We make AI
trustworthy enough
to do real work

Nora is a platform for building AI agents you can trust in production. Agents run on Nora, and every production answer is recorded as a trace. When an answer is wrong, Nora turns it into a signal, helps diagnose the cause, tests candidate fixes in simulation, ships approved changes as versions, and keeps the fix in memory so the same mistake does not keep coming back.

The difference Nora's docs emphasize is that Nora does not stop at showing traces. It collects failure candidates as signals, lets a human confirm or reject them, groups confirmed failures into clusters and cases, runs simulations across candidate fixes, compares cost, accuracy, reliability, and regression risk, then deploys only approved improvements as versions. The product is centered on the full improvement loop: detect, diagnose, experiment, publish, and remember.

If a customer support agent gives the wrong refund-policy answer, the run is saved as a trace and an answer that conflicts with its sources can become a signal in the queue. After a reviewer confirms the failure, the input and wrong answer become a case, and similar failures are grouped into a cluster. Nora can then run an experiment against that cluster, compare prompt, guardrail, memory, or retrieval changes, and show which cases improved or regressed. A human publishes the candidate only after reviewing the results.

Nora's docs describe data access through workspace permissions and scoped connectors. External services such as Google Drive or Slack are connected with saved credentials; credentials are encrypted at rest and never shown again. Foundry's Clean stage can redact PII such as emails, phone numbers, credit cards, and national IDs before content reaches the index. Role permissions limit screens and sensitive actions, while signal decisions, deploys, guardrail blocks, and member changes are recorded in Audit.