Turn AI into results

AI that runs in production.
Not just in demos.

We design and build production-grade AI systems on Databricks from GenAI and LLM applications to MLOps-managed forecasting and decision engines. Embedded in your business workflows. Measured against real outcomes.
Talk to an expert
See it in action
WE'VE SEEN THIS BEFORE.

Most AI initiatives fail when they meet production.

The experiment works. The demo impresses the room. Then the hard part starts with integrating into live systems, handling real data volumes, maintaining model performance, and convincing the business to actually use it. Most organisations have more AI projects than AI results.
Any of these sound familiar?
Pilot Purgatory
"We've run three AI POCs. None of them made it to production."
Experimentation without a production pathway is an expensive way to learn what doesn't work. The jump from a working prototype to a system that runs reliably in a business workflow requires a different architecture, a different team, and a different way of thinking about the problem from the start.
AI Platform & Governance
"We have data, but we don't know how to expose it safely to AI."
A model in production is not the same as AI creating value. If the output isn't trusted, integrated, and acted on, the system exists on paper. Real adoption requires the AI to fit into how people already work. Not demand that people change how they work to fit the AI.
No Idea If It's Working
"We have a model running. We don't know if it's making a difference."
AI without observability and measurement is a black box with a dashboard on the front. Without tracking model performance, business KPIs, and operational impact over time, there is no basis for improvement and no argument for further investment.
The Approach

We design AI systems for the day after launch. Not just the demo.

Every AI system we build starts with one question: how does this run in production, six months from now, without us in the room? That means architecture designed for real data volumes, integration into existing business workflows, compliance and governance built in from the start, and a clear operating model your team can run.

+

AI Strategy and Advisory

Decide what to build first. Facilitated workshops, use case discovery, a scored priority matrix, and a phased roadmap you can take to your leadership team.

AI workshops
Governance design
Cost modeling
+

End-to-end AI Engineering

Build the system. AI assistants, RAG, agentic AI, internal copilots, and workflow automation, engineered for production on Databricks from day one.

Agentic AI
Internal co-pilot
+

Trusted AI Platform

Govern what runs. Unity Catalog, AI Gateway, structured evaluation, monitoring, and deployment controls so AI holds up under audit, not just under demo.

Operational handover
Documentation
Team enablement
+

AI Accelerators

Don't start from a blank page. Production-tested accelerators configured to your data and your terminology, with pilots measured in days rather than months.

Operational handover
Documentation
Team enablement
See It in Action

This is what production AI looks like.

Two end-to-end AI systems built on Databricks. Not concepts or wireframes. Running systems, designed for the complexity of a real business environment.
Manufacturing & Supply Chain
Real-Time Supply Chain Intelligence on Databricks
Live shipment tracking across 51 sea lanes and 41 ports. Automated disruption detection, production impact tracing from vessel to factory floor, and rerouting in under 200 milliseconds.
  • Disruption-to-response in under 200ms
  • Unified view: shipments + production demand
  • Simulation engine for scenario planning
  • Works across ocean, air, rail, and road freight
See it in action
Financial Services
Hyper-Personalisation in Banking
End-to-end Next Best Action engine for banking advisors. ML propensity models, GenAI-generated personalised messages, adversarial AI compliance filtering, and Databricks Genie all in one relationship portal.
  • Individual-level recommendations, not segments
  • GDPR-compliant adversarial AI message filter
  • Multi-channel: email, push, advisor script
  • Databricks Genie for advisor Q&A
See it in action
THE ENGAGEMENT

From use case to production
in a defined engagement.

