Capability
Knowledge-source preparation
Inventory, clean, segment, and permission the material the retrieval system is allowed to use.
AI Engineering / RAG / Retrieval Systems
Help AI answer from the information your business trusts.
Turn approved documents and system knowledge into grounded answers with useful sources, permissions, and quality measurement.
Discuss this serviceThe business problem
What brings this work into focus.
Important knowledge is scattered across documents, systems, and teams, while a general model has no dependable view of what is current for the organization. Users need answers grounded in approved sources and a way to inspect where those answers came from.
System delivery
Define the boundary, build the capability, and prepare it for real use.
Define the system
Build in working slices
Measure and operate
Capability
Retrieval and ranking
Build search, embedding, filtering, reranking, and citation flows around the questions users ask.
Capability
Grounded-answer evaluation
Measure retrieval coverage, source quality, answer support, and failure patterns on representative queries.
Working outputs
What the engagement produces.
- Searchable knowledge pipeline
- Retrieval API or answer experience with sources
- Evaluation set and content-refresh procedure
Fit guidance
Use the approach that matches the constraint.
This is useful when
Strong for internal knowledge, support content, policy reference, research, or product help that must stay aligned with changing source material.
A simpler path may be better when
Improve conventional search or organize the underlying content when users mainly need navigation rather than generated answers.
Related offerings
The adjacent work that often matters.
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Talk with us about rag / retrieval systems.
We will help you identify the useful first move and say plainly when a simpler option is the better answer.