AI agents & automation
An agent is only as good as the data it acts on. If your website, stock, and accounts disagree, the agent acts on the disagreement, confidently and at speed. We fix the foundation first, then build the agent. That order is the difference.
Why this matters now
The pitch you keep hearing is real: AI agents can now answer customers, chase invoices, draft replies, and keep stock moving. What the pitch leaves out is the failure rate. MIT found that 95% of enterprise AI pilots delivered no measurable return on P&L (Project NANDA, July 2025), and Gartner expects 60% of AI projects without AI-ready data to be abandoned before they reach production (February 2025). The cause is almost never the model.
A chatbot on top of inconsistent product data answers customers wrongly, in a confident tone.
An agent reordering stock from a number your warehouse does not recognise buys the wrong things, faster than a person ever could.
An assistant summarising figures from systems that disagree gives you a fluent report of numbers nobody can defend.
This is the same root cause behind why most AI projects fail: disconnected systems, each holding its own version of the truth. Agents do not remove that problem. They act on it, at speed. Which is why we build them in a different order.
What it is
A chatbot replies. An agent does the work: it reads your systems, decides the next step within boundaries you set, and carries it out, drafting the reply, raising the order, chasing the invoice, updating the listing. That is why the quality of what it reads matters more than the cleverness of the model. The model is rented and improves on its own. Your data is the part only you can fix, and the part that decides whether the agent helps or harms.
We build agents in three tiers of increasing autonomy, and we match the tier to what your foundation can honestly support, not to what the demo looked like.
How we do it
Every agent engagement starts with the diagnostic: a map of your systems and data, an AI-readiness review, and a written plan with priorities. If your foundation is ready, we build. If it is not, we tell you exactly what it takes to get ready, and the diagnostic fee is credited against the project when you do.
We map your systems, test whether your data can be trusted, and give you a written plan. This is where every agent project should start, and where most failed ones did not.
Systems map · AI-readiness review · written plan
Workflows that categorise enquiries, draft replies for your approval, extract order data from emails and documents, and flag reconciliation exceptions. Lowest risk, fastest proof.
Enquiries · documents · exceptions
An internal "ask the business" assistant for your team, or a customer-facing one for stock, orders, and policies, answering accurately because it reads one trusted source.
Internal · customer-facing · your data
Agents that complete bounded work: raising purchase orders at stock thresholds, chasing unpaid invoices, updating listings across channels. Always with human approval gates at defined points.
Bounded scope · approval gates · audit trail
On tools: we choose the model and tooling per engagement, based on your data, your budget, and your risk, and we build on the systems you already run. MYT resells no software and takes no commission, so the recommendation is a diagnosis, not a sale. The subscriptions belong to you, not us. And because the same clean foundation also powers your reporting and gets you recommended by AI assistants, you build it once and use it in three directions.
What you get
A written diagnostic first: what your data can support today, what it cannot yet, and the order to fix it in.
An agent scoped to real work in your business, with its boundaries and approval gates agreed with you before it acts on anything.
A fixed price for a fixed scope, with the Embed stage included: training, documentation, and 30/60/90-day support so your team runs it after we leave.
A foundation that outlives the agent: the same clean, connected data also powers your dashboards and your visibility to AI assistants.
We are honest about what this is. We will not build you an agent on data you cannot trust. If your systems disagree with each other, the agent will act on the disagreement, confidently and at speed. That is why every agent engagement starts with the diagnostic. If your foundation is not ready, we will tell you, and tell you what it takes to get ready. Anyone who skips that step is selling you the failure statistics.
Common questions
In short
Start where the failures skip
A 30-minute review tells you whether your foundation is agent-ready, and what to fix first if it is not. No pitch.