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RAG Systems

Help AI answer with context from your own knowledge.

Retrieval-augmented generation (RAG) combines language models with information retrieved from selected knowledge sources. We plan the content, access rules, retrieval behavior, and evaluation needed for a system suited to your use case.

Talk through this service See the delivery path

Outcomes to plan for

Useful results begin with a clear problem.

We shape this work with your team around real workflows, the systems already in place, and the outcomes the project needs to support.

01

Answers connected to selected sources

Retrieve relevant passages from the content you approve for the experience.

02

Knowledge that can be refreshed

Plan how source changes are indexed and how outdated or removed content is handled.

03

Behavior that can be evaluated

Define representative questions and review relevance, answer grounding, and cases that should be escalated.

Retrieval and grounding

Connect model responses to approved knowledge.

A retrieval-augmented system depends on the content it can access, how that content is indexed, and how retrieved evidence is presented. Plan permissions and evaluation alongside answer quality.

Retrieval scope and access controls should match the source systems and user permissions.

Illustrative grounded-answer flow

Step 01

Approved sources

Choose and prepare relevant documents

Step 02

Index and retrieve

Find context relevant to a user question

Step 03

Answer with evidence

Return grounded content and source references

Conceptual flow · retrieval quality and answer behavior require evaluation against your content.

Scope and delivery

A practical plan for RAG Systems.

The final scope depends on your users, systems, constraints, and the first useful release. Typical work can include:

Potential work areas

Defined to fit the agreed project scope

  • 01 Use-case and knowledge source assessment
  • 02 Content preparation, indexing, and retrieval design
  • 03 Prompt, response, and source reference experience
  • 04 Access control, evaluation, and update planning

Delivery path

From first questions to handover

The sequence is shaped around the requirements and review points agreed for this service.

  1. 01Step 01

    Prepare trusted sources

    Select content, confirm access rules, and plan how source updates are handled.

  2. 02Step 02

    Design retrieval and response

    Shape chunking, retrieval, prompting, source references, and fallback behavior.

  3. 03Step 03

    Evaluate with real questions

    Review relevance, grounding, permissions, and unanswerable requests against agreed scenarios.

Technology and architecture

Tools chosen for the work, not for fashion.

These are relevant options rather than a fixed bundle. Final choices depend on your existing environment, requirements, integrations, and operating needs.

LangChain

Retrieval orchestration option

Anthropic

Model provider option

Google Gemini

Model provider option

Hugging Face

Model / embedding ecosystem

Ollama

Local model option

PostgreSQL

Data storage option

Before the build

Set the context before choosing an approach.

Every service has different constraints. These prompts make the decisions behind scope visible before implementation begins.

Context Constraints Scope
  1. 01

    Which sources are approved and how are they maintained?

  2. 02

    Should answers respect existing user and document permissions?

  3. 03

    What should happen when sources do not support an answer?

Direct answers

Questions about RAG Systems.

Every project is different. These answers cover common starting questions; the details can be scoped to your systems and requirements.

Will a RAG system always give accurate answers?

No AI system can guarantee that. We design evaluation around your use case and can include source references, uncertainty handling, and escalation paths to help people review responses.

Can the system use internal documents?

Potentially, subject to the source formats, access permissions, data handling requirements, and the model and infrastructure selected for the project.

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Start with the challenge

Let's plan your RAG Systems project.

Share what you know so far. We can help identify the key questions and a practical next step.

Talk to our team