Every AI engagement starts by identifying where AI can create measurable impact, not by choosing a model. The scope is defined upfront, the architecture is approved before a line of code is written, and the engagement ends with a system your team can operate independently.
01
Discover
We identify the highest-value, technically feasible AI use case for your business and data environment. Not based on what's fashionable, based on where AI changes a real decision, workflow, or output your business depends on.
01
Design
AI system architecture designed for production: data flows, model selection, integration points, compliance and governance requirements, operating model. Reviewed and signed off before build begins.
01
Build
Development of the AI system against the approved architecture in iterative, testable sprints. Data pipelines, model training, API integrations, frontend surfaces, adversarial review layers, and MLOps/LLMOps infrastructure built to production standards.
01
Deploy
Production deployment with your team involved throughout. Performance and business impact baselines set before go-live. Observability, alerting, and retraining triggers configured. The system goes live when it has proven it can hold.
01
Operate
Knowledge transfer, runbooks, and an operating model your team can follow independently. Monitoring thresholds, retraining schedules, and escalation paths documented. The engagement ends when your team can run, improve, and extend the system, not when the build is committed.
AI Systems in Production
79
Typical Build time
8 weeks
Projects on Databricks
200+
Ready to talk?
Talk to an expert
Built on Databricks

One platform.
The full AI stack.
No parallel infrastructure.

Every AI system we build lives entirely on Databricks. That means the data the models train on, the pipelines that feed them, the models themselves, and the operational layer that runs them in production are all governed, monitored, and maintained in one place. No brittle integrations. No second security model to maintain. One audit trail, end to end.
Delta Lake · MLflow · Unity Catalog
Data + AI on one foundation.
Delta Lake governs the training data. Unity Catalog applies the same access controls to the data the models see as to the data the business sees. MLflow tracks every experiment and model version. The foundation that powers the AI is the same one the rest of the platform runs on, no ETL between analytics and ML.
Databricks Lakebase
Real-time AI at production speed.
Lakebase - managed PostgreSQL inside Databricks -  provides the transactional, low-latency operational layer production AI requires. No separate database to stand up. Live model output, journey state, frequency caps, and trigger events managed in milliseconds within the same governance perimeter as the rest of the platform.
Delta Lake · MLflow · Unity Catalog
Conversational AI grounded in your data.
Genie gives business users - advisors, planners, analysts - natural-language access to the AI system's outputs and underlying data, without writing SQL or switching tools. Scoped by Unity Catalog. Auditable by design. The same governance that protects production data also governs what questions each user can ask.
Partnership Tier
Gold Partner
Training Status
Super ATP
Projects Delivered
200+
Years with Databricks
10+
Ready to talk?
Talk to an expert
Results

What migrations deliver in practice

TechnoAlpin

From snow guns to smart systems: TechnoAlpin’s data transformation with Datapao

When we ski down a perfectly groomed slope in the early morning, we rarely think about the streams of data pulsing behind the scenes.
Timeline
Unified data platform live in under 12 weeks
Enablement
Internal team fully enabled to operate independently
Performance
60% reduction in pipeline processing time
Read the whole study
Manufacturing
Good to Know

Common questions before getting in touch.

Moving AI from experimentation to production raises a lot of questions. Here are the ones we hear most often from teams ready to turn their AI investments into real business results.

What types of AI solutions can you build on Databricks?

We design and build GenAI applications, AI agents, RAG systems, internal copilots, intelligent workflow automation, predictive models, and ML-powered decision engines. From conversational analytics to automated business processes, we focus on AI solutions that solve specific problems and integrate into how your organisation actually operates.

Do we need to have a fully developed AI strategy before working with you?

Not at all. If you know AI could create value but aren't sure where to start, we can help identify and prioritise the right opportunities. Through discovery workshops, technical assessments, and business impact evaluation, we develop a practical AI roadmap focused on achievable outcomes rather than technology for its own sake.

Can AI solutions integrate with our existing systems and workflows?

Yes. AI only creates value when people can actually use it. We design integrations with your existing applications, data sources, APIs, and business processes, so AI becomes part of everyday operations rather than another disconnected tool. Whether it's supporting customer service, automating decisions, or providing real-time recommendations, we build around how your business works.

Do we need to use Databricks to work with you on AI?

Our AI solutions are built on Databricks, bringing data engineering, machine learning, GenAI, and governance together on one platform. If you're already using Databricks, we can build on your existing environment. If you're not, we can assess your current architecture and help you determine the right path to implementing AI on Databricks.

Your next AI project deserves to make it to production.

Whether you're exploring new AI opportunities, stuck at the PoC stage, or ready to scale, we'll help you build AI that works in the real world and delivers measurable business value.
Services
Experience
Resources
Careers
Company
Legal information
GDPR