Enterprise knowledge search
Unify retrieval across approved knowledge collections. Preserve useful metadata and present relevant passages so employees can examine the underlying information.
RAG developmentYour knowledge.
Your knowledge.
In the right context.
Retrieval-augmented generation connects language models with relevant enterprise information. Build knowledge experiences around authorized content, traceable sources and evaluation you can inspect.
RAG developmentKnowledge is only useful
Knowledge is only useful
when it can be trusted.
Enterprise information lives across repositories, applications and document collections. Connecting a model to those sources takes more than creating a vector index. The system must retrieve the right context, respect access boundaries and show people where its answers came from.
Our RAG development approach treats ingestion, retrieval and generation as separate parts of one product. We examine source ownership, document structure, permission metadata and freshness requirements before selecting retrieval and orchestration patterns.
The aim is a practical knowledge experience that supports a defined task. We evaluate what the system finds as well as what it says, including how it behaves when relevant information is missing, conflicting or unavailable to the user.
Applications
Build around a meaningful task.
The examples below illustrate possible engagement areas. Each initiative is scoped to your organization’s requirements.
Source-grounded assistants
Generate answers using retrieved context with source references. Design clear behavior for insufficient evidence, conflicting guidance and questions outside the content’s scope.
Document & policy workflows
Help teams navigate product documentation, procedures and approved policies. Combine retrieval with structured extraction or review steps where the task requires them.
The engineering approachMake the foundations
Make the foundations
part of the product.
Prepare the knowledge layer
Assess documents, ownership, format and update frequency. Design parsing, chunking, metadata and ingestion checks around the structure of the content.
Design retrieval
Compare lexical, vector and hybrid search as appropriate. Examine query transformation, filtering and reranking against representative questions and the information they need.
Preserve authorization
Carry permission metadata into the retrieval design and enforce the user’s access at query time. Plan how changes, removals and revised permissions reach the index.
Evaluate the complete system
Test retrieval relevance separately from answer quality. Review citation support, missing-evidence behavior, freshness, response time and cost across representative tasks.
Define successMeasure what matters
Measure what matters
in your environment.
A useful evaluation asks whether the application can perform the task under realistic conditions. We help establish acceptance criteria and make trade-offs visible before a broader release.
Example application: internal policy navigation
An employee asks about a procedure. The system searches only the documents available to that employee, retrieves relevant passages and generates a response with citations. If two documents disagree or no approved source answers the question, the experience makes that limitation clear and points to the responsible team.
Illustrative application, not a client case study.
Retrieval relevance
Does the context include the information needed to answer the question?
Answer support
Do the cited passages actually support the generated claims?
Access behavior
Can the user retrieve only information they are authorized to see?
Freshness & coverage
Are important source changes reflected and are knowledge gaps visible?
The people behind the solutionAdd the expertise
Add the expertise
your team needs.
Already leading the initiative internally? We can focus the engagement on recruiting the specialists who complement your existing team.
Explore specialist staffing- RAG engineer
- Search engineer
- Data engineer
- LLM application engineer
- AI evaluation specialist
One initiative. The right engagement.
Discuss an individual placement, contract expertise or a scoped development effort. Responsibilities, deliverables and working arrangements are agreed before the engagement begins.
Common questions
Go deeper into RAG development.
Does RAG eliminate hallucinations?
No. Retrieval can provide relevant grounding but the model can still produce unsupported or misleading output. Evaluation, source inspection, constrained behavior and human review remain important design considerations.
Is RAG the same as fine-tuning?
No. RAG retrieves information at runtime to supply context. Fine-tuning changes model parameters using examples. They solve different problems and can be combined when the task justifies it.
How should document permissions be handled?
Permission metadata and identity need to be part of the retrieval architecture. Query-time authorization and a defined process for permission updates help keep restricted information out of unauthorized responses.
Can we begin with a limited document collection?
Yes. A focused collection and a well-defined set of user questions often provide a useful pilot scope. Success criteria should cover source quality, retrieval, answers and operating responsibilities.
Let’s define what useful AI looks like for you.
Start with a business task, a data challenge or a specialist role. We’ll help shape the next step.