One scope, ingestion to interface.
A data product is not finished by being good at either the pipeline or the screen. We take both ends, and everything between them.
The people doing the work.
Hired for what they have already built and run. Some came by way of the places below.
What we take on.
Real-time data pipelines
We build streaming ingestion that keeps its footing: normalisation into one model, back-pressure, replay after an outage, and integrity checks that catch a silent gap rather than averaging over it.
Third-party integration at scale
Rate limits, undocumented spec drift, dropped sockets and partial responses are the normal condition of an external feed, not the exception. We design retries and reconciliation so a replay never double-counts.
Metric and indicator engineering
We define what a figure means, implement it, and decide the thresholds that say which regime it is reporting — validated against history rather than asserted. Derivatives, market structure, on-chain flows, macro and sentiment are one domain we know to that depth.
Blockchain and on-chain indexing
Ingestion from nodes and providers, address labelling, flow computation, and the batch jobs behind it. Recomputation and change history are assumed in the design, so results stay reproducible when a definition moves.
AI features grounded in your data
Summarisation and interpretation integrated into the product, where every generated reading carries the data and the criteria behind it so the output can be checked rather than trusted.
Web and mobile product
Front ends on Next.js and React alongside iOS and Android, including the design systems and chart components that dense data screens require, in light and dark.
Cloud infrastructure and operations
AWS architecture, CI/CD, monitoring and alerting, and performance work through caching, queues and event-driven design — planned for scale, and for the cost of that scale, from the outset.
How an engagement runs.
Assess
We review the requirements and the existing assets, and verify the availability and quality of the data sources first. Feasibility and scope are settled here, before anyone commits to a date.
Design
The data model, pipeline structure, computation logic and screen information architecture are fixed in writing, along with the reasoning and the trade-offs behind each technical choice.
Build
Short cycles, deployed and verified. Code review and PR-based collaboration are the default, and integrity checking carries the same weight as feature work.
Operate and hand over
We hand over with monitoring and incident response already in place, together with the specifications and the runbook, so your team can run it without us.
Only what we actually run.
If you already know the data, we will start by validating it.
An unfinished specification is fine. Checking the sources often clarifies the scope on its own.