Data that arrives while it still matters.
If your dashboards describe yesterday and your integrations run at 2am, your business is making decisions on stale data. We replace batch with streams.
We build event backbones: Kafka estates, change data capture from systems you cannot modify, and event-driven integration between services that were never designed to talk to each other. The result is one well-governed stream of business events instead of a web of point-to-point feeds — and data freshness measured in seconds.
- 01 Kafka platform design, build, and operation
- 02 Change data capture from legacy databases
- 03 Event-driven architecture and domain event design
- 04 Schema governance and evolution strategy
- 05 Stream processing and materialised views
- 06 Batch-to-streaming migration
- 07 Exactly-once-effect pipelines (idempotency by design)
- 08 Cross-system integration without touching legacy code
Discover
We inventory data flows and their actual latency, then pick the one stream whose freshness is worth the most to the business.
Build
The backbone goes in alongside existing batch — both run until the stream has proven itself. Consumers migrate one at a time.
Run
Lag monitoring, schema registry discipline, and capacity runbooks handed to your team — a streaming estate that does not need us to babysit it.
Our core system is a legacy database we cannot change. Is that a problem?
No — it is the most common starting point. Change data capture reads the database’s own transaction log, so we can publish its changes as events without modifying a line of its code.
Do we need Kafka, or is this over-engineering?
Sometimes you do not need it, and we will say so in discovery. If your integration count is small and latency tolerance is hours, a simpler design wins. Kafka earns its keep when many consumers need the same data fresh.
How do you handle exactly-once processing?
We design for exactly-once effect, not exactly-once delivery: idempotent consumers, deduplication keys, and transactional outboxes. It is the difference between a system that is correct and one that is usually correct.
Who runs the cluster afterwards?
Your choice: managed services (MSK, Confluent, Pub/Sub) where operations are mostly procurement, or self-managed where we train your team and leave runbooks. We are deliberately unopinionated about vendor versus self-hosted.
Who owns the IP?
You do — connectors, schemas, processors, and documentation are all assigned to you on payment.