Services
RAG & Knowledge AI Platforms
Retrieval-Augmented Generation converts your PDFs, wikis, SharePoint, and databases into an AI system that answers from verified sources with citations.
How it works
- 01Ingest and preprocess documents and structured sources
- 02Embed and index in a vector store suited to your security needs
- 03Hybrid retrieval (semantic + keyword) with reranking
- 04LLM synthesis with citations and evaluation loops
Timeline
- Weeks 1–2: Corpus audit, ACL mapping, and eval set design
- Weeks 3–5: Ingestion, hybrid retrieval, and citation UX
- Weeks 6–7: Groundedness evaluation and access-control tests
- Week 8+: Pilot with a department and production hardening
Risks & mitigations
- Stale or conflicting documents — mitigated with source ownership and freshness SLAs.
- Hallucinated answers without retrieval — mitigated with refuse-when-empty policies.
- Over-broad access — mitigated by mirroring document ACLs in retrieval.
Outcomes
- Employees get instant answers from SOPs and policies
- Reduced hallucination risk on company facts
- Data residency options including India-based hosting
Frequently asked questions
What is RAG?+
Retrieval-Augmented Generation searches your knowledge base before answering, so the model responds from your documents instead of inventing facts.
Where is our data stored?+
We support data localization strategies, including India-based servers, aligned with your compliance requirements.
Which document sources can you ingest?+
PDFs, wikis, SharePoint, Confluence, shared drives, and selected databases — prioritized by freshness and authority.
How do citations work?+
Answers link back to retrieved chunks so users can open the source policy, SOP, or page that grounded the reply.
How do you evaluate groundedness?+
We run golden-question sets scoring retrieval hit rate, citation validity, and answer faithfulness before and after releases.
Can access control mirror our permissions?+
Yes. Retrieval can respect document ACLs so users only see answers from sources they are allowed to read.
How often is the index refreshed?+
Schedules range from near-real-time connectors to nightly batches depending on how often your corpus changes.
See this solution in your stack
We’ll map integrations, controls, and a pilot path for your team.