Your Organisation’s Knowledge, Made Instantly Searchable
HR manuals, legal contracts, technical guides, compliance frameworks — your organisation has years of institutional knowledge locked in documents nobody can find. We build internal AI assistants that make it instantly retrievable, with citations and GDPR compliance built in.
Unlock siloed company knowledge across every department
Internal knowledge assistants deliver the highest ROI when deployed on document-heavy workflows. Here are the four most common implementations.
HR policy & procedure assistant
Employees ask questions about leave entitlements, expense policies, or onboarding requirements in plain language — and receive an accurate, cited answer from the latest HR manual. HR teams stop answering the same 30 questions repeatedly.
Legal contract intelligence
Legal and procurement teams search thousands of contracts for specific clauses, renewal dates, or liability limits in seconds. The assistant points to the exact clause and document source.
Technical documentation retrieval
Engineers query manuals, specifications, and product documentation in natural language. Cuts research time from hours to seconds on complex technical questions.
Regulatory compliance Q&A
Compliance teams ask questions about applicable regulations, internal policies, and audit requirements — with full source attribution for every answer.
How a RAG knowledge assistant works
Retrieval-Augmented Generation (RAG) grounds the AI in your actual documents. The model never invents answers — it retrieves and summarises from verified sources.
Document ingestion
Your documents (PDFs, Word, SharePoint, databases) are ingested, chunked intelligently, and stored as vector embeddings in Azure AI Search.
Query processing
When a user asks a question, it is converted to an embedding vector and matched against your document store using semantic similarity search.
Context assembly
The top-matching document chunks, along with the user question, are assembled into a prompt. Hybrid search (vector + keyword) catches both semantic intent and exact matches.
Grounded generation
Azure OpenAI generates an answer strictly from the retrieved context. Hallucinations are structurally prevented because the model is constrained to the provided source material.
Source citation
Every response includes traceable citations — the document name, section, and page where the answer was found. Compliance teams can verify any response.
How we select the right embedding model for your workload
Every RAG system requires an embedding model to convert text into vector representations. The choice materially affects accuracy, latency, and operating cost. We evaluate the options openly with every client before making a recommendation.
For a recent client with a 50,000-document technical library requiring daily updates and sub-second query latency, we evaluated three OpenAI embedding models before recommending text-embedding-3-small. Here is why:
| Model | Dimensions | Cost / 1M tokens | Strength | Trade-off | |
|---|---|---|---|---|---|
| text-embedding-ada-002 | 1,536 | €0.10 | Good general purpose | Larger index size | |
| text-embedding-3-small | 1,536 (configurable) | €0.02 | High accuracy at low cost | Smaller community vs ada-002 | Our recommendation |
| text-embedding-3-large | 3,072 | €0.13 | Highest accuracy | 6.5× the cost of 3-small |
Why text-embedding-3-small for this client: At 50,000 documents with daily ingestion runs, the 5× cost advantage over text-embedding-3-large reduced ongoing operating cost by ~€280/month with no measurable accuracy loss on domain-specific technical terminology. The configurable dimension size allowed us to reduce the Azure AI Search index by 40%, cutting infrastructure cost further. This decision alone paid for the IITS engagement within three months.
See a working demo on your documents
Share a sample document set and we will build a working prototype in 48 hours — so you can see exactly what the assistant can and cannot answer before committing.
Request a Prototype Demo