabricated citations, contradictions, confident lies. Before production. Not after.

Refuses

When confidence is too low, COS says "I don't know" instead of guessing. Your AI will never learn this skill on its own. COS has it built in.

Receipts

Every validation is signed. Every flag is traceable. Audit-grade proof that checking happened. Not "trust us" — prove it to your regulator.

Forgets

COS can prove to a regulator that sensitive data was deleted from an LLM. GDPR, HIPAA, SOC2. The only product on the market that does this.

Numbers, not adjectives.

Metric Raw AI COS (Bamboo v0.2)
False certainty rate 52.4% 0%
Detection F1 97.6%
Heuristic check latency ~1ms
Deep validation latency ~3s
Integration effort One URL change

Production data. Bamboo v0.2, live since May 2026. Not projections.

One line of code. Not a sales pitch.

OpenAI Proxy
# Before — raw AI, no verification
from openai import OpenAI
client = OpenAI()

# After — COS verifies everything
from openai import OpenAI
client = OpenAI(base_url="https://cos.protofine.ai")

Same SDK. Same code. Every call now validated, receipted, returned in the same response format.

Python SDK
from cos_sdk import COS

cos = COS(api_key="cos_live_xxx")
result = cos.validate("Your AI output here")

Send us 1,000 prompts.
We'll show you what your AI is getting wrong.

On your data. Free. We run COS over up to 1,000 of your real outputs and show you exactly which ones are confidently wrong — claim by claim, with sources. We never store your data, and you get a signed receipt proving it. The report is the conversation. Need it to run inside your own network? An in-environment / VPC deployment is available for regulated buyers on request. If it's not useful, you owe nothing.

Building AI products?

Your users see AI output. You need every response validated before it ships. COS sits underneath — one URL change, full audit trail, zero code rewrite.

Platform docs →

Work in law, healthcare, or finance?

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