Before building anything, you need to know where your data is, what shape it's in, and what it would take to make it AI-ready. This engagement gives you that — in weeks, not months.
Every engagement is structured around specific deliverables — not open-ended retainers. You know what you're getting and when before the work starts.
Map every data source, format, system, and owner — producing a clear picture of what you have, where it lives, and what's missing before any architecture decisions are made.
Structured evaluation of your data quality, governance posture, and infrastructure readiness — scored against the requirements for production enterprise AI deployment.
A phased roadmap from your current state to a governed, AI-ready data foundation — sequenced by value, not by what's technically easiest.
TCO and ROI modelling for your specific environment — grounded in the actual costs of your current state and the verified outcomes from comparable deployments.
Built from what's actually in your systems — not a generic framework applied from the outside.
Sequenced around what delivers the fastest return, not what's technically easiest to build first.
Architecture recommendations are specific to PIEScale's ingestion, governance, and agentic capabilities.
Every engagement ends with a document set your internal team can execute against — not a dependency on us to continue.
18+ years of energy data experience informs every architecture recommendation.
A 2-week assessment for a clear question. A full roadmap only when the complexity warrants it.
Most data strategy engagements start here — a structured 2-week assessment before any architecture work begins.
See Services Overview →Architecture recommendations are built around PIEScale's five components — see the platform the roadmap is designed to deploy.
Explore PIEScale →A 30-minute discovery call, grounded in your actual data landscape and the AI outcomes you're trying to reach.