Tracker intake
Pull approved work from Jira, GitHub Issues, GitLab Issues, monday.dev, Linear, or Azure Boards.
Seamlessly Integrates
Pull approved issues from the tools your team already uses, then return PRs and MRs to the code hosts where engineers already review and merge.
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
Code host output
Return PRs and MRs to GitHub, GitLab, or Azure Repos for normal review.
Workflow rules
Use labels, statuses, queries, and comments to decide what AI is allowed to run.
Repository routing
Map work to the correct repository without developers copying context into chat.
Visible handoff
Keep status updates, validation results, and review output tied to the original work item.
Work in, code out
Pull approved work from Jira, GitHub, GitLab, monday.dev, Linear, or Azure Boards and return review-ready code to the place your engineers already work.
No new backlog
Your team keeps using the issue system it already trusts. MergeLoom watches the ready signals you choose and turns approved work into an AI coding run.
Control every trigger
Labels, statuses, comments, queries, and repository aliases become the control points that decide what can run and where the code should go.
Review where engineers work
The output returns to GitHub, GitLab, or Azure Repos as a PR or MR, so review, approval, and merge stay in the normal engineering flow.
Use integrations as control points, not just notifications
Ready signal
Define the label, status, or query that proves a ticket is approved before AI touches it.
Repository alias
Route work to the right repository without asking developers to paste context into chat.
Status updates
Show when a run starts, fails validation, blocks, or reaches review without chasing updates.
Review request
Open a PR or MR in the code host so the normal review process stays in place.
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 what else MergeLoom can do.
Connect more of your stack, improve context, validate output, and keep audit evidence across every AI coding run.
Try one controlled AI coding workflow.
Start with one tracker, one repository, and one validation path before rolling AI coding across the team.
Clarify on the ticket
When a run needs more detail, the conversation stays on the issue instead of disappearing into a private AI chat.
Questions stay visible
Engineers, product, and QA can add context on the work item, so decisions are visible to the team instead of trapped in one person's AI session.
Reruns improve
The next run can use the ticket comments as extra context, which makes corrections and follow-up work easier for the agent to understand.
Less copy-paste
Nobody has to move acceptance criteria, clarification, and instructions into a separate chat before AI can help.