Services/Generative AI
Enterprise RAG
Retrieval-augmented systems that answer questions from your documents and data, with citations and access control.
- 8 to 12 weeks
- Fixed price
- Retrieval engineers with a data engineer and a frontend engineer
- Organisations with large internal documentation and slow answers
What we do
We turn scattered documents, wikis, and databases into a system your teams can query in plain language and trust. Answers carry sources, respect existing permissions, and are evaluated against a fixed question set before and after every change.
What the work includes
Ingestion across your sources
PDFs, Office documents, Confluence, Notion, SharePoint, and databases, parsed with structure preserved and metadata attached.
Hybrid retrieval
Keyword and semantic search combined and re-ranked, so exact terms and loosely phrased questions both find the right passage.
Answers with sources
Every answer cites the passages it came from, and the system says so when it cannot find one.
Permissions respected
Users only see answers from documents they are allowed to read, with audit logging for compliance.
In production
Vector store and knowledge graph inside an enterprise data platform
Retrieval infrastructure delivered as part of a 15-subsystem platform, serving agents and analysts across a supply-chain pilot.
See our enterprise experienceHow an engagement runs
Scope, build, evaluate, operate.
The same four stages on every engagement, each with a defined output.
Scope
A short discovery with the people who own the problem. Output: a written scope, acceptance criteria, and a fixed estimate.
Build
A named lead and a team sized to the scope. Working software from the first weeks, demonstrated on a fixed cadence.
Evaluate
Every AI component is measured against real cases before rollout. The numbers decide when it goes live.
Operate
Deployment, monitoring, and a support window. Then a handover, or an ongoing team if you want one.
Typical stack
Tools we commonly use for this work. The final choice follows your requirements, region, and existing platform.
- Python
- LangChain
- OpenAI
- Anthropic
- Weaviate
- Qdrant
- Elasticsearch
- Neo4j
- PostgreSQL
- FastAPI
Related use cases
Related services.
Agentic AI
Systems that take an objective, plan the steps, use tools, and complete multi-step work with minimal supervision.
Conversational AI
Assistants that hold context, answer from your knowledge, and hand off to a person at the right moment.
AI-Powered Search
Search that understands intent, not only keywords, across large content libraries and product catalogues.
Knowledge
Institutional knowledge, answerable
Retrieval quality and access control decide whether a RAG system is useful. That is where the engineering time goes.