Applied AI for mid-sized enterprises. You own it. We stay on it.

Applicat AI works directly with mid-sized enterprises to design, build and run AI systems on frontier models. Most engagements begin with a fixed-fee sprint of four to six weeks that puts a working system on your own data and ends in a written go or no-go. If it is a go, the same engineers build it, roll it out and keep it running. Nothing is handed to a second team, and what they build belongs to you.

Start with a sprint See the method

The problem

Why enterprise AI stalls between the demo and the deployment.

01
The demo that never ships
A convincing demonstration on sample data wins the steering committee, and then meets the integration queue, the permissions model and the risk register. Nothing in the demo was wrong. It was simply never built to go anywhere, and that gap between the demonstration and the deployment is where most enterprise AI value is lost.
02
The model you picked is already behind
Capability, price and limits move every few months. A system wired to one vendor and one prompt gets expensive to maintain, and upgrading it turns into a rebuild nobody has the appetite to fund twice.
03
When it underperforms, everyone points somewhere else
Consultancies advise. Software vendors sell licences. Integrators build to the specification they were handed. All three can be blameless while the system sits there doing very little, because none of them ever agreed to be accountable for the result.

Four ways to work with us. The last one has no end date.

Each engagement maps to a stage of the Applicat Method, so you can start small and extend on evidence.

Frame and Prove

Applied AI Sprint

Four to six weeks at a fixed fee. We spend the first week or two ranking your candidate processes by what they are worth and how buildable they are. Then we build the top one on your own data, inside your own environment, and score it against cases your team chooses. You end with a number for what it costs to run and a written answer on whether to carry on.

Your processes ranked by value and feasibility
A working system on your real data, in your environment
Scores against cases your own team picked
What it costs to run, and how fast it answers
A written go or no-go, with the evidence behind it

Fixed fee. Scope agreed before we start.

Build and Deploy

Applied AI Programme

The proof becomes a system your operation can depend on. It connects to what you already run, respects who is allowed to see what, keeps a record of every decision it makes, and clears your security review before release. Then we roll it out with the people who will use it and count whether they actually do.

Wired into your existing systems and identity provider
Who may see what, and what the system may not do
A test set that every future change has to pass
A documented package for your security review
Rollout, training and a supported first period

Priced by milestone against the agreed outcome.

Run

Managed AI Operations

A live system is never finished. Volumes shift, the data behind it changes, and the models underneath it are replaced every few months. We watch accuracy, safety and cost daily, re-test on your own cases whenever anything moves, prove a new model long before it reaches your users, and write you a report each month against the number agreed at the start. End it whenever you like. The system is yours and it carries on running.

A daily watch on accuracy, safety and spend
Re-tested on your own cases after every change
New models proven on your work before they go live
Named engineers on call, with agreed response times
A written monthly report to your sponsor

Monthly, with response times in the contract.

Any stage

Forward-deployed engineering

Named applied AI engineers working inside your teams for a defined period, in your tools and your stand-ups. They are there to move several use cases at once, and to leave your own people able to do this work without us.

Named engineers, agreed before you sign
A weekly review against the outcomes
A written plan for handing the capability across
Our accelerators and testing tools, used on your work

Team-based, minimum three months.

Built for the people who carry the outcome.

Chief Information and Technology Officers: A delivery team that builds on the frontier without tying the estate to one vendor, and that hands back systems your own people can run, change and eventually move elsewhere.
Chief Operating Officers: A number that moves in a process you already measure: throughput, cycle time, cost per case. People stay in control wherever the risk actually sits.
Chief Data and AI Officers: A route from a list of ideas to a handful of systems that are genuinely live, with test records that survive scrutiny and a model choice you can defend in a meeting.
Business unit leaders: Something working within weeks, tied to a number you are judged on, and a named team that is still on the system after go-live.

Security, data and governance

It runs on your accounts, under your security, and your team can read all of it.

No licence to protect, so no model to push

Applicat AI is founder-owned. No AI lab, cloud provider, consultancy or investment fund sits on the cap table, and we earn nothing from anyone else's software sales. Which model goes into your system is settled by testing the candidates on your own cases, and we can tell you a use case is not worth building without it costing us a thing.

Your process knowledge does not travel

The code, the data and the tests are yours in the contract. We commit in writing that what we learn about how your business works stays with your business, and none of your material trains a shared model.

It runs on your accounts, in your environment

Systems are deployed into your own Microsoft Azure, AWS or Google Cloud environment, or into a dedicated one under your control. Model access goes through enterprise endpoints where your data is not used for training. End the relationship and nothing has to be moved.

Your security team reviews a finished package

Identity and access, data handling, logging, what the system may and may not do, and the due diligence on every vendor in the chain: all of it is written up during the Build stage, before release. Your reviewers get something complete to approve or reject. Nobody is asked to sign off an intention.

Data that cannot leave the country does not leave

Residency, retention and sovereignty requirements are captured in the first two weeks, and they drive the choice of model, region and architecture from there on. Where nothing may cross a border, the system is built on open-weight models running inside that jurisdiction.

A record you can hand to an auditor

Every system keeps its test results, its change log and the points where a human decides. Internal audit, a customer due-diligence questionnaire and the incoming AI regulations all ask for the same evidence, and it is written as the system is built, while it is still cheap to write.

The agent governance checklist

FAQ

Enterprise questions

The Applied AI Sprint is one fixed fee, agreed in writing before anything begins. Programmes are priced by milestone against the outcome they are scoped to. Managed AI Operations is a monthly fee with response times written into the contract. We give indicative ranges in the first conversation, so nobody spends six weeks on a proposal that was never going to be affordable.

Yes. Systems are deployed into your own Azure, AWS or Google Cloud environment, on your accounts and behind your identity provider. Model access goes through enterprise endpoints where your data is not used for training. Identity, logging and data handling are documented during the Build stage, so your security review reads a finished package with the answers already written down.

No. The first stage works out what data and which integrations each candidate use case would need, and favours the ones that can succeed with what you already have. A great deal of valuable applied AI runs on the documents, tickets, emails and PDFs that have been sitting in your systems for years. If a use case genuinely needs a data platform first, we will say so and rank it below the ones that do not.

We are accountable for the system once it is live. The engineers who frame the work build it, deploy it and then run it, and the monthly report is written against the number agreed at the start. A consultancy sells advice and an integrator sells hours; our commercial model is tied to a system that works in production. We are also owned by nobody in the AI supply chain, so the model choice is made by testing, and we are free to recommend that you do not build at all.

The Applicat Method works the same way in any sector. It is built for processes made of documents, requests, decisions and data, which covers most of the operational work in financial services, hospitality, retail, manufacturing, professional services, telecommunications, property and the public sector. The industries page sets out the use cases it covers in each.

Keep reading

Start with a sprint. Decide on evidence.

Four to six weeks to a production-grade proof and a go/no-go decision on your own data.

Talk to us See the method

Why enterprise AI stalls between the demo and the deployment.

The demo that never ships

The model you picked is already behind

When it underperforms, everyone points somewhere else