Data Engineering & AI/ML
The layer between raw data and a decision someone can actually trust.
What this department covers
Raw data — sensor streams, telemetry, application logs — is not the same thing as usable data, and a model that performs well in a notebook is not the same thing as a model running reliably in production. Data engineering is the work of building the pipeline in between: ingesting, cleaning, and structuring data reliably enough that a model or a dashboard can depend on it, and then getting that model into production with the same reliability standard as the rest of the system. We do both, which matters because most AI/ML projects fail not on the modeling but on the data pipeline underneath it being an afterthought.
This department overlaps directly with our real-time systems work — a drone's telemetry stream, a fleet's tracking data, or a factory sensor's output is exactly the kind of raw, high-volume, real-time data that eventually needs to feed a model or a predictive system, not just a live dashboard. Building the ingestion pipeline and the eventual ML layer as one connected system, instead of handing data off between separate teams, is what keeps a model's input data as reliable in production as it was in the training set.
Where this shows up
Ingesting and processing live data — sensor streams, telemetry, application events — reliably enough to build on.
Getting a model from notebook to production inference, with the same reliability standards as the rest of the system.
Structuring and pipelining data that already exists but isn't usable yet — the common starting point for most engagements.
Models built on top of live telemetry or sensor streams — predictive maintenance, anomaly detection, and similar systems that depend on a reliable real-time pipeline underneath them.
Building validation and monitoring into the pipeline itself, so bad or malformed data is caught at ingestion instead of silently corrupting a model or dashboard downstream.
Built on infrastructure made for real-time data
Event-driven pipeline infrastructure, shared with our real-time systems work.
High-throughput storage for streaming and time-series data.
Model development and deployment tooling matched to the actual production requirement, not a default stack.
The same real-time ingestion layer used across our drone, robotics, and live-tracking work, feeding directly into the data pipeline.
We're deliberately cautious about where AI/ML actually adds value versus where it's an expensive way to solve a problem a well-structured pipeline and a simpler rule-based system would solve just as well. Part of the engagement is being honest about that distinction upfront, rather than defaulting to a model because it's the more exciting-sounding deliverable — the data pipeline underneath is almost always the part that determines whether the project succeeds, model or no model.