Artificial Intelligence
Enterprise Knowledge Systems and RAG
Retrieval-augmented generation over your documents, data and policies, with permission-aware search, cited answers and measurable retrieval quality.
Overview
Institutional knowledge lives in wikis, shared drives, ticket histories, contracts and databases, and finding the right answer often depends on knowing whom to ask. Retrieval-augmented generation lets a language model answer questions from that material while citing the sources it relied on.
The quality of a RAG system is determined largely by what happens before the model is called: how documents are parsed, chunked and enriched, how retrieval ranks them, and whether access permissions are enforced. That is where we concentrate our engineering effort, and where we measure results.
Business applications
- 01
Employee knowledge assistants
Give staff a single place to ask questions about policies, procedures, products and past projects, with answers grounded in current, approved documents and links to the exact passages used.
- 02
Support enablement
Help support agents find relevant knowledge articles, product documentation and similar resolved tickets, and draft responses that the agent reviews and edits before replying to the customer.
- 03
Regulatory and contract research
Search large collections of contracts, regulations and internal guidance, compare clauses or requirements across documents, and surface relevant passages for legal and compliance professionals to interpret.
- 04
Engineering and field knowledge
Make manuals, maintenance records, specifications and incident reports searchable in natural language, so technicians and engineers can find procedures and prior fixes on site or in the control room.
Engineering approach
- 01
Audit the knowledge sources
Inventory repositories, formats, owners, update frequency and access rules, and identify content that is outdated, duplicated or contradictory before any of it is indexed.
- 02
Engineer ingestion and indexing
Parse documents with layout awareness, chunk them along meaningful boundaries, attach metadata and permissions, and keep the index synchronised as source content changes.
- 03
Tune hybrid retrieval
Combine vector search with keyword search and metadata filters, add reranking and test query rewriting, so retrieval holds up for exact terms, codes and names as well as natural questions.
- 04
Generate grounded answers
Instruct the model to answer only from retrieved context, cite its sources, and say when the available material does not contain an answer rather than filling the gap.
- 05
Measure and maintain
Track retrieval precision, answer faithfulness and user feedback against a curated question set, and review failures regularly to improve content, chunking and ranking.
Technologies and platforms
Retrieval frameworks
- LlamaIndex
- LangChain
- Haystack
- Unstructured
- Docling
Vector and search stores
- pgvector
- Pinecone
- Weaviate
- Elasticsearch
- OpenSearch
- Azure AI Search
Embedding and reranking models
- OpenAI embeddings
- Cohere Embed and Rerank
- Voyage AI
- BGE models
- Gemini embeddings
Evaluation
- Ragas
- TruLens
- Langfuse
- promptfoo
Technology names are trademarks of their respective owners and do not imply partnership or endorsement.
What you receive
- Knowledge source inventory and content readiness report
- Ingestion pipelines with connectors, parsing and permission sync
- Hybrid search index with reranking and metadata filtering
- Question-answering application or API with cited responses
- Retrieval and answer quality evaluation suite
- Content governance guidelines for knowledge owners
Questions leaders ask
Why not simply upload our documents to a chatbot?
That works for small, low-risk collections. At enterprise scale you need permission enforcement, synchronisation with source systems, reliable handling of tables and scanned documents, and a way to measure whether answers are correct. Those requirements are what distinguish a production knowledge system from a convenient demonstration.
How do you make sure answers are accurate?
We cannot guarantee every answer, and we say so plainly. We reduce errors by improving retrieval, requiring citations and instructing the model to decline when evidence is missing. A curated evaluation set measures faithfulness and relevance over time, and the interface makes it easy for users to check sources and report problems.
Will users see documents they are not authorised to access?
They should not, and we design and test specifically to prevent it. Permissions from source systems such as SharePoint, Google Drive or Confluence are captured during ingestion and applied at query time, so retrieval returns only content the requesting user is entitled to see. These controls are verified explicitly during acceptance testing.
Related solutions
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