AI, Data & Emerging Tech

Applied AI & Generative AI

From a proof of concept that answers a real business question to a production system with evaluation, guardrails, monitoring and a cost per request you can defend.

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Overview

AI that gets past the pilot

Part of our AI, Data Science & Emerging Tech practice.

Most AI pilots stall for the same reasons: nobody agreed what 'good' means, the data was not ready, and there was no plan for what happens when the model is confidently wrong.

We start from the decision the system is meant to improve, define an evaluation set before building, and design the failure path as carefully as the happy path. That is the difference between a demo and something you put in front of customers.

Capabilities

What we deliver

LLM applications & RAG

Retrieval-augmented generation over your own documents and data, with chunking and retrieval tuned to your corpus, citations, and grounding checks that limit fabrication.

Agents & workflow automation

Tool-using agents for document processing, ticket triage, research and back-office workflows — with human approval steps wherever the cost of an error is high.

Predictive & classical ML

Forecasting, anomaly detection, predictive maintenance, recommendation and classification — often a better answer than an LLM, and far cheaper to run.

Evaluation & guardrails

Task-specific eval suites, regression testing on prompt and model changes, red-teaming, PII handling, and content and safety filters.

MLOps

Feature stores, training and inference pipelines, model registry and versioning, drift monitoring, and automated retraining with rollback.

Private & on-premise deployment

Open-weight models running inside your own tenancy or data centre where residency, confidentiality or latency rules it out of a public API.

How we work

A predictable path from first call to handover

No two engagements are identical, but the shape is consistent — so you always know what happens next and what you get at the end of each stage.

Engagement models

Frame the use case

We identify where a model actually changes a decision or removes manual effort, and estimate the value before estimating the cost.

Prove it on your data

A time-boxed proof of concept against a held-out evaluation set. If it does not clear the bar we say so — that is a cheap and useful outcome.

Productionise

Pipelines, APIs, guardrails, monitoring, cost controls and a human-in-the-loop path for low-confidence cases.

Improve

Feedback capture, periodic re-evaluation, and model or prompt updates gated by the same eval suite.

Outcomes

What changes for you

A defined quality bar and the evidence that the system meets it.
Known cost per request, and a plan for what it looks like at ten times the volume.
Data handling a security review can approve, including private-model options.
An honest answer early when AI is not the right tool for the problem.

Tools & standards

What we work with

Python
PyTorch
Hugging Face
LangChain
LlamaIndex
OpenAI
Anthropic Claude
Azure OpenAI
AWS Bedrock
Vertex AI
pgvector
Pinecone
MLflow
Kubeflow
vLLM
Ollama

Questions

Frequently asked

Can our data stay in-house?

Yes. We deploy open-weight models on your own infrastructure or inside your cloud tenancy where residency or confidentiality requires it, and we will be clear about the quality trade-off.

How do we know it is accurate enough?

We build an evaluation set from your real cases before development starts and report against it throughout. No eval set, no production launch.

What does it cost to run?

We model inference cost per request during the proof of concept, then design caching, routing and smaller-model fallbacks to keep it predictable.

Let’s scope your next build.

Tell us what you are trying to ship. We will come back with an approach, a team shape and an honest estimate — we aim to reply within two working days.

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