How is this different from a custom GPT?

A custom GPT is great for tasks; a knowledge assistant is built for facts, grounding every answer in your content with citations and proving accuracy through evaluation. Many teams run both.

What if our content lives in a warehouse?

The retrieval layer can draw from prepared content or, for richer cases, the RAG Retrieval and MCP work in Data Solutions, so the assistant answers from governed, trusted data.

AI Assistants

An assistant that answers from your knowledge, with citations.

A retrieval-augmented assistant that answers from your proprietary content, grounded and cited, with a knowledge base, a conversational flow, and evaluation, so the team gets reliable answers instead of confident guesses.

Book an initial consultation Start with an AI Readiness Audit

Our team wastes hours hunting through documents for answers that already exist. Can AI just tell them?

A general chatbot doesn't know your SOPs, your product details, or your policies, and when asked, it makes something up. A knowledge assistant grounds every answer in your own content and cites its source, so the team gets trustworthy answers fast and can verify them. This project builds the assistant and the evaluation that proves it is reliable.

What's included

Knowledge base setup

Your content prepared and indexed for retrieval, so the assistant draws from the right sources with the right scope.

Conversational flow

A conversational experience designed for how the team will actually ask, with grounding and citations built in.

Assistant evaluation

Evaluation against realistic questions, so you can show the assistant answers accurately before you rely on it.

Grounded, cited answers

Answers that come back grounded in your content with citations, so the team can trust and verify them.

How it works

1
Build the knowledge base

We prepare and index your content for accurate, scoped retrieval.
2
Design the experience

We build the conversational flow with grounding and citations.
3
Evaluate

We test against realistic questions so reliability is proven, not assumed.

What you walk away with

A knowledge base indexed for accurate retrieval
A conversational assistant designed for real questions
Grounded answers with citations the team can verify
Evaluation that proves the assistant is reliable

Frequently asked

How is this different from a custom GPT?: A custom GPT is great for tasks; a knowledge assistant is built for facts, grounding every answer in your content with citations and proving accuracy through evaluation. Many teams run both.
What if our content lives in a warehouse?: The retrieval layer can draw from prepared content or, for richer cases, the RAG Retrieval and MCP work in Data Solutions, so the assistant answers from governed, trusted data.

Stop hunting for answers that already exist

Book a consultation to build a grounded, cited knowledge assistant your team can trust.

Book an initial consultation Start with an AI Readiness Audit

Where this leads next

GPT Workspace

Pair the knowledge assistant with role-specific custom GPTs for tasks.

Explore the project

AI Agent Build

When answering isn't enough and the assistant needs to act, build a production agent.

Explore the package