Do we need this if we already have enterprise AI access?

Yes, they complement each other. Provisioning gives access; a GPT workspace gives people purpose-built assistants for their roles, which is what turns access into daily use.

What about answering from our own documents?

For grounded, cited answers over your proprietary content, the Knowledge Assistant adds a RAG layer. Many teams do both: custom GPTs for tasks, a knowledge assistant for facts.

AI Assistants

Role-specific AI assistants your team uses on day one.

A library of custom GPTs built into your workspace, scoped to specific roles and tasks, with the enablement that gets people actually using them. The fastest path from AI interest to AI value.

Book an initial consultation Start with an AI Readiness Audit

Everyone's heard AI can help, but our team doesn't know where to start. How do we get a quick, real win?

A generic chatbot leaves people guessing what to ask. Role-specific custom GPTs do the opposite: each one is scoped to a real job, pre-loaded with the context and instructions for that role, so the value is obvious the first time someone opens it. This project builds that library and gets your team using it.

What's included

Custom GPT build

A library of custom GPTs, each scoped to a specific role or task, with the instructions, context, and guardrails that make it genuinely useful.

Workspace enablement

The enablement that gets the team using the GPTs: discoverable in the workspace, with examples and onboarding so adoption is real.

Role-specific value

Assistants built around the jobs people actually do, so the win is obvious from the first interaction.

Fastest path to value

Useful AI in weeks, the quick win that builds momentum for bigger automation later.

How it works

1
Scope the roles

We identify the roles and tasks where a custom GPT delivers a fast, real win.
2
Build the library

We build each GPT with the instructions, context, and guardrails that make it useful.
3
Enable adoption

We make the GPTs discoverable and onboard the team so they get used.

What you walk away with

A library of role-specific custom GPTs in your workspace
Enablement that drives real adoption
Assistants built around the jobs people actually do
Useful AI in weeks, the quick win that builds momentum

Frequently asked

Do we need this if we already have enterprise AI access?: Yes, they complement each other. Provisioning gives access; a GPT workspace gives people purpose-built assistants for their roles, which is what turns access into daily use.
What about answering from our own documents?: For grounded, cited answers over your proprietary content, the Knowledge Assistant adds a RAG layer. Many teams do both: custom GPTs for tasks, a knowledge assistant for facts.

Get a quick AI win your team will use

Book a consultation to build the role-specific custom GPTs that put AI to work fast.

Book an initial consultation Start with an AI Readiness Audit

Where this leads next

Knowledge Assistant

Add a grounded, cited assistant that answers from your proprietary knowledge.

Explore the project

Workflow Automation

Move from assistants to automating a real bottleneck end to end.

Explore the package