As a specialist resource for the architectural and infrastructure work your team doesn't have bandwidth for — pipeline design, Kafka cluster configuration, Databricks optimization — while your team retains ownership of the data models and business logic. We hand off with full documentation, not a black box.

We have built IoT streaming pipelines processing router device data at 5G network scale — high-velocity, high-volume event streams across multi-zone Kafka clusters. At Comcast, we worked on operational visibility data across a national cable and broadband network. These are not proof-of-concept environments.

Both. The pipeline and the analytics dashboards can be a single engagement — Kafka producers through to Redshift, plus the React-based dashboard consumed by data science teams. We design for the end consumer of the data, not just the infrastructure that moves it.

AWS (S3, Redshift, EKS), GCP (BigQuery, Dataflow, GKE, App Engine), and Azure. Our IoT work ran on AWS; our Comcast and BNY Mellon work ran on GCP. We're platform-agnostic and will work within your existing cloud commitment.

Yes — this is specifically where we focus. Building a model is the easy part; operationalizing it into a reliable production pipeline with monitoring, retraining hooks, and data quality guarantees is the hard part. Our Reed Elsevier engagement was exactly this: collaborative filtering from algorithm through to production evaluation.

In 30 minutes we map your pipeline across four dimensions: ingestion (where data enters and what's validated), transformation (what's happening to the data and where quality degrades), storage (whether the warehouse or lake is architected for the queries being run against it), and consumption (whether dashboards and models are working with fresh, trusted data). You leave with a clear picture of the highest-leverage fix.