AI that does the work, not just the talking.
We design, build and run production AI: agents that take action inside your systems, assistants that answer from your own data, and models that predict what happens next. Engineered with human oversight from the first sprint, and built to keep working where the connection drops.
Everyone is piloting AI. Very few have it working.
The models have never been better. What separates a demo from a dependable system is engineering: clean data, evaluation, integration and oversight. That gap is the work we do.
17%
of organisations have actually deployed AI agents, while more than 60% expect to within two years.
36%
of companies have no formal plan for supervising the AI agents they are already running.
23%
of executives report significant ROI from AI agents. For generative AI overall, the figure is 29%.
3 in 4
enterprises say they are adopting agentic AI. Only a small minority run it in meaningful production.
Authenticity in action
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No business case. A clever demo with no number attached to it.
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Messy data. The model is fine; the inputs are not.
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No evaluation. Nobody can say whether version two is better than version one.
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No oversight. Agents act, and nobody owns what they did.
We design against all four from day one.
Six ways we put AI to work.
Pick a capability to see what it looks like in a real business, what we build it with, and what you walk away with.
Assistant, model or agent? Pick the right tool.
Most failed AI projects used the wrong kind of AI for the job. This is the first conversation we have with every client.
Assistant
Answers and drafts
Predictive model
Scores and forecasts
Agent
Plans and acts
Best for
Best for
Best for
Knowledge lookup, drafting, summarising
Maintenance, demand, risk, anomalies
Multi-step workflows across systems
Risk
Low: a person reads every output
Time to value
Fastest
Risk
Medium: depends on data quality
Time to value
Needs history to learn from
Risk
Higher: it takes actions
Time to value
Highest payoff, needs guardrails
Built for the sea, by people who have worked on it.
Shipping is one of the least digitised major industries, which makes it one of the biggest opportunities. With a Master Mariner and former Salvage Master in our leadership, we know which problems AI can genuinely solve on board, ashore and on a live operation, and which ones it cannot yet.
01
Predictive maintenance
Engine-room and machinery sensor data turned into early warnings, so crews act on condition rather than the calendar.
03
Compliance and document AI
Certificates, surveys, crew records and charter documents read, checked and tracked automatically.
02
Voyage and fuel optimisation
Weather, routing and consumption models that support CII, EU ETS and FuelEU Maritime reporting, not just cheaper passages.
04
Salvage operations intelligence
Offline-capable situational dashboards, drone and imagery analysis, and reporting copilots for the team on site.
The honest caveat: maritime AI is only as good as the data feeding it. Patchy sensor feeds or delayed AIS data will sink any model, so every maritime engagement starts with a data audit, not a demo.
Five stages. Each one ends with a decision.
Every stage has an exit gate. If the numbers do not justify the next step, we tell you, and you have spent the least possible to find out.
Discover
Prove
Evaluate
Pilot
Scale
Map the workflow, size the prize and choose the right kind of AI.
Audit the data and build a working prototype on real inputs.
Build the test suite before the product, so quality is measured, not assumed.
Run in production with real users and a person approving key actions.
Roll out, monitor drift and cost, retrain and widen autonomy as trust is earned.
Gate: a business case with a target number
Gate: prototype hits the agreed accuracy
Gate: passes evals and red-team tests
Gate: measured impact against the baseline
Ongoing: monitoring and support
Guardrails built in, not bolted on.
An AI system you cannot explain, audit or switch off is a liability. These are standard on every build, not extras on a quote.
Human in the loop on every action that matters, with clear approval steps.
Evaluations and red-teaming before launch, including prompt-injection tests.
Audit logs of every prompt, tool call and decision.
Your data stays yours. Private deployments and data residency on request; never used to train public models.
Model-agnostic architecture, so you can switch providers without a rebuild.
EU AI Act: where things stand
Relevant if you serve EU customers, whether you are based in the UK, India or elsewhere.
Feb 2025
Prohibited AI practices banned; AI literacy duties apply
Aug 2025
Rules for general-purpose AI model providers apply
Aug 2026
Transparency duties (Article 50) in force: chatbots and AI-generated content must be disclosed
Dec 2027
High-risk system obligations (Annex III), deferred by the Digital Omnibus
Aug 2028
High-risk AI embedded in regulated products (Annex I)
We design for these obligations from the start, so compliance is a checklist, not a rebuild. This is general information, not legal advice. Sources: Gibson Dunn, Cloud Security Alliance.
We pick the model for the job, not the other way round.
No vendor lock-in. We benchmark the options against your task, budget and data rules, and design so the model can be swapped as the market moves, which in AI is every few months.
Models
Frameworks
Platforms
Data
Ops
Start small. Scale what works.
AI readiness sprint
A short, fixed-scope engagement to map your use cases, audit your data and leave you with a prioritised roadmap and business case.
Best if you are asking “where do we start?”
Build and launch
We take one high-value use case from prototype to production, with evaluation, oversight and handover included.
Best if you know the problem and need it solved
Embedded AI team
Our engineers work inside your team on an ongoing basis, shipping new capabilities and keeping existing ones healthy.
Best if AI is becoming core to your product
