AI Observability

Every AI call: traced, audited, exportable.

Per-call traces. Cost rollup by tenant + solution. Drift detection. Compliance-grade exports for DPDP, HIPAA, GDPR.

Why generic AI stacks fail audit

Most LLM stacks log nothing. When a customer asks 'why did your AI tell me X?', you have no answer.

Token spend triples in a month. Without per-tenant + per-solution attribution, you can't even invoice it back.

DPDP Section 16 + HIPAA + GDPR all demand audit-grade logs. CSV-export of every AI call is non-negotiable.

Prompt, model, tokens-in, tokens-out, latency, cost, output, trust score, redactions — every call, forever (DPDP Section 16 retention configurable).

Per-tenant, per-solution, per-feature, per-day. Forecast next month's spend; alert on anomaly.

Trust Score distribution shifts over time? Alert. Fact-check failure rate climbing? Alert. Catch model regressions before customers do.

One-click CSV / Parquet / JSON export for any date range. Pre-formatted for DPDP Section 16, HIPAA Security Rule, GDPR Article 30.

Tenant brings their own OpenAI/Anthropic/Bedrock key — observability still works. We trace via signed proxy; their key, our audit.

Built so DPDP, HIPAA, and GDPR auditors can sign off without you scrambling.