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Answer first: Logistics: AI Evaluation Harness Guide — a practical field guide from Shriram IT Ventures for engineering and product leads who need decisions, not decks.
Context & who this is for
Prefer boring technology where the risk is operational, and reserve novelty for the actual product wedge.
Architecture decisions that age well
Prefer boring technology where the risk is operational, and reserve novelty for the actual product wedge.
Implementation checklist
Teams that document trade-offs before coding waste fewer sprints when requirements shift mid-build.
Failure modes we see in the wild
Skip the buzzwords. For Logistics: AI Evaluation Harness Guide, the constraint that matters is usually data ownership, not feature count.
Measurement & rollout
Ship a thin vertical slice, measure, then widen. Big-bang rewrites rarely survive contact with production.
Updated regularly as delivery patterns change.