Nightwatch provides proof when your AI is questioned.
Audit logs, data lineage, deletion receipts, and retention evidence — based on what your systems sent, stored, reused, and deleted across RAG, agents, logs, embeddings, and caches.
JSON / CSVThe moment this matters is when someone asks for proof.
Customers, regulators, or internal legal/security don’t want “trust us.” They want an exportable record.
“What did your AI see?”
Show what context was retrieved or injected into the prompt, with IDs and timestamps.
“Prove you deleted it.”
Produce deletion receipts tied to a request_id (not a promise in Slack).
“Show retention + access history.”
Evidence for what was stored, for how long, and what touched it.
Exports you can hand to a serious person.
Audit exports
Structured logs of memory events (write / retrieve / delete) that can be exported as JSON/CSV.
Lineage
Request ID ties together: prompt/context → retrieval trace → storage writes → later deletions.
Deletion receipts
Evidence that specific records/vectors were deleted, with job status and counts where possible.
Important: Nightwatch does not inspect model internals. It proves what your stack sent, stored, reused, and deleted.
Between your app and your memory stores.
App / agent
Write / retrieve
Nightwatch
Memory stores
Vector DB
SQL / logs / caches
Nightwatch is not a model replacement. It’s a proof layer around the memory surfaces you already run.
I’m running research conversations this week.
If you ship an AI product and you’ve dealt with audit requests, proof demands, or deletion obligations, I’d love to learn how you handle it.