What we build
What this service covers, in detail.
Permission-aware retrieval, citation-based answers and hybrid search across your organisation's knowledge.
- 01
Document processing
Parsing of PDFs, office files, web pages and tickets into clean text with structure, tables and metadata preserved.
- 02
Chunking and embedding
Chunking strategies tuned to your content, with embedding choices measured rather than assumed.
- 03
Vector databases
pgvector or dedicated engines, selected for scale, filtering needs and operational simplicity.
- 04
Hybrid retrieval
BM25 keyword matching combined with semantic search so exact terms and meaning are both respected.
- 05
Reranking
Cross-encoder or model rerankers that reorder candidates for relevance before the answer is written.
- 06
Citation-based answers
Every claim linked to the passage it came from, with the document opened at the right place.
- 07
Permission-aware retrieval
Access rules applied at query time so people only see content they are entitled to.
- 08
Knowledge graphs
Entities and relationships layered on top of text for questions that span many documents.
- 09
Evaluation and retrieval quality
Golden question sets, recall and precision tracking and regression tests on every pipeline change.
- 10
Agentic retrieval
Multi-step retrieval that plans follow-up searches across sources for complex questions, with every hop logged.
Example use cases
Where teams put this to work.
- 01
Internal policy assistant
Staff ask HR, finance or security questions and get answers tied to the current policy document.
- 02
Customer-facing help centre
A support assistant answering from product docs and escalating when it is not sure.
- 03
Contract and compliance search
Find clauses and obligations across thousands of agreements in seconds.
- 04
Engineering knowledge base
Search code, runbooks and incident history together.
Technical approach
Tools we typically use here.
A starting point, not a mandate. We adapt to your existing stack and constraints.
- Python
- FastAPI
- PostgreSQL + pgvector
- Elasticsearch / OpenSearch
- LangGraph
- Reranking models
- Unstructured parsers
How it works
The engagement, step by step.
- 1
Map sources and permissions
List content sources, owners, formats and who may see what.
- 2
Build the ingestion pipeline
Parse, clean, chunk and index with incremental updates.
- 3
Tune retrieval
Test chunking, embeddings, hybrid weights and rerankers against real questions.
- 4
Add the answer layer
Generate cited answers with refusal behaviour when evidence is weak.
- 5
Monitor and expand
Track quality and coverage, then add sources and features.
FAQ
Frequently asked questions
Why hybrid search instead of only vectors?
Vectors capture meaning but can miss exact identifiers or rare terms. Combining them with keyword search is more dependable for business content.
How do you stop leaks across teams?
Permissions are enforced during retrieval, before any text reaches the model, using the access rules of the source system.
What if the answer is not in the documents?
The assistant says so and offers related sources, rather than inventing an answer.
Projects
Reference projects that show this in practice.
Systems we designed as demonstrations, using sample data. Each has features, architecture and security notes.
Related capabilities
Often combined with
AI Engineering
Agentic systems, LLM-powered applications and enterprise assistants engineered with evaluation, security and oversight.
AI Automation & Developer Tools
AI coding assistants, workflow builders, research agents and automation with human approval.
Enterprise Software Development
CRM, ERP, HR, portals and operational systems designed around how your organisation works.
Planning a RAG & Enterprise Search project?
Tell us the problem you are solving. We will reply with questions, options and a realistic next step.