AI built to do real work, not demos
We build applied AI that plugs into how you already operate — automation, applied intelligence, and the infrastructure to run it safely and predictably in production.
Best for: Organizations with a real workflow to automate, not just an interest in AI for its own sake.
AI that earns its place in production
Most AI projects stall between the demo and the real workflow: the model works in a notebook, but nobody has decided who checks its answers, what data it can see, or what happens when it is wrong. That gap is where we spend most of our time.
We build AI into the systems and processes you already run, with evaluation, monitoring and human review designed in from day one, so you can trust it with real work and explain how it behaves.
What good looks like
- Routine decisions and queues handled automatically, with people focused on the exceptions
- Models measured against your own data and success criteria before launch, not after
- A clear fallback and review path for every automated decision
- Accuracy, cost and drift visible on a dashboard rather than discovered in an incident
- Governance your legal, security and compliance teams can sign off on
What's inside Artificial Intelligence
AI-powered automation
Automating decisions and workflows that used to require a person watching a queue.
Applied intelligence & data science
Models built against your own data, not generic off-the-shelf assumptions.
LLM & agent integration
Practical use of language models and agents inside existing systems and support tools.
Predictive analytics
Forecasting demand, churn, and risk from patterns already sitting in your data.
AI readiness & governance
Assessing what's safe to automate, and putting guardrails around what isn't.
Applied AI, end to end
Practical building blocks we combine to fit your workflow. We choose the model and the approach after we understand the problem, not before.
Language models & agents
Assistants, document understanding and agents wired into your tools, with guardrails on what they can see and do.
- LLM APIs & open models
- Retrieval-augmented generation
- Tool-using agents
- Output evaluation
- Document extraction
Machine learning & prediction
Models trained on your own data for forecasting, scoring and classification.
- Python & scikit-learn
- Gradient boosting
- Time-series forecasting
- Anomaly detection
- Feature engineering
Computer vision
Image and document analysis for inspection, estimation and verification workflows.
- Image classification
- Object detection
- OCR & document parsing
- Damage & quality assessment
- Human-in-the-loop review
Data & MLOps
The pipelines and tooling that keep models accurate and repeatable in production.
- Data pipelines
- Model versioning
- Drift monitoring
- Evaluation harnesses
- Cloud ML platforms
Governance & safety
Controls that make AI auditable and safe to run in regulated environments.
- Access & data boundaries
- Audit logging
- Bias & quality testing
- Human approval flows
- Usage guidelines
Engineering discipline, not a pitch deck
We treat AI as an engineering problem before a product decision: data quality, evaluation, and failure modes come first. Nothing goes into production without a clear answer for what happens when the model gets it wrong.
What this can look like
A support team deploys an AI agent to triage and draft responses to routine tickets, freeing analysts to handle the cases that actually need judgment.
From idea to a model you can trust
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01 / 04
Find the right problem
We look through your workflows for tasks where AI genuinely helps, and rule out the ones where a simpler solution is better.
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02 / 04
Prove it on your data
A small, measured pilot on your real data with success criteria agreed up front, so you know what works before investing further.
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03 / 04
Build for production
Integration, evaluation, monitoring and human review designed around the workflow, not bolted on at the end.
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04 / 04
Monitor and improve
Ongoing tracking of accuracy, cost and drift, with a clear process for retraining and updating as the business changes.
The same stages apply to every engagement. See our full process.
Choose the engagement that fits
AI readiness assessment
A short engagement that reviews your data, workflows and risks, and ranks the use cases worth pursuing.
Best for: Leadership teams deciding where to start, or what to stop.
Pilot to production
A scoped build that takes one proven use case from prototype to a monitored production system.
Best for: A specific workflow with a clear business owner.
Ongoing AI team
Engineers and data scientists working your roadmap, extending and operating what is already live.
Best for: Organizations running several AI features.
See it in practice
Building an AI estimator engine for auto and property damage claims
A US-based claims processor handling both auto and property damage claims relied on manual estimation for repair costs, creating long turnaround times and inconsistent estimates between adjusters working comparable damage.
Case study · Auto & Property DamageAn AI auditor engine for catching claims errors before payout
The same claims operation was manually spot-checking only a small percentage of estimates and payouts for errors and irregularities, catching a fraction of the mistakes slipping through a high-volume claims pipeline.
Case study · HealthcareCutting no-shows for a multi-location healthcare practice
A growing outpatient practice group was losing a meaningful share of daily appointment slots to no-shows and manual double-booking across locations, while front-desk staff spent hours a day reconciling schedules across disconnected systems.
Blog · Artificial Intelligence · 5 min readWhy most enterprise AI pilots never reach production
Most AI pilots impress in a demo and die in a backlog. Here's what actually separates the ones that ship from the ones that don't.
Blog · Artificial Intelligence · 6 min readLLMs in production: reliability patterns beyond the demo
A language model that works in a demo and one that works in production are different systems, even when the model itself hasn't changed.
Blog · Operations · 5 min readAutomating the boring parts: a framework for business process automation
Not every manual process is worth automating. Here's how we decide which ones actually are, before we write a line of code.
Frequently asked questions
Do we need a lot of data to get started?
Not always. It depends on the task. Language-model work can often start with the documents and records you already have, while predictive models need enough history to learn from. An assessment tells us which applies before you commit.
How do you stop AI from making mistakes in front of customers?
We design for the failure case first: confidence thresholds, human review for uncertain or high-impact decisions, and monitoring that flags unusual outputs. The goal is a system whose errors are caught and contained, not one that claims to have none.
Will our data be used to train public models?
We choose model and hosting options based on how sensitive your data is, including approaches where your data stays within your own environment. We agree the data handling rules with you before any work begins.
How do you measure whether it is working?
We agree success measures before building, such as accuracy against a labelled sample, time saved or error rates, and we track them after launch so you can see the impact.
Can AI work with our existing systems?
Yes. Most of our AI work is integration: connecting models to the systems, queues and documents your teams already use, rather than asking them to adopt a new tool.
Is AI the right answer for every problem?
No, and we will say so. If a rules-based automation or a better report solves the problem more reliably and for less, we will recommend that instead.
Have a workflow worth automating?
Tell us what your team spends its time on and we'll tell you honestly whether AI is the right tool, and what it would take to do it safely.