Deploy enterprise AI that delivers measurable value. From AI readiness diagnostics to production agentic systems, generative AI, machine learning, and intelligent process automation – we build AI that scales with your business.
7 services
A Working, Governed AI Agent in Production. In Weeks, Not Quarters.
Dubai has given its private sector two years to adopt agentic AI. The training on offer will teach your teams what agents can do. It will not get one running safely inside your business. That is the hard part, and it is an infrastructure and governance problem rather than a training one.
The AI Proof Sprint is where you start with something real.
A fixed-price engagement of two to three weeks, delivery-led rather than a slide assessment. We take one high-value, well-bounded process in your business and put a working, governed AI agent into it, with the integrations, guardrails, security, and monitoring in place from day one.
Mid-to-large firms in financial services, professional services, retail, and logistics, where an agent has real operational leverage and running it safely is not optional. If you are weighing what agentic AI looks like inside your business, rather than inside an announcement, this is the way in.
Two years is a countdown, not a press release. The firms that start now will be the ones everyone else spends the next two years trying to catch.
You're rushing into AI without the foundations. If data quality, governance, and culture aren't ready, AI initiatives fail expensively and breed scepticism.
AI needs the right data, infrastructure, governance, and culture. This diagnostic shows exactly what to build before you launch, so you avoid costly failures and set up sustainable success.
Get a first read today with the free AI Readiness Audit – 15 minutes, a maturity scorecard, and a personalised report to your inbox.
Avoid costly AI missteps. Get a clear, fundable roadmap so leaders can prioritise investment with confidence and deliver ROI.
You struggle to find, validate, and scale AI use cases that deliver measurable value – and you get stuck in proof-of-concept purgatory.
A factory approach drives rapid iteration, value validation, and scaled deployment so you ship AI that actually pays off.
Deliver multiple AI use cases that generate substantial value. Escape PoC purgatory and scale what works.
Your processes are still manual and inefficient despite available AI. That creates bottlenecks and puts you behind on cost, speed, and accuracy.
Combine human judgment with AI to unlock efficiency, accuracy, and scalability – and free your people for higher-value work.
Developed ML-powered demand forecasting system with automated reordering and real-time inventory optimisation.
Achieve sizeable efficiency gains in core processes, cut costs meaningfully, and improve speed and accuracy.
Know Exactly What It Takes to Run Your AI Properly – Before You Commit to Anything.
A fixed-fee engagement of two to three weeks. We map your stack, and where model monitoring does not already exist – which is the usual case – we deploy and tune off-the-shelf tooling to your models, in your own cloud environment, so your data never leaves it. Where we did not build the system, this is how we learn to run it.
Organisations with AI or data systems live in production – whether we built them or someone else did. It is also the natural next step when a free AI Health Scan comes back red or amber: the scan shows you the symptom on one model, the audit instruments the estate and confirms what it means.
You decide about the retainer with the facts in hand: what is monitored, what it costs to run, and where the risk sits today.
The Recurring Assurance Layer That Keeps Your AI Accurate, Governed and in Control.
An AI system in production is not a finished thing. It is a living system that decays quietly. Accuracy drifts as the world moves under the model, nothing crashes, and the first anyone hears of it is a bad decision, a complaint, or a regulator's question.
We built it, or we adopt what you have built. Then we run it – on an SLA, with a named owner, and evidence you can show your own risk function.
A managed service built on two layers. The platform layer – infrastructure, pipelines, uptime, cost – is the floor. The ModelOps layer is the product: model accuracy and performance, data and concept drift, retraining, eval scoring, guardrail and RAG monitoring, lineage and model cards. Generic managed DevOps watches the infrastructure; we watch the thing that actually degrades.
Every engagement opens with the fixed-fee Onboarding & Assurance Audit above: two to three weeks to map the stack, deploy and tune monitoring in your own environment, and set the SLA baseline – with part of the fee credited against the retainer on conversion. Not sure you need any of this yet? Start with the free AI Health Scan on one model.
Mid-market organisations with AI or data systems live in production – whether we built them or someone else did. Two tiers: Foundation, business-hours monitoring of a small model estate with a monthly assurance pack, and Managed, extended cover with a defined incident SLA, a monthly retrain cadence, and the committed evolve block.
Your AI stays accurate, governed and provably in control – continuity and accountability, evidenced monthly, instead of an incident explained after the fact.
You need 24/7 intelligent automation that traditional software can't deliver, and hiring in-house AI expertise is costly and complex.
AI agents run business functions autonomously, deliver consistent performance, and free your team for strategic, creative, relationship-focused work.
Deliver notable productivity gains and reduce manual workload, freeing high-value staff for strategic work while maintaining round-the-clock operations.
The questions leaders ask when they are weighing up who runs a live AI system. Direct answers, no hedging. Open one to read in full.
They can keep it up. The harder question is whether they can tell you the day the model starts getting less accurate, and prove to your auditor that it is behaving. Diagnosing drift is a specialist, part-time need – awkward to hire for, wasteful to keep on a bench. Managed ModelOps is that skill on tap, with the evidence produced monthly.
Keep them. They watch the infrastructure – uptime, pipelines, cost. We watch the model itself: accuracy, drift, evals, and guardrails, the layer that degrades invisibly while the dashboards stay green. The two services sit alongside each other, and ours is the one a hosting provider cannot credibly offer.
A model that is working today is quietly becoming a different model as the world moves underneath it. Nothing crashes. By the time degradation shows up in the business, it has already cost you – in bad decisions, complaints, or a regulator’s question. The retainer is the difference between catching drift early and explaining an incident afterwards.
Fixing issues as they arise means paying after the damage is done, at incident prices. The retainer also carries its own offset: inference and compute spend drifts upward when nobody owns it as a discipline, and the FinOps work inside the service claws a good share of that back. The engagement starts with a fixed-fee audit rather than a long commitment, so you see the run-readiness picture and the running costs before anything ongoing is signed.
Yes – that is exactly what the Onboarding & Assurance Audit is for. Over two to three weeks we map the stack, deploy and tune model monitoring in your own environment, and set the SLA baseline. We learn to run the system before either side commits to the retainer, and part of the audit fee is credited when you convert.
Send us a brief and we'll come back within one working day with a senior AI & Automation consultant – and a clear sense of how to start.
