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Knowledge assistants that search your own documents, answer in plain language and cite the exact passage, so people can check the source rather than trust the model.
Retrieval pipelines built on your documents, tickets, wikis and databases — with hybrid search, reranking and citation enforcement so every answer traces back to a source.
Vector search finds passages that mean the same thing; keyword search catches part numbers, clause references and names. We run both and merge the results.
Documents are split along their real structure, such as sections, clauses and table rows, so a retrieved passage carries enough context to be understood alone.
The assistant must point to the passage behind every claim. If retrieval finds nothing relevant, it says so instead of filling the gap from memory.
Access rights from SharePoint, Drive or your identity provider are carried into the index, so people only ever see answers drawn from documents they can open.
Connectors pick up new, edited and deleted documents on a schedule, so the assistant reflects the current policy rather than last quarter’s version.
Questions in Hindi, Arabic or English can retrieve from documents written in another language, the same approach behind our Lingora assistant.
Most of the quality in a knowledge assistant comes before the model writes a word, so most of our time goes into ingestion and retrieval.
We inventory what you have, where it lives, who can see it and how current it is. Duplicate and outdated documents are flagged before anything is indexed.
How knowledge assistants shows up across India, the Gulf and the US. Pick one to see what changes in the process.
Branch staff phone a central team or search long circulars to confirm KYC and lending rules for unusual cases.
Staff ask in Hindi or English and get the relevant rule with a link to the exact circular and paragraph it came from.
Code, documentation and the tests that prove it — yours outright — and the limits it runs inside, agreed before anything goes live.
Each stage ends in something you can hold — a document, a demo, a passing eval, a dashboard. Nothing carries over on trust alone.
A paid two-week audit of your processes, data and systems. We come back with a ranked list of what AI should touch — and what it should not.
Audit report and ranked backlog
Model selection, retrieval design, guardrails and the integration surface. You get a written architecture with a cost model attached to it.
Architecture doc with cost model
Two-week sprints to implement agents, integrate with your systems and run internal evals. You see working software early and often.
A working demo in your environment
We run your real use cases, measure accuracy, latency and cost, and pressure-test edge cases with your team before go-live.
Evaluation report with KPIs
We help you launch, monitor and continuously improve. You get playbooks, dashboards and regular reviews to scale safely.
Live dashboards and runbooks
Still deciding?
Thirty minutes with an engineer who builds knowledge assistants. No deck, no discovery form.
Talk to an engineerGeneral tools work for a handful of files. With thousands of documents, retrieval quality decides whether answers are right, and you need permissions respected, superseded versions excluded and every answer tied to a source. Those are engineering problems in ingestion and search, which is where this work actually sits.
Most engagements combine two or three of these. Discovery is where we tell you which.
Agents that take a task, use your tools and finish it.
ExploreModels tuned and tested on your task, not a benchmark.
ExploreProcesses that run end to end, people only on exceptions.
ExploreGuardrails, tracing and access control around every model.
ExploreA costed plan of what to build, buy or leave alone.
ExploreBring one process you think an agent could run.
We'll tell you straight whether it's worth building — and what it would cost if it is.