What can we build on top of this?

RAG retrieval is the data layer under a knowledge assistant or a grounded agent. With it in place, the AI Workflow Automation pillar builds the assistant or agent that uses it.

Does our data need to be perfect first?

Not perfect, but trustworthy. Retrieval quality follows source quality, which is why this pairs well after Data Foundation work or a Data Audit that flags the gaps.

AI-Ready Data

Let agents answer from your knowledge, grounded and cited.

Prepare the corpus, embeddings, and vector store for retrieval-augmented generation, so an AI assistant answers from your proprietary content with grounding and citations, instead of a general model guessing.

Book an initial consultation Start with a Data Audit

How do we get an AI assistant that answers from our knowledge, not the internet's?

A general model has no idea what is in your SOPs, your warehouse, or your field notes, and when asked, it guesses. Retrieval-augmented generation fixes that by grounding answers in your own content. This project builds the retrieval layer it depends on: a clean corpus, a reliable embedding pipeline, and a vector store tuned for accurate recall.

What's included

Corpus preparation

Source content cleaned, chunked, and structured for retrieval, with the metadata that makes results filterable and citable.

Embedding pipeline

A repeatable embedding pipeline that keeps the vector store current as the underlying content changes.

Vector store setup

A vector database provisioned and tuned for accurate, low-latency retrieval at your scale.

Grounded retrieval

Retrieval wired so answers come back grounded in your content with citations, the foundation a trustworthy assistant needs.

How it works

1
Prepare the corpus

We clean, chunk, and enrich your source content with retrieval metadata.
2
Build the pipeline

We stand up the embedding pipeline and vector store, tuned for recall at your scale.
3
Validate retrieval

We test retrieval quality so answers come back grounded and citable.

What you walk away with

A clean, chunked, metadata-rich corpus ready for retrieval
A repeatable embedding pipeline that stays current
A vector store tuned for accurate, low-latency recall
Grounded, citable retrieval an assistant can build on

Frequently asked

What can we build on top of this?: RAG retrieval is the data layer under a knowledge assistant or a grounded agent. With it in place, the AI Workflow Automation pillar builds the assistant or agent that uses it.
Does our data need to be perfect first?: Not perfect, but trustworthy. Retrieval quality follows source quality, which is why this pairs well after Data Foundation work or a Data Audit that flags the gaps.

Ground your AI in your own knowledge

Book a consultation to build the retrieval stack a trustworthy, cited AI assistant depends on.

Book an initial consultation Start with a Data Audit

Where this leads next

Knowledge Graph

Add entity-rich context so retrieval follows real relationships, not just text similarity.

Explore the project

MCP Data Servers

Expose the retrieval layer to agents through governed MCP servers.