Delivery Practice
The Problem
Your team bought AI coding tools and the throughput barely moved. Tickets still need a clarifying conversation before anyone can start them, review is backing up faster than it clears, and nobody has trusted the end-to-end suite since the last redesign broke every selector in it.
How I Solve It
Two changes carry most of the gain. Agents do the research that turns a vague signal into a ticket someone can execute cold, and a drift check validates that ticket against the current codebase before any code gets written. Then validation ends in agent-driven UAT that reads your acceptance criteria and drives a real browser, so the suite survives the refactor that would have broken a selector-based one. I install the rest of the lifecycle I run day to day around those, including the review-bandwidth defaults that keep a small team from drowning in its own PR queue once authoring gets cheap.
How It's Sold
Scoped as a Blueprint against your current lifecycle, then installed as a fixed-price Build with your team doing the work alongside me, so it sticks after I leave. Every price is published.
Proof
The full lifecycle written up: capture, definition, execution, and the four validation tiers, with the failure signals that tell you which part broke.
A CI accuracy gate that can only tighten, and a written measurement policy that stops a team from overstating its own numbers.
Let's talk about your lifecycle.
Let's figure out the right approach for your situation.