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AI & Copilot

Make the system reason, not just record.

Business applications have been very good at storing what happened

That's no longer the interesting part.

Copilot and the agent model change what an application is for. A well-built agent over your Dynamics and Dataverse data can answer a question a person would otherwise spend twenty minutes assembling from four screens and increasingly, act on the answer.

The technology is ready. Most organisations aren't, and that gap is where AI projects fail.

What we build

Copilot Studio agents
Custom agents grounded in your Dataverse, Dynamics and line-of-business data. Scoped properly, governed properly, and tested against what people actually ask rather than what demos well.
Copilot for Sales and Service
Deployed with the data model and process work that makes it useful, rather than switched on and abandoned.
AI Builder in process
Document processing, classification and prediction embedded in the flow of work. Invoices, delivery dockets, claims and forms read automatically into Dataverse instead of being keyed in. The easiest AI business case to prove, because the time saved is countable before the project starts.
Agent governance
DLP, environment strategy, and a defensible answer to what an agent can see and do. This is the security question of the next three years.

What a grounded agent actually does

The demo is the easy part. What decides whether an agent is safe to put in front of a whole company is what sits between the question and the data.

How a grounded Copilot agent answers a question A person asks a question. The agent interprets it, then queries Dataverse through a security layer that enforces the asking person’s own permissions, so it can only return records that person is already allowed to see. “Which of my projects are over budget this quarter?” A person asks Copilot Studio agent Scoped to a defined set of topics and actions Security trimming The agent queries as the person who asked. It cannot return a record they were not already allowed to open. Projects Dataverse Budgets Dataverse Finance system Integrated Most failures happen above this line, not below it: data that was never structured for retrieval, or a security model that lets an agent over-share.
The interesting engineering is the middle band. An agent that answers well but answers from the wrong records is worse than no agent at all.

Most AI projects fail on preconditions, not capability

Data that isn't structured for retrieval. Security models that would let an agent surface things it shouldn't. Licensing prerequisites nobody checked. A use case chosen because it demos well rather than because it's worth money.

Our AI readiness assessment covers all four and gives you a prioritised roadmap. Available as part of an Elevate block, or standalone.

Book a readiness conversation See Elevate

Start small and find out what's real

An Elevate block is the fastest way to get a working pilot in front of your team rather than a slide about one. Forty hours is usually enough to build something people can use and judge.

How Elevate works