Mission Brief
AI Consulting That Ships
Working Agents.
Most AI projects stall as a demo that impressed everyone once. We design, build, and run production AI agents — in Mastra, CrewAI, or LangGraph, wired to your real systems over MCP — and hand you the code, the evals, and the runbook.
Flight Manual
What Does An AI Consultant Actually Do?
An AI consultant works out which parts of your business an AI agent can genuinely take over, builds those agents against your real systems, and puts controls around them so they can be trusted in production. In practice that is four kinds of work — and the last two are where most projects fail.
Agents & Multi-Agent Workflows
Retrieval & Company Knowledge
Automation & Integration
MCP & Tool Infrastructure
Honest Log Entry
Why Most AI Pilots Never Reach Production.
The demo is the easy 20%. Projects stall on the other 80%: nobody can tell whether a change made the agent better or worse, permissions were never scoped so it can’t be trusted with real data, costs are invisible until the first surprising invoice, and when the model is upgraded underneath it, quality quietly drops and no one notices for a month.
We build the boring parts first — evals, scoped credentials, approval gates, cost instrumentation — because they are the difference between an agent you demo and an agent you depend on.
Mission Profiles
Three Ways To Fly This.
Every engagement is scoped and quoted against the work in front of it, so there is no list price here. These are the three shapes that work — tell us which one fits and we will put a number on it.
Agent Sprint
One workflow, picked because it is expensive and repetitive, taken to production. Fixed scope, agreed and priced before we start.
- One high-volume workflow, live
- Framework and model selection, with the reasoning written down
- Eval set so you can tell if it regresses
- Handover session and a runbook
Agent Build
A multi-agent system your team owns and can extend: orchestration, retrieval, integrations, observability, and the tests that keep it honest.
- Multi-agent orchestration across your real systems
- Retrieval layer over your documents and records
- Evals, tracing, and cost instrumentation
- Approval gates on every write path
- Source code, in your repository, no vendor lock
Run & Improve
We operate what we built. Models change underneath you roughly every quarter — this is the tier that absorbs that instead of letting it quietly degrade.
- Model upgrades and prompt migration
- Eval regression runs on every change
- Token and inference cost tuning
- Monthly report, and a human who answers
Not sure which profile fits? That is what the briefing is for — and we will tell you if the answer is none of them.
Flight Sequence
How An Engagement Runs.
Four stages. You can stop after any one of them and still be left with something that works.
- 01
Briefing
Thirty minutes. You describe the work that eats your team’s week; we say plainly whether an agent is the right tool for it, and tell you when it isn’t.
- 02
Flight Plan
A short written plan: the workflow we’d automate first, the framework we’d use and why, what the finished system will cost you to run each month in inference, and how we’ll measure whether it worked. Our fee for building it is fixed here too.
- 03
Build
Weekly working software, not status decks. You see the agent run against real data early, while it is still cheap to change direction.
- 04
Handover
Code in your repository, evals in your CI, a runbook your team can act on, and a walkthrough. You can carry on without us — that is the point.
Instrument Selection
Which AI Agent Framework Should You Use?
There is no single right answer, and anyone who says otherwise is selling their preference. Here is how we actually decide — and we put the reasoning in writing before we build.
Need Somewhere To Run It?
Agents need somewhere to read from and write to. If you don’t already have that, our platform gives you a CRM, documents, automations, bookings, and a 478-tool MCP server the agent can drive on day one — instead of spending the first three weeks of the build wiring up plumbing.
It is Apache-2.0 and self-hostable, and it is entirely optional. Most engagements build straight into the client’s existing stack.
- 478 MCP tools — Scoped, permissioned, ready for an agent to call.
- Company Brain — Retrieval over your documents with pgvector, already built.
- Approval queue — AI proposes, a human confirms, before anything goes live.
- Apache-2.0 — Self-host it, fork it, or walk away with your data.
Flight Record
Read The Code Before You Hire Us.
We are not going to show you a logo wall. Everything we claim about building agent infrastructure is in a public repository you can audit before the first call — which is a harder thing to fake than a testimonial.
The same team that built a 478-tool Model Context Protocol server, a pgvector retrieval layer, and a human-in-the-loop approval queue is the team that will build yours. You can go and read how we did it.
Transmissions In
AI Consulting, Answered.
- How much does AI consulting cost?
- We quote each engagement against its own scope rather than publishing a list price, because the honest answer depends on how many systems the agent has to touch, how clean your data is, and whether the result needs approval gates and audit trails. Send us the workflow through the quote form and you will get a fixed scope and a fixed price back, usually within one working day. That price does not move once agreed.
- How long until something is actually in production?
- Two weeks for a single workflow in an Agent Sprint. Six to twelve weeks for a multi-agent build. We deliberately put the first agent in front of real data in week one, because pilots that stay in a sandbox are how most AI projects quietly die.
- Which framework will you use — Mastra, CrewAI, or LangGraph?
- Whichever fits your stack and your failure modes, and we write the reasoning down. Mastra when you are TypeScript or Next.js and want typed tools and built-in evals. CrewAI when the work maps to a crew of role-specialists and you want a fast prototype. LangGraph when runs are long, need approval gates, or must survive a crash. MCP sits underneath any of them as the integration layer.
- Do we own the code?
- Yes. Code lands in your repository under your licence, with no runtime dependency on us and no per-seat fee to keep using it. Our own platform is Apache-2.0 for the same reason: we do not think lock-in is a business model.
- Do we have to use the SimplerDevelopment platform?
- No. Most engagements build into the client’s existing stack. The platform is there if you want somewhere to run the result — CRM, documents, automations, and a 478-tool MCP server already wired up — but choosing it is optional and never a condition of the work.
- What if our data is a mess?
- That is the normal starting condition, and it is usually the actual project. Retrieval quality is set by how your documents are structured, chunked, and permissioned long before the model matters. We audit that in the flight plan and tell you honestly if the first phase needs to be data work rather than agents.
- How do you stop an agent doing something destructive?
- Scoped credentials so an agent can only reach what it needs, approval gates on every write path so a human confirms consequential actions, and an eval set that runs on each change so regressions surface before your customers find them. We build the same way on our own platform, where AI-proposed changes go through a human approval queue.
- Can you work with our existing team and stack?
- Yes, and it is the better outcome. We build alongside your engineers, in your repository and your CI, and hand over with a runbook and a walkthrough so the system keeps improving after we leave.
Open A Channel
Get A Quote.
Tell us what the work looks like and we’ll come back with a fixed scope and a fixed price — usually within one working day. If an agent is the wrong tool for it, we’ll tell you that instead.
Tell Us About The Work
Rather Talk First?
Book thirty minutes instead. No pitch deck, no discovery-call funnel — an engineer who has shipped this, looking at your actual problem.
Still Weighing It Up?
Read how we build agent infrastructure, or see the platform they run on.