From self-service BI to machine learning in production, we engineer analytics that move from dashboards to decisions. Production-grade engineering, not pilot-stage data science, so the insights keep running long after the models are built.
Analytics capability areas
From traditional analytics to advanced ML. Every domain anchored in production engagements, exchange-grade fraud and surveillance.
How analytics engagements run
Semantic layer · curated data products · self-service BI · domain models for 2-3 priority use cases. Establishes trusted, governed analytics foundation.
One named ML use case from notebook → production. Feature store + serving + monitoring + business integration. The full MLOps backbone for that workload.
Pilot of SentinelAI + Forensica on your historical data. 1-2 alert types tuned and validated. Demonstrated false-positive reduction. Path to production.
Let's talk.
Tell us what's in your data and AI stack, what's stalled, and what would change if it worked. We'll share what we've shipped against similar patterns in production, and what makes sense as a first step.
Our Hyperscaler & Strategic Partners
