- 16,000
- Documents ingested per day, on average
- 4
- Source classes aggregated
Context
The intelligence a company needs about its own market is rarely in one place. This client held it across vetted news feeds, third-party consulting reports, internal analysis, and the bid material moving through the business, with a handful of subject-matter experts who knew where the good sources were. Every question worth asking crossed at least two of those, and answering one meant waiting on someone's time.
Constraints
Not everyone may see everything. Procurement, supply chain, and the bid teams work from overlapping material with different entitlements, so an assistant that answered from the whole corpus would leak by design rather than by accident. The volume was the other half: RFPs, technical volumes, and cost volumes arrive faster than any curation step could keep up with.
RegainFlow's role
RegainFlow architected and built the platform end to end — the ingestion path, the retrieval layer, the entitlement model, the per-team assistants, and the report generation on top of them.
What RegainFlow engineered
- An ingestion path sized for the real arrival rate, averaging 16,000 documents a day across news, third-party research, internal reporting, and bid material.
- Structure-aware handling of RFPs and their technical and cost volumes, so a document that is really twenty documents is treated as twenty.
- An entitlement model enforced at retrieval, so a team's assistant can only reach material that team is cleared for and the boundary does not depend on prompt wording.
- Assistants tailored per function, from supply chain to procurement, each carrying the vocabulary and the sources its team actually works from.
- Report generation for executives and for subject-matter experts, drawn from the same aggregate rather than assembled by hand.
- Memory that carries a user's context between sessions, and adapts to the corrections they make.
- A feedback path where a correction changes later answers instead of being logged and forgotten.
Selected artifacts
Reconstructed, not screenshotted.
One corpus, four answers
- Aggregate corpus
- Entitlement filter
- Team assistant
- Cited answer
What the client received
- A high-volume ingestion path across four classes of source
- Entitlement-aware retrieval enforced below the assistant
- Per-team assistants for supply chain, procurement, and bid work
- Executive and subject-matter-expert report generation
- Per-user memory and a feedback loop that changes later answers
Outcome
Each team asks the whole aggregate and gets answers built only from what it is entitled to see. The research that once required a subject-matter expert's attention now starts from a generated draft, and the experts spend their time on the judgment rather than the assembly.
It learns from the people using it
The system improves as it is corrected. Feedback from each team changes what later answers look like for that team, so the platform gets more useful the longer it runs rather than drifting out of date.