Answers while they still matter
Data & Intelligence
The data exists; the answers arrive too late to act on. We build ingestion that keeps up, transformations that enforce a contract, and serving layers that answer in seconds, with ML only where it earns its place.
How we think about it
Most organizations do not have a data shortage. They have a pipeline shortage — data is collected faster than it can be cleaned, joined, and put in front of the people who can act on it. We build the systems that close that gap: ingestion that keeps up with reality, transformation that enforces a contract, and serving layers that answer questions in seconds rather than overnight.
Intelligence is the layer on top. Once data flows reliably, the next question is what it should do automatically — flag anomalies, rank candidates, forecast demand, recommend the next action. We integrate ML where it earns its keep and stop short of where it doesn't. Every model we ship is treated as a production system with monitoring, fallbacks, and a clear definition of when its output should stop being trusted.
- Real-time data pipeline engineering
- Data warehouse and lake architecture
- Analytics dashboards and visualization
- ML model integration and serving
- ETL/ELT process design
- Data governance and quality frameworks
Related disciplines
Most engagements combine more than one of these. See how the rest fit together.
- 01
Platform Engineering
When scale stops being theoretical
The system that runs the business has stopped scaling with it. We design service boundaries, event-driven pipelines, and APIs against their failure modes first, and ship the observability needed to run them.
- 02
Infrastructure
Deployment should be boring
If deploying your software is exciting, something is wrong. Kubernetes, infrastructure as code, CI/CD, and monitoring built so a 2am deploy is a non-event and a blank cloud account becomes your environment again in an afternoon.
- 03
Product Development
Shipped, then kept shipping
The product has to ship, and then keep shipping. Discovery, build, launch, and the releases after it as one engagement: production code, documentation, and a team — ours, yours, or both — that keeps extending it.
Does this fit your problem?
Tell us what you're trying to solve. We'll tell you whether data & intelligence is the right place to start.