Internal knowledge-base AI search
Organisational knowledge lives in five to ten different systems. Staff spend 30 to 60 minutes a day searching for information that already exists somewhere in the stack.
AI search across your internal systems (Confluence, SharePoint, Slack, tickets, email) that respects permissions and cites every source in every answer.
- 01
Connectors ingest content from your internal systems, respecting the original permission model.
- 02
Every answer is grounded in retrieved documents and cites the source path.
- 03
Conflicts between sources (stale policy vs current policy) are surfaced, not hidden.
- 04
Usage analytics show which content is most consulted and which systems hold the answers.
- 05
Content owners get prompts when their docs are retrieved so they can keep them current.
20 to 40 minutes per person per day; 15 to 30 percent faster onboarding.
Ranges drawn from production deployments and public enterprise benchmarks. For a specific rupee or dollar figure tailored to your volume, use the calculator below.
Prerequisites for a clean deployment.
- Connector access to your source systems
- A respected permission model across those systems
- A source-of-truth hierarchy for overlapping content
- Content owners named per system
Put your own numbers on it.
“At 200 briefs a month and a loaded monthly cost of ₹1,50,000 per person, internal knowledge-base ai search would typically save ₹5.6 L to ₹7.3 L a year.”
Range uses this use case’s typical automation rate against the baseline time per task for research work, with your cost per person converted at 160 working hours a month.
More in Operations
All use casesInvoice processing and AP automation
Accounts payable teams spend most of their month on the same repetitive work: pulling data from invoices, matching to purchase orders, routing for approval, and chasing exceptions.
Supply-chain document reconciliation
Purchase orders, goods receipts and invoices routinely disagree. Payment delays, supplier disputes and reconciliation work consume operations time every month.
AI opportunity prioritisation
Organisations run too many AI pilots in parallel. Few reach production. There is no shared logic for deciding which bets to fund, which to park, and which to kill.