Software & cloud
AI, data and analytics that people in the business actually use
Data platforms, pipelines and AI assistants for enterprises: from raw events to a governed lakehouse, dashboards, chatbots and models your teams trust.
- Data sources and owners
- Decisions the data should support
- Volumes and freshness needed
- Security and governance rules
- Batch and streaming pipelines
- Lakehouse with medallion layers
- BigQuery and warehouse models
- Dashboards and an in-browser analytics bot
- AI assistants, document AI and matching
- Interfaces
- Python
- pandas
- Spark
- Event streaming
- BigQuery
- Graph DBs
- WebAssembly
- LLMs
- Applies to
- Data platforms · Analytics and BI · AI assistants
- Engagement
- Data health check · Accelerator deployment · Data pod
We build data-intensive applications and jobs for enterprise teams: batch and streaming pipelines, event-driven systems, and lakehouses organised in bronze, silver and gold layers so every number has a lineage. On top of the data we put the tools people use every day: analytics that answer questions in plain language, AI assistants that read documents and contracts, matching engines that clean up catalogues, and segmentation that tells marketing who to talk to. Our own accelerators (Idea Flow, AI Chatbot, AI Desk, an in-browser analytics bot built on WebAssembly, and item matching) mean most projects start from working software, not a blank page. Models are evaluated on your data, and governance is designed in from day one.
Why teams call us
- 01
Data in many places, numbers that don't match
One governed lakehouse with lineage from source to dashboard.
- 02
Reports that take days
Pipelines that refresh on schedule or in real time, and analytics anyone can query.
- 03
AI pilots that never reach production
Assistants grounded on your data, evaluated, governed and monitored.
- 04
Messy catalogues and duplicate records
Matching that combines rules, fuzzy search and ML, with a human review queue.
How we work
From first workshop to running in production.
- STEP 01
Map the data
Sources, owners, quality and the decisions the data should support.
- STEP 02
Design the platform
Ingestion, medallion layers, storage, security and cost model.
- STEP 03
Build pipelines and models
Tested jobs, data contracts, evaluation on your data.
- STEP 04
Put it in people's hands
Dashboards, chat and assistants, with training and monitoring.
What you get
Deliverables, not slideware.
- Pipelines and data platform
- Batch and streaming, documented and monitored
- Lakehouse / warehouse
- Bronze, silver and gold layers with access controls
- Analytics and AI tools
- Dashboards, analytics bot, chatbots and assistants
- Models and evaluation
- Segmentation, matching and forecasting, with accuracy reports
Technology
No stack limits. Right tool for the problem.
Processing
Streaming
Storage
Graph & logs
AI
In the browser
Need something not listed? We hire for it or bring in a specialist. The problem picks the stack.
One version of the truth
Every metric traceable to its source
Answers in minutes, not days
Self-service analytics and assistants
AI you can govern
Evaluated, monitored and within your data rules
AI, data & analytics: common questions
Do you work with our existing warehouse?
Yes. We extend the warehouse or data lake you already run rather than replacing what works.
Where does our data go when we use AI?
It stays in your cloud. We use private endpoints and keep prompts and documents within your controls.
How does the in-browser analytics bot work?
It runs a database engine in the browser with WebAssembly, so quick analysis on a file or extract happens on the user's machine without uploading it.
Engagement models
Ways to work with us
Data health check
assess sources, quality and architecture in 2–3 weeks
Accelerator deployment
Idea Flow, AI Desk, chatbot or analytics bot on your data in 4–6 weeks
Data pod
engineers who build and run your pipelines, lakehouse and models
Where this sits in the stack
Six blocks on one die. One team across all of them.
We work across .NET, C, C++, MFC, Go, Java, Python, Node.js, React and Spark, on GCP, AWS and Azure, plus embedded C on RTOS and embedded Linux. Pick a block to see where it fits.
Hover, tap or tab through the blocks. Lit copper shows which blocks work together.
Block · Data
Pipelines that turn events into decisions.
- Spark
- Python
- Go
- BigQuery
- PostgreSQL
- Event streams and pipelines
- Warehousing and reporting
- MIS and command dashboards
- Data models for stores, bins and assets
Where it shows up
Scope your AI, data & analytics project
Tell us the problem, the constraints and the deadline. A senior engineer replies within one business day.