Applied AI

AI that works inside real operations.

We design AI as part of the operating model — connected to workflows, data quality, decision rights and measurable adoption — and guide clients from early generative use cases to controlled agentic execution, only where readiness is proven. Before anything gets built, we answer the question most teams skip: which model do you actually need, how big, and where should it run.

The problem

Most teams pick a model before they’ve defined the job.

Everyone debates which model is “best”. Almost nobody asks which model the job needs.

“Bigger model” and “more AI” are not a strategy. They’re a guess.

Token prices look low today. They’re subsidised, and that won’t last.

Full dependence on one hyperscaler. A business risk, not an infrastructure choice.

Sensitive data sent to an external model. A governance decision you can’t undo afterwards.

Our position

Deep applied AI, sized to the job.

Before we talk about models, we answer two questions: which tools does the AI need access to, and which data does it need access to.

Not the biggest model.

The model the job needs.

Not one hyperscaler by default.

Infrastructure you control, where it fits.

Not a separate technology track.

AI designed into the operating model.

Those answers decide whether you need a large, general-purpose model, or whether a smaller, open source model running on infrastructure you control gets the job done — at a fraction of the cost and risk.

How it works

The language model talks. It doesn’t decide alone.

We separate every system into two layers. A deterministic layer — rules, logic, structured data — handles what needs to be predictable, auditable and fast. A probabilistic layer — the language model — handles communication: understanding intent, generating language, managing ambiguity. The deterministic layer feeds the language model; the language model talks to the people. Neither layer replaces the other.

rules

logic

structured data

LLM

understanding intent

generating language

managing ambiguity

Model fit

A small or large model isn’t a preference. It’s a calculation.

Neither option below is “better” in the abstract. The right choice depends on the job, the data and how much control you need to keep. Before writing any code, we answer some questions to determine the model, its size and where it runs:

What is the task and how repeatable is it?
How sensitive is the data it will touch?
What volume and latency does the use case actually require?
What is the existing or foreseen infrastructure?
How much control do you need to keep in-house?

Dimension	Small Language Model (local)	Large Language Model (hosted)
Cost exposure	Small (local)Predictable — tied to your own compute	Large (hosted)Variable — tied to token price, which is subsidised today
Data governance	Small (local)Data stays on infrastructure you control	Large (hosted)Data passes through a third party by default
Latency	Small (local)Low, local	Large (hosted)Depends on network and provider load
Implementation effort	Small (local)Higher upfront (fine-tuning, RAG setup)	Large (hosted)Lower upfront, higher running cost over time
Best fit	Small (local)Defined, repeatable tasks on your own data	Large (hosted)Broad, open-ended tasks, general knowledge

The smallest sufficient technology — never automatically the largest.

Model

LLM, SLM or classical ML

Open-ended reasoning with an LLM, defined domain tasks with an SLM, and precise detection and prediction with vision or classical ML.

Hardware

Edge or cloud — your choice

Runs on your preferred hardware: an edge device for data security, or the cloud for scale.

Platform

Private, public or hybrid

For cloud hosting we select the best-fit platform: private cloud for control, public cloud for scale, or a hybrid setup.

Into operation

The model is a decision. The operation is the work.

Without clear ownership, reliable data and strong governance, AI remains unpredictable in a real organisation. So before we choose technology, three things are put in place.

Ownership

One accountable team

No more fragmented decision-making about who decides what. One team owns delivery, quality, support and incidents.

Data

Quality and provenance first

We only build AI applications once data quality and provenance are secured — and we help diagnose and resolve those issues.

Governance

Approval and control

Strict approval gates, plus control mechanisms that systematically eliminate risks — enforced technically, not just on paper.

AI maturity path

Moving at the right pace.

AI maturity is how much we let AI do on its own. The path runs in five levels — from drafting and answering questions for your teams, to preparing decisions in workflows, serving clients with approved content, proposing actions people approve, and finally coordinating multi-step work across systems under monitoring.

We never jump to the highest level. A workflow moves up one step at a time, only when its value, risk and operational readiness justify it — and critical decisions keep a human in the loop.

L1 · Internal GenAI

Search, summarize, draft and answer internal questions from approved knowledge. Low-risk productivity inside teams.

L2 · Operational GenAI

Prepare decisions in workflows: stock, campaigns, support, execution. Workflow owner and KPI are clear.

L3 · Client-facing GenAI

Product guidance, service and self-service with approved content and escalation. Confidence, brand and risk rules are tested.

L4 · Controlled action

AI prepares recommendations, tasks or changes; people approve critical actions. Decision rights and approval flows are live.

L5 · Controlled agentic AI

AI coordinates multi-step workflows across approved skills, APIs and systems. Logging, monitoring and rollback are in place.

The AI Pod

One team that takes responsibility for scaling.

The work is owned by one multidisciplinary pod — not as a staffing model, but as an ownership one. Specialists engage as the use case requires and ease off when it doesn’t; the core stays, and stays responsible for delivery, quality, support and incidents.

Senior team from day one

A senior AI engineer and an architect form the core, with specialists brought in as the use case requires. Small enough to move fast, senior enough to carry production.

Dedicated AI Champion, along the way

Part of the Pod, and your single point of contact throughout. Keeps knowledge flowing into your team and raises blockers before they cost time.

How it works

Step 1 · Week 1

Select the use case

Jointly select one PoC that is ready to scale.

One proven PoC
Clear scope
Risk and value assessed

Step 2 · From week 2

Pod starts

Fixed scope, timeline and cost — engineering plus applied AI where useful.

Dedicated team gets started
Sprint cadence based on your backlog
Knowledge transfer every two weeks

Step 3 · Ongoing

Team takes over

Your team builds alongside us and gradually takes over the way of working.

AI Champion active
Scale-up or exit criteria
Knowledge stays with your team

Case studies

Proof in production

Selected engagements where applied AI moved from pilot to dependable, controlled operations — matched to each client's readiness, risk and decision rights.