CircaOS

The substrate behind 5CEOs. Available standalone.

🔒 Verifiable security, not vendor-attested. Every /v1 response carries an HMAC tamper signature and an Ed25519 attestation receipt binding the request, the response, the running build, and our audit chain head. Customer authentication can be Ed25519 keypair-based — you hold the private key, we hold only the public. Each customer's audit log is hash-chained per-(tenant, app), and the content of every row is encrypted to your X25519 public key at write time — we can't decrypt the rows we just wrote. Server-side HMAC secrets and the attestation signing key sit at rest as AES-256-GCM ciphertext under a Key-Vault-resolved DEK — a stolen disk yields ciphertext only. Customers self-rotate via POST /v1/keys/rotate with a 24-hour grace window, and a leaked key auto-quarantines the moment a scanner pattern fires from the same IP. Container images are cosign-signed, the runtime is distroless with no shell and no admin endpoint on the internet, and a Container App Job runs continuous probes against this domain daily. See /trust → · verify a signature →

→ Run the 90-second proof. Copy-paste code that proves determinism, schema-locking, and cost on your own machine. Open the demo →

🎁 Try free, no card. 100 requests/day on Tier B (3B the substrate, schema-locked, signed responses). One curl to get a key.

Get my key

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Or browse the cookbook. Six archetypal patterns — extraction, classification, routing, scoring, agent step, multi-extract. Copy-paste recipes →

Or read the dev-honest version. The technical whitepaper — mechanism, bench methodology, cost math, and the explicit list of things CircaOS does not do. ~15 min read.

Most AI failures don't come from your code — they come from drift, retries, malformed JSON, and silent provider changes that break features without warning. But the deeper cost is bigger than debugging. When a company can't get a truthful, stable view of its own operations, everyone pays for it: higher prices, slower products, wasted compute, wasted labor, and decisions made on bad data.

This loop removes that waste. Reproducible LLM calls with no drift, no retry storms, and no malformed output because schema-locked decoding makes invalid JSON physically impossible. Same call → same bytes out. Same call next month → same bytes out. Same call under load → no throttles, no surprises.

The result is 78% less inference spend, 72% less carbon, and a business that finally sees what's actually happening instead of guessing.

curl https://cogos.5ceos.com/v1/chat/completions \
  -H "Authorization: Bearer sk-cogos-..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "cogos-tier-b",
    "messages": [{"role":"user","content":"Capital of France?"}],
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "name": "answer",
        "strict": true,
        "schema": {
          "type": "object",
          "required": ["country","capital"],
          "properties": {
            "country": {"type":"string"},
            "capital": {"type":"string"}
          }
        }
      }
    }
  }'