Production narrative
The system owned end to end, including what broke and what changed afterwards.
- Pipeline
- Ownership

LLM, RAG, MLOps, streaming, analytics, warehouse and platform specialists evaluated against real production data environments.
Most data and AI candidates can describe a model. Far fewer have carried one into production, watched it drift, and owned the correction — which is exactly the capability that decides whether your platform earns trust.
Prism screens for pipeline ownership, evaluation discipline, cost awareness and governance around sensitive data, because those are the dimensions that separate a demo from a dependable system.
A model nobody maintains is a liability with good early metrics.
Production reality, tested by a practitioner who can tell the difference between a pipeline and a prototype.
Something the candidate built, shipped and then kept running under real freshness expectations.
How correctness, drift and regression were measured — before a business user reported the problem.
Lineage, access and handling of regulated or sensitive data as part of the engineering work.
The measurable use the work actually served, described without inflation.
Enough context for your team to assess fit instead of re-running the screen.
The system owned end to end, including what broke and what changed afterwards.
How quality was defined and monitored, and what the candidate did when it slipped.
Privacy, lineage and access posture confirmed during screening, not assumed at onboarding.
Rate, availability and authorization settled before technical interest builds.
Describe the data estate, the failure you cannot afford and the deadline. Prism calibrates the evidence standard around it.