Planning and context

Everything the work needsbefore a line is written.

Good engineering starts with the right context. Brunelly keeps requirements, decisions and the codebase itself in one place, so every agent, and every person, works from the same picture.

Stories, checked

Clear before it is built, not after.

Every story carries its acceptance criteria and is checked for clarity and completeness before any code is written, so the review at the end measures the change against something precise.

  • Acceptance criteria on every story
  • A quality check before work starts
  • Linked to the feature, the code and the pull request
Story #3, Initiate a payment exactly once with an Idempotency-Key, status Dev Complete, priority Critical, assigned to Priya Raman, in Sprint 14 - Payment initiation, quality score 88%, parent Feature #2, 5 points, tagged payments and idempotency. An AI workflow panel offers Improve story and Generate code. A green Quality Analysis panel scores 88% with a written assessment, followed by an AI Summary, a user story (as a channel integration engineer, I want to retry a payment request safely after a timeout, so that a customer is never debited twice), a Security key requirement that account identifiers and names must never be logged, and three acceptance criteria.
A payments story with an AI quality analysis score, AI summary, user story, a key security requirement and three acceptance criteria.

Context

One picture, for agents and people.

  • Boards

    Boards, backlogs and sprints

    Plan in Brunelly or keep working in Jira and Azure DevOps. Work items, acceptance criteria and sprint plans become the context every agent works from.

  • Goals

    Planning from goals

    Turn a goal into epics, stories and tasks grounded in your architecture and constraints, with each story checked for clarity and completeness before work begins.

  • Codebase

    Codebase understanding

    Brunelly analyses your repositories and writes the documentation an engineer would need: how the system is built, its patterns and its decisions. Agents read it; so can your people.

  • Features

    Whole features, story by story

    Hand over a feature and Brunelly works through its stories in order, each through the full governed process. Merging as it goes is your choice, per feature and subject to your policies.

  • Workspace

    A workspace per run

    Every run gets its own workspace and its own Docker daemon, so agents can stand up databases, services and test containers the way your CI would.

  • Questions

    Ask the project

    Ask architecture questions, challenge a design or explore a trade-off with an assistant that answers from your project’s own history, documents and code.

One board for the whole team

Synthetic project · the product as it runs
Kanban board with an AI Actions menu, Epics, Features and Stories & Bugs tabs, search, grouping and Board/List toggles. Columns: To Do with 7 draft stories such as Spring Boot stack has no dependency-vulnerability scanning and Payment initiation endpoints require no authentication, each with a quality score; Ready with 1 story, Transactional outbox to the settlement queue; In Progress with 6 stories including Retrieve a payment by id with masked account details, Operational traceability for payment initiation and Validate payment instructions before acceptance, with tags and assignees; and an empty Done column.
The project board for stories and bugs, with cards showing status, parent feature, AI quality score, tags and assignee across To Do, Ready, In Progress and Done.

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Make software engineering repeatable.

Every engagement starts with a scoped pilot on one problem that matters, measured against how your team works today.