What is a fixed-scope AI engagement?

A package of AI work whose deliverables, scope and price are agreed before it starts. You know what you will receive and what it costs before you order, instead of paying for open-ended consulting hours.

Which fixed-scope engagements does Jemimo AI offer?

Two today: Regressops QA, where our engineers run Regressops across your live app for regression, drift and security readiness, and AI Agents & MCP Servers, where we build production-grade agents and Model Context Protocol servers with an evaluation harness and guardrails.

What if my project does not fit a fixed package?

Larger or less defined scopes are quoted directly. Use the enquiry form on our site and tell us what you are building.

The same senior people who scope it. We take on two engagements at a time, so every engagement gets our full attention.

How a fixed-scope AI engagement works: scope, timeline, what you get

What a fixed-scope AI engagement with Jemimo AI covers, how it runs from order to handover, what you receive, and when a larger, quoted scope fits better.

Most AI consulting is sold by the hour. That works when the problem is still being discovered, but it leaves the buyer carrying the risk: the scope can grow, the bill grows with it, and "done" is hard to define. A fixed-scope engagement inverts that. The deliverables are written down before any work starts, and the price is attached to them, not to the time we spend.

What "fixed scope" means

A fixed-scope engagement is defined by three things agreed up front:

The deliverables. A concrete list of what you receive at the end, for example a regression pass over your key flows with screenshot and video evidence, or an MCP server exposing your tools to Claude.
The boundaries. What the package covers and, just as important, what it does not.
The price and timeline. Both are set by the package tier you choose, before you order.

If the work you need sits inside those boundaries, a fixed scope is the fastest, lowest-risk way to start working with us.

How an engagement runs

Choose a package. Each package states its deliverables and tiers. You order it directly, without a sales cycle.
Share the brief. For Regressops QA that can be as little as the URL of your live app. For agents and MCP servers, it is the workflow, tools and data the agent needs to work with.
We deliver. Our engineers do the work, with AI-augmented tooling and humans firmly in the loop for every judgement call.
Handover. You receive the deliverables listed in the package, in a form your team can act on: a ship / no-ship call with evidence, or working code with the evaluation harness that shows it behaves.

What you get

Regressops QA. Human consulting, augmented by Regressops automation. We run it across your live app and hand back a ship / no-ship call you can act on. There is no test code for you to write and no framework to adopt. Depending on the package, it covers a regression pass over key flows, a scheduled drift watch that flags feature-level changes as they land, and a security readiness check with simulated attacks on your real entry points.

AI Agents & MCP Servers. Production-grade agents and Model Context Protocol servers, not demos. We work out where an agent is genuine leverage in your workflow, then build it with the evaluation harness and guardrails that let it run unsupervised. Builds are Claude-powered and BYOK-ready, so your data stays in your own account.

When a fixed scope is the wrong fit

A package works when the problem is well defined. If you are still deciding where AI fits in your business, need a programme of work across several teams, or need something no package describes, a fixed scope would either under-deliver or force the problem into the wrong shape. Those engagements are scoped and quoted directly, from AI strategy and adoption through to application building.

Because we take on only two engagements at a time, the people who scope your work are the people who do it.