Capability
Data Platforms
Operational decisions require reliable and structured data flows. We design and implement platforms that support critical data operations.
Data platforms should make data useful and trustworthy inside operational systems, not just available for analysis after the fact.
Typical work includes
Data pipelines
Ingestion, transformation, and delivery with defined schedules, error handling, and lineage, so the systems downstream can state where their data came from and when.
Operational data stores
Data structured for the systems that act on it during business operations, at the latency and consistency the workflow actually requires rather than the strictest available.
Real-time and batch processing
Streaming where a decision cannot wait, batch where completeness matters more than immediacy, within one platform rather than two disconnected estates.
Data integration across systems
Reconciling records that exist in several systems under different identifiers, with explicit rules for precedence and for what happens when sources disagree.
Data quality and governance
Validation, ownership, and shared definitions applied where data enters the platform, so quality is a property of the pipeline rather than a recurring clean-up exercise.
Analytical and operational data environments
Serving reporting and live operations from one governed foundation, arranged so that neither workload destabilises the other under load.
Our focus is on making data useful and trustworthy inside operational systems.
Let's talk about your challenge
If your organization is working with complex digital systems or exploring operational AI, we are always open to a conversation.
proof and reading
Related cases and insights
Related cases
- Secure RAG System
Secure retrieval architecture for trusted, role-aware access to internal knowledge.
Further reading
- Why AI pilots stall before production
A working demo is not a working system. The difference between AI that ships and AI that stalls is operational integration, not model quality.
- The EU AI Act: what it asks of deployers
Most organizations buying or building on AI are deployers rather than providers. That distinction decides which obligations land on you, and most of them are architectural.
- What the Open Knowledge Format does
A directory of markdown files with YAML frontmatter, published by Google Cloud as an open specification. What OKF requires, what its trust fields record, and which of your problems it leaves untouched.
- OKF and RAG in the same system
The Open Knowledge Format describes a corpus. Retrieval finds things in one. What each contributes when they run together, what the pair does that neither does alone, and how to tell which of the three shapes your problem needs.
- Your AI needs playback—not a blockchain
A trustworthy AI system should not merely retain an answer. It should let you return to that interaction and inspect the evidence, decisions, configuration, and controls behind it.
faq
Frequently asked questions
- What makes a data platform operational rather than just analytical?
- An operational data platform feeds reliable, structured data into the systems that run the business in real time, not only into dashboards consumed after the fact. The emphasis is on data quality, governance, and trustworthy flows inside live workflows.
- Do you support both real-time and batch data processing?
- Yes. We design data pipelines and operational data stores that combine real-time and batch processing as the use case requires, with data quality and governance built in across both.
- How is a data platform different from a data warehouse?
- A warehouse is optimised for analysing what has already happened. A data platform also serves the systems acting during operations, which changes the requirements: latency, availability, and correctness in flight rather than correctness at rest.
- How do you approach data quality?
- By validating where data enters the platform and giving every dataset an owner and an agreed definition. Quality enforced at the boundary stays fixed. Quality corrected downstream has to be corrected again on the next load, by whoever notices.
- How is personal data handled under GDPR in a data platform?
- Purpose, retention, and access are modelled in the platform rather than documented beside it: which datasets hold personal data, on what basis, for how long, and who can reach them, with deletion that propagates through derived data instead of stopping at the source table.
- Can a data platform be built incrementally?
- Yes, and usually it should be. Starting with one operational flow that genuinely matters proves the pipeline, the governance model, and the ownership arrangement against something real, and gives every later flow a pattern to follow.