82% of enterprise AI initiatives stall between proof-of-concept and production. Not because the models failed - because nobody defined what "working" meant before the work started. If that description fits your situation, you're in the right place
The tools were purchased. The announcement went out. Then nothing. Adoption stays below 10% because nobody integrated the tool into a workflow worth automating. The spend is visible. The output isn't.
It worked in the demo. It worked in staging. It has been "almost production-ready" for four months. No fallback defined. No monitoring in place. No clear owner of the failure when it breaks on real data.
Leadership is asking what the AI budget returned. The team has no answer that holds. Not because the system isn't running - but because nobody defined what "working" meant before the work started, so there's no baseline to measure against.
These aren't execution failures. They're the direct result of prescribing before diagnosing.
APMM is a five-level diagnostic framework for measuring where your AI actually stands - not where you think it does. Level 0 to Level 4: from AI Curious to AI Native.
For organisations that need to understand their AI position before they commit to building anything. Structured assessment, shared language, and a clear picture of where your AI stands - before any build spend.
For organisations with a pilot that won't ship or a feature that needs to go live. A working, deployed system - production-grade, documented, and handed over to your team with no vendor dependency.
For AI already in production that needs to stay there. Monthly monitoring, optimisation, and incident response - a retainer built around maintaining the standard, not managing the relationship.
A RAG knowledge system built for a cultural foundation (30 books, 2 languages). Solid retrieval architecture, but lacked production infrastructure:
Three critical failures identified before the first test query was run.
A mid-market SaaS platform (200-person company) had built a semantic search feature that worked in demos but degraded silently on production data. They were stalled for four months due to:
Shipped to production in five weeks. Zero regressions in the following 90 days.
Dev Prasadh is a former doctor and IIM MBA who now builds production AI systems. The unusual combination isn't incidental - medical training is diagnostic training. You don't prescribe before you assess. That standard, applied to AI, is what Devverse Labs is built around.
"One of the few consultants who could actually explain what was wrong with our RAG pipeline - and fix it."
- Engineering Lead, B2B SaaS (anonymised)
30 minutes · Written follow-up within 24 hours · No pitch