Knowledge, with a source.

RAGSystems.

Answers grounded in the knowledge your organization actually owns.

We build retrieval-augmented generation systems that connect models to controlled knowledge sources, with retrieval quality, citations and evaluation treated as core product behavior.

Ground the answer See what we build
Owned knowledge.Visible evidence.Honest uncertainty.
From source to answer
01

SOURCES

Documents, records and knowledge with explicit ownership.

Illustrative retrieval study. No documents are uploaded or searched.

The problem worth solving

When the answer should come from your knowledge, not model memory.

General models know a lot, but they do not automatically know which internal source is current, authoritative or relevant. Retrieval systems connect a user question to controlled context before generation.

  1. 01

    Teams repeatedly search the same policies, manuals, documents or knowledge bases.

  2. 02

    Answers need to be grounded in approved sources rather than general model knowledge.

  3. 03

    Users need citations or visible evidence behind an AI-generated response.

  4. 04

    Knowledge is spread across many files, pages or repositories.

  5. 05

    A support or research workflow needs semantic search rather than exact keyword matching.

  6. 06

    An existing RAG prototype retrieves plausible but irrelevant context.

What we build

More than an answer.
A way to verify it.

01

Internal knowledge assistants

Question-answer systems grounded in organization-controlled documents and knowledge sources.

02

Semantic search systems

Search experiences that retrieve by meaning, not only exact terms.

03

Support knowledge retrieval

Grounded assistance around product, service or operational documentation.

04

Research copilots

Retrieval and synthesis flows that help users navigate larger bodies of reference material.

05

Citation-aware answers

Responses that preserve source references so users can inspect the supporting material.

06

RAG quality improvement

Evaluation and tuning of ingestion, chunking, retrieval and reranking in existing systems.

The agreed outcome

Grounded answers instead of confident guessing.

A useful RAG system depends on source quality, retrieval behavior and evaluation as much as it depends on the model generating the final response.

The Intelligence Yard standard
Scope / Agreed
  1. 01Source-aware answers
  2. 02Retrieval quality baseline
  3. 03Citation behavior
  4. 04Controlled knowledge scope
  5. 05Evaluation dataset
  6. 06Observable retrieval pipeline

Final deliverables follow the agreed project scope.

From intent to implementation

Start with the source.
Test what comes back.

  1. 01

    MAP SOURCES

    Identify authoritative sources, ownership, freshness and access boundaries.

  2. 02

    INGEST

    Clean, segment and enrich source material for retrieval.

  3. 03

    INDEX

    Create searchable representations using the storage and embedding strategy that fits the content.

  4. 04

    RETRIEVE

    Tune search, filtering and reranking so relevant context reaches the model.

  5. 05

    GENERATE

    Build answer behavior around retrieved evidence, citations and uncertainty.

  6. 06

    EVALUATE

    Measure retrieval and answer quality against a realistic question set before expanding scope.

Our wider AI methodology
Technology fit

The work chooses
the tools.

A relevant toolkit, not a compulsory stack. The final choices depend on the task, existing systems and operating constraints.

  • Python
  • LlamaIndex
  • pgvector
  • Qdrant
  • PostgreSQL
  • Redis
Explore the technology landscape
Where we draw the line
Retrieval quality determines whether grounding is real or only decorative.

Junkyard Mind / Intelligence Yard

Before we begin

Worth asking.
Clearly answered.

01Does RAG train the model on our documents?

Not necessarily. RAG usually retrieves relevant source material at request time instead of retraining the base model on every document.

02Can answers include citations?

Yes. Citation behavior can be designed so users can inspect the sources used to support an answer.

03Can RAG work with private documents?

Yes, provided access control and source permissions are designed appropriately for the use case.

04Do we need a vector database?

Not always. The right retrieval approach depends on corpus size, filters, search behavior and existing data infrastructure.

05How do you know whether retrieval is good?

A representative evaluation set can measure whether the system finds the right evidence and whether answers stay grounded in it.

06Can an existing RAG system be improved?

Yes. Poor results often come from ingestion, chunking, metadata, retrieval or ranking problems rather than the final prompt alone.

HAVE KNOWLEDGE THE MODEL NEEDS TO USE?

Better sources.
Better answers.

Bring us the sources, the questions people ask and what a trustworthy answer must show.

Let’s define
the work