Technology Services · Data Analytics and Architecture
Data analytics and AI-ready architecture
Most enterprises have already modernized something — a warehouse migration, a lake, a mesh pilot. The real question isn’t which architecture is best. It’s whether what you’ve built can govern itself, control its own cost, and actually serve AI. We design lakehouse, governance, and AI-readiness as one connected platform, not three separate projects — built by a team that’s shipped data platforms for over a decade.
What we build
Data analytics and AI/ML solutions we deliver.
Data architecture, governance, and AI/ML pipelines sit alongside our data reporting practice in the stack we list across our own published case studies — not a technology we’re learning on your project.
Expert Team & Proven Experience
10+ years in the industry, with 500+ happy clients worldwide.
Lakehouse Architecture
We build on the lakehouse pattern — open table formats like Apache Iceberg and Delta Lake underneath, so you get warehouse-grade transactions and lake-grade flexibility without copying data between systems.
Federated Data Governance
Central teams set the hard boundaries on security and compliance (GDPR, HIPAA, CCPA); domain teams own and ship their own data products on top — with full lineage and cataloguing throughout.
AI/ML & Vector Pipelines
We architect pipelines that extract embeddings from unstructured content and serve vector search inside the same query engine as your SQL, so an AI agent and a BI analyst rely on the same trusted data.
Real-Time Streaming
Event-driven pipelines that move data as it happens, so dashboards, alerts, and automated decisions reflect what’s true right now — not what was true at midnight in last night’s batch job.
BI & Decision Intelligence
Dashboards and reporting layers built on top of governed, trusted data — so the numbers a BI analyst sees are the same numbers your AI agents and applications are querying.
Cloud & Multi-Cloud Engineering
AWS and Azure-certified architecture and migration work, including zero-copy, federated access across clouds and regions, so your data estate isn’t locked to a single vendor.
Our approach
A decade of data platform delivery, led from the UK
Building and shipping data platforms since 2013, across 500+ clients in the UK, US, Europe, Japan, the Middle East, and Asia — we’ve already solved most of the classic data-platform production problems before they cost you time and money. Our London team manages everything client-facing, from discovery through delivery sign-off, while our engineering team in India handles the deep technical work: senior oversight without the cost of a fully Western dev team.
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Foundation first
One platform for lakes, warehouses, and AI
The old debate between data lakes and data warehouses is largely settled. We build on the lakehouse pattern — open table formats like Apache Iceberg and Delta Lake underneath, so you get warehouse-grade transactions and lake-grade flexibility without copying data between systems. That means ACID transactions on object storage, not just inside a database, and zero-copy, federated access across clouds and regions.
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Governance that enables, not restricts
Federated governance, not a bottleneck or a free-for-all
Fully centralized governance creates bottlenecks; fully decentralized governance creates chaos. We implement federated governance instead: central teams set the hard boundaries on security and compliance — GDPR, HIPAA, and CCPA controls enforced automatically — while domain teams own and ship their own data products on top, with full lineage and cataloguing for every dataset, not just the ones someone remembered to document. That groundwork is also where our data profiling and maturity work usually starts.
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Built for AI, not just BI
Data built to be used by AI, not just stored for it
Most data wasn’t designed with AI in mind — it was designed backward from a dashboard. We architect pipelines that extract embeddings from unstructured content — documents, images, recorded calls — and serve vector search inside the same query engine as your SQL, so an AI agent and a BI analyst can rely on the same trusted data. Lineage and access control extend to the AI agents querying that data, not just to the people who log in.
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Delivery, cloud & real-time
Real-time architecture, delivered through a clear process
A nightly batch job is a dated answer to how businesses actually need to run today, so we build event-driven pipelines that move data as it happens — architecture that scales from gigabytes to petabytes without a rebuild. The platform behind it is built by an AWS and Microsoft Azure certified partner, giving direct platform support and deep cloud expertise on every deployment. We start with 1–2 weeks of discovery and an architecture audit to find the real bottleneck — integration, governance, scale, or AI-readiness — move into 1–3 weeks of reference architecture and roadmap planning, build and migrate in sprints with weekly progress reports, and follow every launch with 90 days of active operation: cost, performance, and governance tuned against real usage data, not theoretical capacity planning.
Book a discovery callWhere this works
Built for data that actually matters to the business.
Healthcare
Personalized treatment plans and clinical decision support from predictive analytics.
Financial Services
Real-time fraud detection and customer behavior insight at transaction speed.
Retail & eCommerce
Personalized shopping experiences and inventory optimized against real demand.
Manufacturing
Predictive maintenance and smart production from IoT and process data.
Telecommunications
Real-time network optimization and churn-driving customer insight.
By the numbers
A decade of proven data platform delivery.
10+
Years of proven success
500+
Happy clients worldwide
20+
Products we have built
250+
Technical team members
Technologies we work with
- Apache Iceberg
- Delta Lake
- Snowflake
- Databricks
- Apache Spark
- Apache Kafka
- Apache Airflow
- dbt
- Pinecone
- Collibra
- Power BI
- AWS
- Azure
FAQ
Frequently asked questions
What we hear most often about data platform projects — lakehouse vs. mesh, AI-readiness, and what happens after launch.
What’s the difference between a data lake, warehouse, and lakehouse?
A warehouse is structured and fast but rigid. A lake is flexible but often ungoverned. A lakehouse combines both — open table formats like Iceberg or Delta Lake give you warehouse-grade transactions on lake-style storage, so you stop choosing between flexibility and performance.
Do we need a data mesh, or is that overkill for us?
Often, yes, it’s overkill. Data mesh makes sense when you have multiple mature domain teams that genuinely need to own their own data products. A well-governed centralized lakehouse can serve a smaller organization perfectly well for years at a fraction of the cost and complexity.
How do you make existing data AI-ready?
We build embedding pipelines for your unstructured content and add vector search alongside your existing SQL layer — inside the same query engine where possible — so AI features query the same governed, trusted data your BI tools already use.
Can you work with our existing cloud and data stack?
Yes. We regularly build on top of existing AWS, Azure, or GCP investments rather than recommending a platform migration as the default first move.
How do you handle data governance and compliance?
Through federated governance: central policy for security and regulatory compliance (GDPR, HIPAA, CCPA), with domain teams owning their own data products on top — full lineage and cataloguing throughout, not just for the systems someone remembered to document.
What happens after the platform is live?
We include 90 days of post-launch support covering performance tuning, cost optimization, and governance refinement based on real usage. After that, we offer optional retainers for ongoing platform evolution.
Ready to make your data actually AI-ready?
Start with an honest assessment of what you already have — not a sales pitch for whatever’s trending this quarter.