AI Development
RAG Knowledge Assistant Development
Ask questions of your own documents and get sourced answers.
- Typical timeline
- 4-8 weeks depending on document volume and formats
- Category
- AI Development
- Engagement
- Fixed-scope phases
- You receive
- Code, data & documentation
Overview
Retrieval-augmented generation connects a language model to your own documents, so answers come from your manuals, policies and contracts rather than from the model's general knowledge. Crucially, every answer can cite the passage it came from, which is what makes it usable for real work.
The answer exists in a document nobody can find
Policies, product manuals, contracts, technical specifications and standard procedures accumulate across shared drives and inboxes. Staff either interrupt a senior colleague or guess. Both are expensive, and the guessing is the more expensive of the two.
You will recognise this if
- New staff take months to become productive because knowledge is undocumented or unfindable
- Experienced employees are interrupted constantly for answers already written down
- Different teams apply different versions of the same policy
- Searching the shared drive returns a hundred files and no answer
What you get out of it
The difference this makes
Answers with sources attached
Every reply links to the document and passage it came from, so staff can verify before acting — the difference between a tool people trust and one they ignore.
Institutional knowledge stays put
Expertise captured in documents remains accessible when the person who wrote them moves on.
Faster onboarding
New employees ask the assistant the questions they would otherwise hesitate to ask a colleague, and get consistent answers.
Access respected
Document permissions carry through, so the assistant never surfaces a passage from a file the person asking is not allowed to read.
What we build
- Ingestion of PDF, Word, Excel, PowerPoint, HTML and scanned documents with OCR
- Semantic chunking that keeps tables, clauses and sections intact
- Hybrid retrieval combining keyword and vector search for better recall
- Citations linking each answer to its source passage and page
- Permission-aware retrieval honouring per-document access rules
- Conversational follow-up that keeps context across questions
- Admin dashboard for ingestion status, unanswered questions and content gaps
- Scheduled re-indexing as documents change
How it gets used
A manufacturer with decades of technical documentation
Service engineers query machine manuals and past fault reports from a phone on the shop floor and get the relevant page rather than a file list.
A professional services firm managing contracts
Staff ask what a specific client agreement says about notice periods or liability and receive the clause itself, with a link to the document.
An institution handling policy questions
Administrative staff answer fee, admission and refund questions consistently by querying the current policy documents rather than recalling last year's rules.
Technology we typically use
You do not need to care about this list — but your next developer will.
- Claude API
- pgvector / PostgreSQL
- Embedding models
- Python with FastAPI
- OCR (Tesseract / cloud OCR)
- Next.js
Timeline: Typically 4-8 weeks depending on document volume and formats
Questions
RAG Knowledge Assistant Development — common questions
Something else on your mind? Ask us directly — it is usually faster than reading.
Are our documents used to train an AI model?
How many documents can it handle?
Can it read scanned documents?
Can it be hosted entirely within our own infrastructure?
Related services
Businesses that need this often need one of these as well.
Thinking about rag knowledge assistant development?
Tell us about your situation. We will tell you whether this is the right service, what it would involve, and roughly what it would cost.