AI feature development from backlog to pull request
The ticket that sat in the backlog for weeks, as one tested PR
Small features never win the sprint. Describe the goal and approve the spec, and a ready-made AI team builds and tests it in an isolated sandbox in your existing codebase. Your team reviews the pull request and merges it.
A feature spec for your lead’s approval
“Let customers download any past invoice as a PDF.”
That is all the product owner wrote. The planner read the billing code and came back with acceptance criteria, edge cases, and the files it would touch. The tech lead approved it before the stand-up ended. This is the first of three checkpoints: spec, pull request review, and merge.
See what each specialist agent contributes
No prompts, no stitching agents together. Each layer of the change has an owner, and each owner hands back something you can check.
Itemized feature costs on the pull request
Finance asks what each feature cost. The pull request shows it: AI usage, sandbox time, and the 10% platform fee, item by item.
Keep approval and release decisions with your team
How Priya hands approved backlog work to agents
Illustrative example, not a customer result.Here is what the work looks like today, and what changes when an AI team takes the busywork off the plate.