AI & Agentic AI

Enterprise RAG and knowledge intelligence

Answers drawn from your own documents, with citations — and honest silence when the answer is not there.

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Rack-mounted servers with blue status lights

Retrieval-augmented generation is the most reliable way to make a language model useful on private knowledge. It is also where most implementations quietly fail — not in the model, but in retrieval. If the right passage is never fetched, no amount of prompting recovers it.

We treat retrieval as the engineering problem it is: how documents are chunked, how hybrid keyword-and-vector search is tuned, how permissions follow the user rather than the index, how freshness is maintained, and how the system behaves when it genuinely does not know.

Permission-awareness is not optional. A knowledge system that answers from documents the asker could not otherwise open is a data breach with a friendly interface.

What you get

Answers with sources, not unverifiable assertions
Retrieval quality that is measured rather than assumed
Access control preserved end to end

What's included

Ingestion and chunking

Connectors for document stores, intranets, ticketing systems and databases, with structure-aware chunking rather than fixed windows.

Hybrid retrieval and reranking

Vector similarity combined with keyword search and a reranking pass, tuned against a labelled question set.

Permission-aware access

Retrieval filtered by the requesting user's entitlements, enforced at query time and not merely at ingest.

Citations and traceability

Every answer linked to its source passages so a reader can verify rather than trust.

Retrieval evaluation

Recall and precision measured on a golden question set, so tuning is evidence-led and regressions are visible.

Freshness and lifecycle

Incremental re-indexing, deletion propagation, and versioning when source documents change.

Where this applies

Expertise concentrated in a few people who are constantly interrupted
Policies and procedures spread across years of documents
Support agents searching several systems to answer one question
Onboarding that takes months because nothing is findable

Technologies

Pinecone
Weaviate
ChromaDB
Milvus
LangChain
Hugging Face
Python

How it works: the retrieval pipeline

01
Ingest
Connectors pull from document stores, intranets and ticketing systems.
02
Chunk
Structure-aware splitting that respects headings, tables and clauses.
03
Index
Embeddings plus keyword index, with entitlements stored alongside.
04
Retrieve
Hybrid search filtered by the asking user's permissions.
05
Rerank
A second pass orders candidates by actual relevance.
06
Generate
The answer is composed only from retrieved passages.
07
Cite
Every claim linked to its source so a reader can verify it.
EVAL
Measured, continuously
Recall and precision tracked on a golden question set. Most RAG systems fail at retrieval, not generation — so this is where the work goes.

Permissions are enforced at query time, not only at ingest. A knowledge system that answers from documents the asker could not otherwise open is a data breach with a friendly interface.