Frequently asked questions
Direct answers to the questions mid-sized enterprises ask us most often: what we do, how an engagement runs, what it costs, where your data goes, which models we build on and who owns the company. If your question is not here, contact us and we will answer it personally.
01
About Applicat AI
Applicat AI is a frontier applied AI company headquartered in London. We design, build and run production AI systems on frontier models for mid-sized enterprises, and we stay accountable for those systems after go-live. Most engagements begin with one process that is costing an organisation real money and end with a working system inside that organisation, reported on every month against the number agreed at the start.
Applied AI is the practice of taking AI capabilities that already exist, in particular frontier models, and applying them to specific business processes to produce measurable results in production. It is distinct from AI research, which creates new capabilities, and from AI strategy, which advises on them. Success is measured by what is running in your business at the end of it.
Frontier models are the most capable AI models available at any given time, currently produced by AI labs such as Anthropic, OpenAI and Google, alongside the strongest open models. A frontier applied AI company builds on those models and designs its systems so that the next generation can be swapped in and proven in days, which matters because the frontier moves every few months.
The founders, and no outside shareholder of any kind: in particular none of the model labs, clouds, consultancies or funds now selling AI deployment. We also sell no software licences of our own. That is why the model that goes into your system is the one that scored best on your own cases, and why we are free to tell you that a thing is not worth building.
Because the model is then picked by testing it on your work, your process knowledge and everything built with it stay yours under contract, and the engagement is sized for your organisation rather than for the Fortune 500. Plenty of organisations will use both: an independent firm can run the testing, and run the system after go-live, alongside a lab's own team.
A consultancy advises. Applicat AI frames the work, proves it, builds it, deploys it and then runs it, with one team accountable from the first workshop to the monthly report in production.
02
Working with us
With a conversation about the outcome you need, followed by an Applied AI Sprint: four to six weeks, fixed scope, fixed fee. It produces a ranked shortlist of what is worth building and a working version of the strongest candidate, running on your real data inside your own environment.
An Applied AI Sprint puts a working, production-grade system on your real data within four to six weeks. What follows is scoped once that has been measured, so the date for a first release to your users is agreed on evidence once there is some. It depends mostly on your integrations and your governance requirements.
Sprints are a fixed fee. Programmes are priced against outcome milestones. Managed AI Operations is a monthly subscription with service levels attached. We give indicative ranges in the first conversation, before anyone spends time on a proposal.
Yes. Our engineering team is globally distributed and we work with clients across the UK, Europe, Africa, the Middle East and North America.
We encourage it. Our forward-deployed engineers work alongside your teams, and handing over what we build is part of the Build and Deploy stages. You decide afterwards how much of the running you keep in-house and how much you leave with us.
The system keeps running. It sits in your own environment, on your accounts, and the code, the data, the prompts and the test cases are yours in the contract, with your team holding what it needs to change them. There is no part of it we can switch off.
03
Technology, security and data
We have no house model. We build on frontier models from Anthropic, OpenAI and Google, and on the leading open models when data residency, cost or speed favour them. Which one goes into your system is settled by running your own cases through the candidates and comparing the scores, and it can change when a better model arrives.
Nowhere it is not already. The system runs in your own cloud account, or in a dedicated environment under your control, and model access uses enterprise endpoints where your data is not used to train anyone's models. Residency rules are captured in the first week and drive the choice of region and model.
Every system gets the least access it needs, a fixed set of tools it is allowed to use, checks on what it sends back, a person in the loop wherever an action is hard to undo, and a full record of what it did and why. Every change is then re-run against tests built from your own cases, which is the evaluation suite that gates releases. These are built during Build and watched during Run.
Yes, and the work usually stands or falls on it. We integrate with Microsoft 365 and Azure, Google Workspace and Cloud, AWS, SAP, Microsoft Dynamics, Sage, Salesforce, ServiceNow, HubSpot, Zendesk and most platforms with a supported API, and with secure automation where an API does not exist.
Bring the frontier into production.
Tell us about the process you want to change. We reply within one business day.