Internal Knowledge Search
The answer exists — in a policy PDF, a past project, or the head of whoever's been here longest. Everyone loses minutes per question; the organization loses the compounding.
What can be automated in internal knowledge search? With current AI and integration tooling: plain-language q&a over your documents with citations; retrieval that respects existing permissions; summaries of long source material; gap detection (questions with no documented answer). What should remain under human control: trusting an answer for high-stakes decisions (citations exist to be checked); updating the underlying documents; anything client-facing built on retrieved content. Olyra implements this with confidence scoring, audit logging, and human-review gates matched to risk.
What this work looks like today
Everyone; acutely: new hires, support teams, and anyone client-facing.
Document stores, wikis, shared drives, DMS, past project folders, policy binders.
Measured before we touch anything: Time-to-answer · Deflection of expert interruptions · New-hire ramp time
What gets automated — and what stays human
Automated (with logging):
- Plain-language Q&A over your documents with citations
- Retrieval that respects existing permissions
- Summaries of long source material
- Gap detection (questions with no documented answer)
Stays under human control:
- Trusting an answer for high-stakes decisions (citations exist to be checked)
- Updating the underlying documents
- Anything client-facing built on retrieved content
How value and risk are managed
What we baseline and track
Time-to-answer · Deflection of expert interruptions · New-hire ramp time · Undocumented-answer rate
Risks designed for
Access control mirrored from source systems; confidentiality walls between clients/matters; retrieval logging.
Typical implementation path: One high-value corpus (SOPs or past deliverables) first; expand as trust builds. Implementation follows the Olyra 120: pilot on real historical volume, parallel run, then measured cutover.
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