Initiatives Ready to Execute. Per Team.
How Relatient turned broad AI exploration into a concrete build roadmap — with use cases ranked and ready for development.
High AI awareness. Zero implementation clarity.
Relatient had already invested in exploring AI opportunities and AWS technologies. The blocker wasn't knowledge — it was translation:
- Loosely defined use cases that couldn't be scoped
- Uncertainty about where AI actually fit in their products
- No clarity on Bedrock vs. SageMaker for which problem
- Product and engineering pulling in different directions
"The challenge wasn't AI awareness — it was turning ideas into executable initiatives."
A decision-enablement engagement anchored to real product scenarios.
ML lifecycle with SageMaker
The end-to-end path from model development to enterprise-scale deployment — so teams could judge feasibility, not just possibility.
GenAI with Bedrock
Building actual GenAI applications: foundation models, RAG patterns, and the governance questions that decide what ships.
Product-anchored throughout
Every discussion tied to a real product opportunity and a real implementation decision. No technology demonstrations for their own sake.
"The goal was not to learn AI — it was to identify what to build and how to build it."
implementation-ready AI use cases identified per product team
improvement in use-case clarity through structured opportunity identification
faster cross-team alignment through shared decision frameworks
Each team left with initiatives capable of moving directly into the next stage of development.
The full roadmap case — free, in your inbox.
What's inside:
- How use cases were framed around business value and feasibility
- The Bedrock/SageMaker decision logic
- The before/after shift from exploration to execution