Use your tracker
Jira, GitHub Issues, GitLab Issues, monday.dev, Linear, and Azure Boards stay in place.
Adopt AI coding without changing how the team works.
MergeLoom fits around the workflow your team already trusts: ticket states, comments, labels, repository routing, validation, and PR/MR review.
Works with
Jira GitHub GitLab M monday.dev Linear Azure Boards Azure Repos
Jira GitHub GitLab M monday.dev Linear Azure Boards Azure Repos
Jira GitHub GitLab M monday.dev Linear Azure Boards Azure Repos
Jira GitHub GitLab M monday.dev Linear Azure Boards Azure Repos
Start with labels
Use the existing label, status, or query that means work is ready.
Comments matter
Clarification stays attached to the ticket instead of disappearing into private AI chats.
Review stays
PRs and MRs remain the approval checkpoint.
No retraining
Teams keep the operational habits they already understand.
The same workflow, with a controlled AI run in the middle
Use the same place for planning, clarification, and review while MergeLoom handles the implementation pass.
Work stays in the tracker
Priority, acceptance criteria, comments, and status remain visible to the team.
MergeLoom picks up approved work
A label, status, or query tells the worker what is ready.
Validation runs
Repository commands run before the review request is opened.
Engineers review in Git
GitHub, GitLab, or Azure Repos remains the place where code is approved.
Keep the workflow
Adopt AI coding without adding a hidden backlog, private AI chat queue, or separate handoff process.
No second queue
MergeLoom works from the tracker and issue flow your team already uses, so AI work does not become another place to manage.
Use existing signals
Labels, statuses, comments, and queries decide what runs and when, without forcing teams to learn a new planning process.
Review stays normal
Output returns to GitHub, GitLab, or Azure Repos for the same review and merge process engineers already trust.
Clarify in the issue
Questions, corrections, and rerun instructions stay on the work item so the whole team can see what changed.
Visible decisions
Clarification stays beside the ticket or issue, so product, engineering, QA, and managers can see what was asked and answered.
Better reruns
When the run is sent back through the workflow, MergeLoom can use the latest comments as context for the next attempt.
No lost chat context
Important implementation instructions do not disappear into private AI chats that reviewers and auditors cannot inspect.
Runs with
CX Codex Claude Vertex AI AWS Bedrock AZ Azure Foundry API OpenAI-compatible
CX Codex Claude Vertex AI AWS Bedrock AZ Azure Foundry API OpenAI-compatible
CX Codex Claude Vertex AI AWS Bedrock AZ Azure Foundry API OpenAI-compatible
CX Codex Claude Vertex AI AWS Bedrock AZ Azure Foundry API OpenAI-compatible
See how MergeLoom helps your engineering team.
Connect the tools you already use, give each AI run the right context, validate output before review, and keep the audit trail tied to the ticket.
Related pain points.
See the same ticket-to-code workflow from another toolchain, cost, or governance angle.
Try this workflow on one real ticket.
Start free, connect one repository, add one intake rule, and see whether a real ticket can reach review with less manual implementation work.