AI knowledge base
A single question box across documents, wikis and FAQs, answering with citations and links to sources.
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Retrieval-Augmented Generation
Your company knowledge sits in PDFs, drives, wikis, tickets and databases. We build RAG systems that search all of it and answer questions in plain language, citing the exact source, so people stop hunting for information.
What this covers
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
Who it is for
Good RAG is mostly engineering: clean ingestion, smart chunking, hybrid search, re-ranking and permissions that mirror your existing access rules. We build and evaluate each layer so answers stay accurate as your content grows.
Discuss your requirementEnterprises with large policy, SOP and product documentation
Support teams that need instant answers from manuals and past tickets
Legal, compliance and finance teams searching contracts and circulars
SaaS companies adding "chat with your data" features
Capabilities
A single question box across documents, wikis and FAQs, answering with citations and links to sources.
Semantic plus keyword search that understands intent, synonyms and Hindi-English queries.
Embeddings stored in pgvector, Qdrant, Pinecone or Weaviate with metadata filters and fast retrieval.
Ingestion, chunking, re-ranking and answer generation pipelines built on proven frameworks.
Users only see answers from documents they are allowed to access, enforced at query time.
Automated accuracy tests and scheduled re-indexing so answers stay right as documents change.
How we work
You always know what happens next, who is responsible and what you will receive at each stage.
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
We list content sources, formats, owners and access rules.
Documents are parsed, cleaned, chunked and embedded.
Hybrid search and re-ranking are tuned on real questions.
Prompting, citations and refusal rules are implemented.
Accuracy is measured on a question bank before go-live.
Deliverables
Technology & standards
We recommend tools based on your scale, budget and existing systems, not on what is fashionable. Every choice is explained in the proposal.
Engagement models
A 3–4 week pilot on your own data with agreed accuracy targets, so you see real results before scaling.
Hardened integration, guardrails, monitoring and admin controls, delivered in milestones with a fixed quote.
Ongoing prompt and model tuning, evaluation runs, cost monitoring and feature additions on a monthly plan.
How pricing works: AI projects are priced in two stages: a fixed-price proof of concept, then a production quote based on what the pilot proves. Model and API usage costs are estimated upfront and billed at actuals.
Get a quoteFAQs
Retrieval-augmented generation first finds the relevant passages in your own documents and then asks the model to answer only from them. Public ChatGPT does not know your internal content and cannot cite it; RAG does, and it keeps data under your control.
PDFs, Word and Excel files, scanned documents (via OCR), web pages, Confluence or Notion wikis, Google Drive, SharePoint, helpdesk tickets and database records are all supported.
Answers are generated only from retrieved passages, each answer cites its sources, and the system says it does not know when the documents do not cover a question. We measure this on a test set before launch.
No. Retrieval respects the permissions of the source systems or the roles you define, so each user only gets answers from content they are allowed to read.
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