Applied AI

Applied AI,
in production.

We engineer systems that combine language models, live data pipelines, and classical statistics — turning open-ended problems into structured, auditable decisions.

Get in touch What we do
What we do → Claude / LLM engineering paired with classical predictive modelling. Decision quality → Every output tested for consistency. Close calls flagged for human review. How we engage → Embedded or scoped — both ending in production code, not slides. Get in touch → Tell us what you're working on. We reply within a day.
The problem

Plenty of AI projects stall before production.
The ones that ship aren't always easy to trust.

Three failure modes we see repeatedly — and the discipline required to avoid them.

01

AI as slideware

Most consultancies stop at demos, decks, and roadmaps. The thing never makes it to production — or it does, in a brittle form that someone else has to maintain.

02

Black-box outputs

When AI ships, it's an opaque text generator. No consistency check, no confidence signal, no way to audit a single decision the system makes.

03

No data-science discipline

LLMs get treated like magic. They're statistical systems that need testing, calibration, and structured failure modes — exactly like any other model.

What we do

Two practices.
One discipline.

Production-grade Claude applications, paired with a deep predictive-modelling and machine-learning toolbox. Engineered as one practice — classical ML where the right answer is numerical, LLMs where it's linguistic or structural, statistical analytics over both to keep outputs auditable.

Pillar 01

Claude & LLM engineering

  • Consistency testing Running prompts repeatedly to expose instability, refining until the model gives the same answer every time on the same input.
  • Structured human-in-the-loop When a decision is genuinely ambiguous, surfacing alternatives and reasoning for a one-click human review instead of guessing.
  • Multi-step workflows Long-running sessions driven by explicit state machines, so the framework keeps the model on the rails.
  • Prompt orchestration System, tool, and per-state prompts designed for reliability under real-world inputs.
  • Model selection matched to task complexity Strongest models where reasoning matters, faster models where it doesn't.
  • Tool use with validated outputs Claude calling our functions and returning structured results we can verify, with retries and fallbacks built in.
  • Live data pipelines Feeding current, structured information into the model — not stale CSVs.
Pillar 02

Predictive modelling & machine learning

  • Machine learning Gradient-boosted models (GBMs) and related supervised methods for classification and regression.
  • Generalised linear models (GLMs) And statistical inference, for problems where the structure of the relationship matters as much as the prediction.
  • Time-series forecasting Forecasting and change detection where the signal moves through time.
  • Simulation and optimisation Monte Carlo, mathematical programming, and heuristic search for decision-under-uncertainty problems.
  • Pricing and price-elasticity modelling Understanding how demand responds to price — the foundation for any optimisation that touches revenue.
  • Data analytics That go beyond dashboards — the bridge between raw data and a decision someone actually makes.
How we engineer trust

Tried-and-tested framework
for AI decision quality.

Every AI decision carries a confidence level and a reasoning trail. Our framework makes the most of powerful LLM logic by automating the obvious decisions and routing the ambiguous ones to structured human review.

Building confidence

The AI is certain

Decisions are tested for consistency across multiple runs. If the AI gives the same answer every time, it ships. If not, the prompt is refined until it does.

Handling ambiguity

The AI flags the close calls

When a decision is genuinely debatable, the AI says so. It provides its best choice, names the alternatives, and asks for human input rather than picking arbitrarily.

Human validation

The expert makes the judgement call

A reviewer sees each flagged decision with the AI's reasoning, alternatives, and a clear, structured question. One click confirms or swaps. The system adapts immediately.

The result

High-confidence decisions are consistent and auditable. Low-confidence decisions become a structured conversation — not a black box.

~100% consistency on clear-cut calls
Reasoning provided for every decision
Alternatives surfaced for every close call
One click confirm or swap by the expert
How we engage

Embedded or scoped.
Either way, code that ships.

Two engagement models, both ending in working software running in your environment — not a deck.

Option 01

Embedded build

We work alongside your team for three to twelve months, build to fit, and transfer the work when you trust it.

Direct engineer-to-stakeholder contact, no account layer Iterative shipping — production from week one Knowledge transfer baked in, not bolted on
Option 02

Scoped build

Fixed-scope build with clear deliverables and a defined endpoint. Best for self-contained problems where the spec is well understood.

Written scope, fixed price, fixed timeline Working code at the end — not a report Optional post-handover retainer
Get in touch

Let's talk.

A problem you'd like applied to, or just want to compare notes? Drop a line.