Defend every AI decision before the regulator asks.

“Why is this your production default?” significance + IRR floor

Every production default — which prompt variant ships, which memory strategy lives, which provider is the baseline — passes (a) a statistical significance gate over a content-hashed fixture, then (b) an inter-rater reliability floor (Krippendorff α with bootstrap CI — the same measure clinical-trial reviewers use to prove humans agree above chance). The verdict, the confidence interval, the fixture hash, the DLP rule-set fingerprint that scrubbed it, the approving operator — all frozen on the audit row and shipped in the Evidence Pack. Your auditor reconstructs why this is the current default from the bundle alone. No meeting required. No engineer dragged in. Six years from now, same answer, same hash.

“Show me everything that happened, signed.” tamper-evident audit

Every AI decision your team makes — every prompt, every multi-agent run, every Cursor session — lands in a cryptographically chained log scoped per tenant, so cross-tenant tampering is structurally detectable. Export as a signed Evidence Pack — a standalone offline verifier ships in the bundle so your auditor reads it without backend access, without a Proofpane account, six years from now.

“Map this to my framework controls.” NIST · ISO 42001 · EU AI Act · GDPR · SOC 2

Control library aligned with NIST AI RMF, ISO/IEC 42001, and EU AI Act evidence expectations — pre-mapped per skill, with per-org overrides. A closed-set guard cross-checks every cited control ID against a curated truth set so fabricated references can't pass. Proofpane supports operational evidence; it does not replace legal, regulatory, or certification assessment.

“How do you stop costs running away — and catch quality drops?” cost-aware by design · 5 detection layers

Token budget control is the spine of the architecture, not a dashboard pasted on top — every call records token + latency + cost into the chain, and five layers catch cost-explosions before they become invoices: (1) a pre-call gate refuses LLM calls over per-org cap (refusal audited); (2) threshold alerts (50% / 80% / 100%) push to Slack + email before you hit cap; (3) per-call anomaly flag on any call > N× recent baseline; (4) month-end forecast projects current burn against cap so a 2-week overspend is visible 2 weeks early; (5) provider price-drift detection — a plausibility band catches silent per-token bumps from Anthropic / OpenAI. Quality runs the same way on a parallel track: closed-set hallucination guard against 259+ control IDs from NIST AI RMF / ISO 42001 / EU AI Act / GDPR / SOC 2, judge-grounded scoring, cross-vendor disagreement (3 providers vote), drift alerts on pass-rate drops. The /cost and /quality dashboards are the views; the design is the contract.

Want the full walkthrough? Watch the 1-min Slack + 3-min Salesforce demos →

“How do you improve safely?” human approves every change

Two reflection loops, same approval contract. The first watches the audit log for drift, hallucination, and low-score signals, and proposes prompt edits against the org's own failure cases. The second tracks curated AI-research feeds and auto-sandboxes proposed updates against production behaviour. In both cases only the changes a human approves ever go live.

“Show me the process, not just the outputs.” visual workflows · multi-agent · scheduled

Compose governance tasks, multi-agent primitives (consensus and adversarial review), and scheduled triggers on a visual canvas. An AI builder edits the graph for you. Every node execution writes a row into the same audit chain — the canvas is the planning view, the chain is the proof.