Proof is part of the implementation, measured the same way every time: the problem, what was built, and a substantiated result.
Two specific engagements are detailed below; further cases will be added as they're documented and cleared for publication.
A growth-stage technology company needed to embed agentic AI into their product to strengthen competitive positioning and significantly improve customer engagement beyond their existing chatbot, but had no production AI infrastructure and no in-house team who had shipped one before.
A production LLM application with a real-time RAG pipeline and a multi-step agentic workflow, built in Python (architecture, model, and vector store selected against latency, cost, security, and scalability), with an evaluation harness and observability gating every release.
Full SHIP cycle, embedded inside the client's team (Scope, Harness, Implement, and Prove), taken from concept to production in 8 weeks.
Around 70% of the target workflow now runs automated, where none of it did before.
Executive workshops and internal capability handover, so the system keeps delivering value after Prople's engagement ends.
Six business units, each generating its own AI ideas, with no shared way to compare them on feasibility, ROI, or delivery complexity, and no board-ready view of where to invest first.
AI discovery and strategy work with C-suite stakeholders: 30+ candidate use cases scored and prioritised across all 6 business units, resulting in a costed, board-ready enterprise AI roadmap.
Scope stage only. This engagement was scoped and delivered as strategy and discovery: Harness, Implement, and Prove were not part of it.
The roadmap was handed to the client's internal teams to take forward into delivery.
Every engagement is measured through multiple dimensions: technical performance, operational adoption, and business results, so "it works" always means something specific.
Whether the system performs against the evaluation design set in Harness.
Whether the people who own the workflow are actually using it, and how often.
Hours returned, cycle time, cost, capacity, revenue, or decision speed, whichever the Scope stage identified as the real target.