AI development services that earn their keep.
Custom chatbots, predictive analytics, visual recognition, and enterprise AI applications — engineered to move the metrics that matter, not just demo well. 8,000+ projects delivered since 1998.
Four disciplines, one coherent practice.
Strategic planning through production deployment — each capability compounds with the others. Explore the full breadth of our AI & ML engineering practice for cross-cutting tooling and methodology.
Strategic AI planning & roadmap
Before writing a line of code, we map your data assets, business objectives, and risk tolerance to an AI programme that ships in quarters — not years. Architecture decisions that compound.
Custom chatbots & conversational AI
LLM-backed assistants tuned to your domain, tone, and workflow. Support deflection, sales qualification, internal knowledge — shipped with guardrails your compliance team can defend.
Advanced predictive analytics
Demand forecasting, churn prediction, fraud detection, and dynamic pricing — models trained on your historical data, served in production, monitored for drift.
Visual recognition & computer vision
Object detection, OCR, defect classification, AR overlays — computer-vision pipelines calibrated to your image distribution and deployed where latency matters.
AI tuned to your vertical.
Generic models underperform in specialized domains. Our industry teams bring prior art — trained data sets, regulatory patterns, and production benchmarks — so you start ahead. See how we apply this across our full industries practice.
Healthcare
Diagnostic imaging assistance, clinical NLP for unstructured records, patient-flow prediction, and drug-interaction screening — built to HIPAA standards.
Retail & e-commerce
Personalization engines, visual search, inventory optimization, and dynamic markdown pricing — the difference between a 2% and a 12% conversion lift.
Finance
Fraud detection, credit-risk scoring, regulatory-document parsing, and algorithmic trade signals — models that survive regulatory scrutiny and adversarial inputs.
Manufacturing & supply chain
Predictive maintenance, quality-control vision, demand sensing, and supplier-risk monitoring — keeping lines running and costs predictable.
Frameworks we run in production.
We're not framework-agnostic as an excuse — we're fluent across the AI/ML stack and match the tooling to the problem. The right model architecture beats the fashionable one.
- TensorFlow
- PyTorch
- Scikit-learn
- Keras
- Apache SystemML
- Caffe
- H2O
- Google ML Kit
- MxNet
- OpenNN
- LangChain
- OpenAI API
The compounding advantage of 28 years.
Institutional memory, domain depth, and full-stack ownership — built up across 8,000+ projects with enterprise clients across six continents. That compounds on every new engagement.
28 years of production-grade delivery
Operating since 1998. We've shipped 8,000+ projects across 92+ countries — the kind of institutional memory that prevents the mistakes newer shops repeat.
Models that earn, not demos that impress
Every AI engagement is wired to a business metric — uplift, cost-per-unit, or retention rate. If it doesn't move the number, we don't ship it.
Full-stack AI ownership
Data engineering, model development, serving infrastructure, monitoring — one team owns the pipeline so there are no integration gaps between disciplines.
Domain depth across verticals
Healthcare, retail, finance, logistics — we bring prior art from 3,000+ active clients so your project starts from a pattern, not a blank board.
Compliance-aware by default
GDPR, HIPAA, SOC 2, regional AI regulations — governance and auditability are designed in from the data model, not retrofitted under a deadline.
IP stays with you
All model weights, training artefacts, and eval sets transfer to the client at engagement close. No vendor lock-in, no licensing surprise.
Straight answers on AI delivery.
QHow long does it take to go from idea to a production AI model?
QDo you work with our existing data infrastructure?
QWhat happens when the model drifts in production?
QCan you build AI features into our existing product?
QHow do you handle AI explainability and regulatory requirements?
Let's build AI that earns its keep.
One discovery call, a data audit, and a working proof-of-concept inside four weeks. From there, full production delivery using our battle-tested method from 8,000+ projects.