Custom AI applications

Sometimes nothing on the market does the thing, because the capability you need sits in the model itself. Custom AI applications are the products and internal tools built on that: a customer-facing feature, an assistant for a piece of regulated work, or a platform your own teams build on top of. We design the architecture so the model underneath can be changed when a better one arrives, prove the idea on your cases before it is built out, and hand over an operating model that says who watches quality, who approves an upgrade and who owns the bill.

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Fig. 01Use cases

01AI-native products
02Internal platforms
03Assistants for expert work
04Multimodal systems
05Choosing the model

How the system is put together.

What goes in, what the system is allowed to touch, where a person decides, and the number it gets measured on. We draw every system before we build it, which is the cheapest place to have the argument about what it should do.

What actually changes once it is live.

These are the changes we measure. One of them becomes the number in the contract, and the monthly report is written against it for as long as we run the system.

A product on the frontier without hiring a research team
An architecture that survives the next generation of models
Someone accountable for quality, latency and cost once it is live

What custom ai applications gets used for.

AI-native products

Customer-facing features where the model is the product, from specialised assistants to generation and analysis tools.

Internal platforms

Shared retrieval, agents, testing and governance, so your teams build on one foundation and not six.

Assistants for expert work

Underwriting, clinical administration, legal review, engineering design. Work where being roughly right is worse than being no help at all.

Multimodal systems

Images, audio, video and documents understood together, for inspection, monitoring and media work.

Choosing the model

An independent read on which frontier or open model wins on your cases, what it costs to run, and how you would move if that changes. We have no licence riding on the answer.

From your process to a running system.

01Agree the outcome and the test that will prove it, before anyone writes code.
02Choose the model by running the candidates on your cases, and build the model layer so it can be swapped later.
03Build the application itself: orchestration, guardrails, monitoring and cost controls.
04Prove it on real users, in a narrow slice, with a way back.
05Hand over the operating model: who watches quality, who approves a model upgrade, who owns the bill.

Good fit

The right call when you have a clear product idea and a real user at the end of it, and nobody at hand who has shipped on frontier models before.

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FAQ

Custom AI applications: questions

Where the measurements show it earns its place. Most applications do better on a frontier model with well-designed context, retrieval and tools than on a fine-tuned smaller one. Fine-tuning becomes worth it for narrow repetitive tasks, for hard latency or cost targets, or when an open model has to run inside your own environment because nothing may leave it.

You own the application, its code, its prompts and the tests built from your own cases. Applicat AI keeps its own accelerators and tooling, and licenses those to you for the life of the system. End the relationship tomorrow and what we built keeps running, with your team holding everything they need to change it.

The model layer is designed so a new one can be dropped in. We run it against the tests built from your cases, compare quality, latency and cost with whatever is running now, and show you the comparison. It only reaches your users if it wins. That work is part of Managed AI Operations, and it is the reason a system built this year is still the right system in three years.

Yes, and we start by measuring what it actually does today, because that is usually unknown. Then we stabilise it, add the testing and the monitoring that were missing, and move it onto an architecture where the model underneath can be changed. Sometimes the honest answer after that assessment is that rebuilding costs less than rescuing, and we will put that in writing.

Systems that often run alongside this one.

Bring the frontier into production.

Tell us about the process you want to change. We reply within one business day.

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