The job is to close the operating loop.
Teams already create memory signals every day: meeting outputs, email threads, decisions, risks, assignments, roles, policies, and governance records. The problem is that those signals rarely become trusted, structured, searchable records. Sera closes that loop by preparing the operating record for review.
What a working AI operations layer includes.
Capture
Google Meet is native. Transcripts or summaries from other platforms can be emailed into the capture inbox.
Extraction
Sera turns source material into candidate decisions, tasks, risks, roles, policies, people, projects, and context.
Review
A human reviewer approves, corrects, or rejects candidates before they become trusted memory.
Storage
The Living Memory Hub is the default private backend, with custom data architecture scoped when needed.
Retrieval
Sera answers in plain English from reviewed records and cites sources.
Operations
Memory health, intake status, and review workload remain visible.
Your records should stay where your team can inspect them.
Saberra is designed around inspectability. Client records live in the client workspace by default. For teams that need Postgres, a different source of truth, or additional systems of record, that should be an explicit setup decision, not a hidden promise.
Your tools
Trusted memory path
This matters when memory loss has become operational risk.
The strongest fit is a team with repeated decisions, role or staff transitions, governance complexity, meeting-heavy operations, and at least one person who can own memory review for about 1-2 hours per week.