Solution

RAG Consulting & Implementation Services

A RAG system lets an AI model answer from your documents instead of from memory, so every answer can point at its source. We build them over the places your knowledge already lives, with citations, access control and an honest 'I could not find that' when the answer is not there.

2–6 weeks to a production-grade first integrationTypical timeline

3Services behind it

2Live demos

4Questions answered

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Is this you?

You are probably here because…

01The same questions get answered from scratch, again and again.
02Policies, contracts and manuals are spread over SharePoint, Drive, Confluence or Notion.
03You tried a general chatbot and it confidently made things up.
04You need answers you can trace back to a document.

How we approach it

Index what you actually have

Retrieval pipelines over SharePoint, Google Drive, Confluence, Notion and S3, with semantic search and permission-aware access, so people only get answers from documents they may see.

Force answers to come from the passages

The model is constrained to the retrieved text and cites it. If the document set does not contain the answer, the system says so rather than guessing.

Measure before you trust it

We score candidate models on your real questions for accuracy, latency and cost per call, then keep a test set you can rerun after every change.

The services behind it

Each one has its own page with deliverables, timelines and tools.

2–6 weeks to a production-grade first integrationCustom LLM Integration & RAG Implementation

4–10 weeks depending on data condition; audit alone in 2AI Data Strategy & Predictive Model Development

1–3 weeks for a full audit cycle, scope-dependentTechnology & AI Security Auditing

Live products behind it

Working systems, not slides. Each page has the detail and a button into the live demo.

Live demoGrounded Knowledge Search: Cited answers over your documents — never invented ones

Live demoEmbeddable Support Chatbot: Train it on your site, embed it with one script tag

Questions

What is RAG?

Retrieval-augmented generation. The system first retrieves the relevant passages from your documents, then has a language model write an answer using only those passages. That is what makes answers checkable and keeps the model from inventing facts.

How long does a RAG system take to build?

A RAG assistant over existing documents can be live in 2–3 weeks. A fuller integration with permissions, evaluation and several data sources takes longer, up to the 2–6 week range for a production-grade first integration.

Do our documents have to be clean?

Messy is fine, missing is not. RAG works with the documents you already have. During the audit we tell you if the content is too thin to answer the questions you care about.

Where does our data go?

That is a design choice made up front. Options include private model endpoints, on-premise or VPC deployment, and redaction of personal data before any model call, with an audit log of what was asked and answered.