Ready-to-deploy AI solution
AI Knowledge Base with Cited Answers from Your Documents
A pre-built retrieval-augmented assistant that lets staff ask questions in plain language and get answers drawn from your own documents, with sources and access control.
Overview
Why AI Knowledge Base matters
An AI knowledge base turns the documents your organization already owns into answers your people can use in seconds. Policies, SOPs, manuals, contracts and project files are usually spread across shared drives, wikis and inboxes. Staff either search for twenty minutes or ask a colleague, and the same questions are answered again and again by your most experienced people.
We deploy a pre-built retrieval-augmented generation (RAG) engine and connect it to your document sources. Every answer is grounded in your content and shows citations to the exact pages it used, so users can verify before acting. Access rules mirror your existing permissions, which means a staff member only ever sees answers from documents they are allowed to open.
What we build
AI Knowledge Base: capabilities
Every feature is built on our standardized stack, so it is secure, tested and maintainable by any engineer in our pool.
Retrieval-augmented answers
Questions are matched against your indexed documents first, and the model writes its answer only from the passages it retrieves.
Citations on every answer
Each response links to the source document, section and page, so users can open the original and check the context in one click.
Permission-aware access control
Document-level and folder-level permissions are respected at query time, so confidential HR, legal or finance files stay restricted.
Connectors to your document stores
Index content from Google Drive, SharePoint, Confluence, network file shares, PDFs and scanned documents with scheduled re-syncing.
Handles messy real-world files
Scanned PDFs, tables, slide decks and spreadsheets are parsed with OCR and layout-aware chunking so their content is searchable.
Conversation follow-ups
Users can ask clarifying questions in the same thread, and the assistant keeps context without losing track of its sources.
Answer quality feedback
Thumbs-up and thumbs-down ratings with comments are collected, giving document owners a clear list of gaps and outdated content.
Admin and audit console
Administrators see which sources are indexed, when they were last updated, which questions are asked most and who accessed what.
Key benefits
Outcomes your business can count on
Less time searching
Employees get a direct answer with its source instead of opening dozens of files or waiting for a colleague to reply.
Answers you can verify
Citations make it easy to confirm the source, which builds trust and keeps people accountable for decisions they make.
Faster onboarding
New joiners can learn processes by asking questions in their own words rather than reading a manual cover to cover.
Expertise that stays with the business
Know-how captured in documents remains accessible when experienced staff are busy, on leave or have moved on.
Use cases
Where this delivers value
Clinical and hospital protocols
Give doctors and nurses fast access to treatment guidelines, drug information and internal protocols, with links back to the approved source.
HR and policy helpdesk
Answer leave, reimbursement and code-of-conduct questions for employees from the current policy handbook, with sensitive files restricted by role.
Engineering and maintenance manuals
Let field technicians search equipment manuals and past service reports from a phone or tablet while standing at the machine.
Legal and compliance research
Help compliance teams locate relevant clauses across contracts, regulations and internal guidance, with citations for every finding.
Technology used
Built on one proven stack
The knowledge base uses an ingestion pipeline for parsing and chunking, an embedding model and vector index for retrieval, a re-ranking step for precision and a language model that writes the cited answer. A NestJS backend enforces permissions and logging.
- LlamaIndex and LangChain
- Pinecone or Weaviate vector search
- OpenAI GPT, Claude or Gemini
- Llama, Mistral or DeepSeek via Ollama
- OCR and layout-aware document parsing
- Google Drive and SharePoint connectors
- Python, FastAPI and NestJS
Our process
From first call to confident launch
- 01
Discovery
We inventory your document sources, sample the formats and quality, gather the questions staff ask most and map the permission model the assistant must respect.
- 02
Design
We choose the chunking strategy, embedding model and hosting option, design the citation view and agree an evaluation set of real questions with correct answers.
- 03
Agile Build
We connect the pre-built RAG engine to your sources in stages, measure answer accuracy against the evaluation set each sprint and tune retrieval until it meets the target.
- 04
Production + 90-day hypercare
We roll out to a pilot group, then the wider organization. For 90 days we monitor feedback, fix ingestion issues and help document owners close content gaps.
How an AI knowledge base answers with confidence
A general chatbot answers from what it learned during training, which is why it can sound confident and still be wrong about your business. Retrieval-augmented generation works differently. When a question arrives, the system first searches your own documents for the most relevant passages, then asks the model to compose an answer using only those passages, and finally shows the sources alongside the reply.
If the documents do not contain an answer, the assistant is configured to say so rather than fill the gap with a guess. That single behavior is what makes a knowledge assistant safe to use for policies, procedures and regulated work.
Access control that mirrors your organization
Indexing everything into one search engine creates a risk: a junior employee could ask about salaries and receive an answer from a confidential spreadsheet. We prevent this by storing the permissions of every document alongside its content and filtering results by the user's identity before the model ever sees them.
For organizations with strict confidentiality requirements, the full pipeline, including the language model, can run on your own servers. Our local LLM deployment option keeps documents, embeddings and queries inside your network so your data never leaves your infrastructure.
- Single sign-on with your existing identity provider
- Role and group-based filtering at query time
- Full audit log of questions, sources and users
From pilot to organization-wide rollout
We have used this approach for a clinical knowledge assistant for a hospital network, where accuracy and source traceability were non-negotiable. The same engine can sit behind your intranet, inside Microsoft Teams or Slack, or power a customer-facing assistant such as our AI customer support solution.
Industries served
Proven across industries
FAQ
AI Knowledge Base: frequently asked questions
Put your documents to work
Share a representative set of documents and twenty real questions, and we will show you cited answers before you commit.