How it works

From first idea to shipped code.
Quality-enforced, every step.

PushBackLog covers the full software delivery lifecycle — shaped around the three zones where teams lose time and money to poor process.

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Zone 1

Discovery — Turn any idea into a structured backlog

Answer a few focused questions about a feature or idea and PushBackLog creates well-formed, quality-scored work items — in minutes, not meetings.

Quick intake

Answer 5–10 focused questions; PBL creates a structured, quality-scored backlog item immediately.

Document import

Paste a PRD, brief, or email. PBL proposes a full structured backlog from the document.

Deep discovery

Extended guided interview — from raw idea to full spec to backlog with AI assistance throughout.

Email intake

Forward emails to your team's PBL address; items are created and quality-checked automatically.

AI personas

20 fictional practitioners covering engineering, QA, design, product, and leadership roles.

MCP integration

Connect PBL to your AI coding tools via the Model Context Protocol server.

Zone 2

Definition — Nothing enters your workflow below the bar

Configurable quality gates enforce your team’s standards at every stage transition — automatically, without micromanagement.

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Enforcement modes

Hard block, soft nudge, auto-enrich, or advisory — configure per dimension, per team, per project.

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Quality dimensions

Title clarity, acceptance criteria, Definition of Done, scope boundedness, effort estimate, linked context.

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Best Practices Library

Use community standards from the public library, or define your own — then embed them into AI personas.

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Per-project config

Tenant-wide defaults with per-project overrides. High-stakes projects can enforce harder gates.

Zone 3

Execution — Refined tickets become pull requests

AI personas work your tasks against your codebase, raise PRs, and follow every best practice you’ve attached.

Autopilot execution

Persona works the task, raises a PR, and requests review — fully autonomous.

Supervised execution

Pauses at configurable checkpoints so a human can review and guide the AI.

Best practice enforcement

Personas follow attached library standards during every run — consistently.

AI refinement

AI suggestions for missing context, acceptance criteria, affected code areas, and decomposition.

Readiness score

Configurable threshold shows how close an item is to being safely executable.

Full audit log

Every action taken in a run — AI decisions, code changes, comments — is captured.

From first idea to shipped code. Quality-enforced, every step.