Technology Services · Artificial Intelligence
AI and machine learning built to reach production, not just a pilot
Most agentic AI initiatives still don’t make it past the pilot stage — Gartner expects over 40% of agentic AI projects to be cancelled by 2027. We build AI and ML systems designed for production from day one: the right use case, clean data underneath, and guardrails that make autonomy safe to trust.
What we build
AI and ML solutions we deliver.
From predictive models to agentic systems and computer vision — AI built to run in production, not just a demo.
Expert Team & Proven Experience
10+ years in the industry, with 500+ happy clients worldwide.
Predictive & Prescriptive ML Models
Forecasting, recommendation engines, and fraud detection built on models trained for your actual data — not a generic template fitted to your problem after the fact.
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Agentic AI Systems
Autonomous agents that plan and execute multi-step tasks, built with bounded autonomy — clear limits, audit trails, and a human in the loop at the decisions that need one.
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Computer Vision & NLP
Image and video understanding for inspection and recognition tasks, and document or text intelligence that turns unstructured language into something a system can act on.
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AI Integration Into Existing Systems
Embedding AI capability into the CRM, ERP, or legacy platform you already run — so AI becomes a feature inside your workflow, not a separate tool people have to remember to open.
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AI Strategy & Readiness Consulting
An honest assessment of where AI fits your business, where your data isn’t ready yet, and a phased roadmap — before any model gets built.
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Our approach
Built for production, not another pilot
Most organizations aren’t short on AI experiments — they’re short on AI that survived contact with production. We start with a data-readiness check, because most stalled AI projects are actually data problems wearing an AI costume, and we’ll tell you when a simpler rules-based system beats a model. Every agentic system ships with bounded autonomy, audit trails, and monitoring — not just a demo that worked once.
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Governance
Governance built in, not bolted on
Explainability, bias testing, and audit trails are part of the build itself, mapped to relevant frameworks like GDPR, HIPAA, and the EU AI Act where they apply. Agentic systems specifically ship with bounded autonomy and human checkpoints at the higher-risk decisions.
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Our process
From use case to a system you can trust
We assess whether your data and use case are actually ready for AI before committing to a build (1–2 weeks), validate with a focused proof of concept against real data (2–4 weeks), then harden and integrate the system into production in sprints, with 90 days of post-launch monitoring for drift, performance, and bias. This page is the overview — behind it sit dedicated practices for when a project needs deeper focus, including LLM development and generative AI development.
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How we deliver
UK leadership, world-class engineering
Our London team manages everything client-facing, from discovery through to delivery sign-off, while our India team handles the deep technical build — senior oversight without the cost of a fully Western dev team. Your AI infrastructure is built by an AWS and Microsoft Azure certified partner, which means direct platform support, early feature access, and deep cloud expertise baked into every deployment.
Talk to usWhere it works
AI tuned to your industry, not generic.
The same model rarely fits two sectors — we build for the data, constraints, and outcomes yours actually runs on.
Healthcare
Predictive analytics and AI-assisted clinical decision support that personalizes treatment and supports faster, better-informed care.
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Fraud detection and behavioral analytics models accurate and fast enough to act on at transaction speed.
Retail & eCommerce
Personalization and demand forecasting that turns customer data into recommendations people actually respond to.
Learn moreManufacturing
Predictive maintenance and process optimization from IoT and production data, catching failures before they cost you.
By the numbers
A decade of production AI delivery.
10+
Years of proven success
500+
Happy clients worldwide
20+
Products we have built
250+
Technical team members
Explore our AI services
Every AI service we offer, in one place.
This page is the overview of our AI & ML practice — browse the specialized service that fits what you’re building.
Technologies we work with
- Python
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face
- LangChain
- LangGraph
- AWS SageMaker
- Azure AI
- Docker & Kubernetes
FAQ
Frequently asked questions
What we hear most often about AI and ML projects — readiness, governance, and what happens after launch.
What’s the difference between AI, ML, and agentic AI?
AI is the broad field. Machine learning is a method within it — systems that learn patterns from data rather than following explicit rules. Agentic AI goes further: it plans and takes multi-step actions toward a goal with limited human input, rather than just producing an output for someone else to act on.
We’ve run AI pilots that went nowhere — what’s different about working with you?
We start with a readiness audit before committing to a build, and we’ll tell you directly if the blocker is your data, your use case, or the expectations around what AI can actually do — rather than building a demo that looks good once and never reaches production.
Is our data even ready for AI?
Often, not yet — and that’s normal. Data quality and availability are the most common reasons AI projects stall. We assess this honestly up front; if your data needs work first, we’ll say so before proposing a model.
How do you handle AI governance and compliance?
Explainability, bias testing, and audit trails are part of the build itself, mapped to relevant frameworks like GDPR, HIPAA, and the EU AI Act. Agentic systems specifically ship with bounded autonomy and human checkpoints at higher-risk decisions.
Can you integrate AI into our existing systems?
Yes — embedding AI into an existing CRM, ERP, or legacy platform is one of our most common engagements, designed to minimize disruption to systems you already depend on.
What happens after the system is launched?
We include 90 days of post-launch monitoring covering model drift, performance, and bias checks, with tuning based on real usage. After that, we offer optional retainers for ongoing model maintenance and improvement.
From our blog
AI insights from our engineering blog.
Real-world guidance on scoping, costing, and shipping AI projects that reach production — not just a pilot.
Ready to move past the AI pilot stage?
We’ll tell you what’s feasible, what’s missing, and what it will actually take. No obligation.