AI

From a customer ticket to a fix pull request

This is the part we haven't seen anywhere else. A customer ticket can move on its own from triage to a root cause, then to a fix plan, then to an actual code change on a branch and a pull request in your Git. Whenever the AI isn't confident, it stops and waits for a person to weigh in.

ReplacesManual triage & context-gatheringAvoryx
The USP

The whole support-to-engineering hand-off, collapsed into one loop.

Because support, source, pipelines and incidents are one platform, the AI can carry a ticket the entire way and keep every artifact linked. It triages and auto-applies priority above 0.85 confidence, root-causes against recent commit SHAs and incidents, ranks the likely repos, reads the relevant files via Gitea, generates minimal per-file diffs, and opens a branch + PR — then can trigger a pipeline for the fix branch. Every AI call is metered and audited through one path.

  • Triage → root cause → fix plan → code diff → branch + PR in Gitea
  • Ranks likely repos and reads the relevant files before proposing a change
  • Human gate on low confidence — it drafts; a person reviews and merges
  • One metered, audited callAI path (Gemini default) — never a raw provider SDK
Understand the ticket Find the root cause Propose the fix Ship it — with a gate
What it actually does

Built with precision — every part of it.

Not a thin feature bolted onto a suite. Here's the real surface area, grouped.

Understand the ticket

  • AI triage: category, priority, severity, confidence and affected components
  • Auto-applies priority above 0.85 confidence and re-stamps the SLA
  • Auto-opens a Sev1 incident for high-confidence critical tickets

Find the root cause

  • Correlates the ticket against recent commits and incidents
  • Produces a hypothesis with confidence, related commit SHAs and evidence
  • Ranks which repositories most likely contain the cause

Propose the fix

  • Reads the repo file tree via Gitea and selects the files to inspect
  • Generates minimal, surgical per-file diffs plus commit message, PR title and body
  • Turns a diagnosis into a concrete fix plan: change type, risk, files, testing, rollback

Ship it — with a gate

  • Creates a branch, patches files, opens a PR and records it against the ticket
  • Can trigger a pipeline run for the fix branch, or emit a downloadable patch package
  • Low-confidence work stops for a human — autonomy where it's earned
On top of Manual triage

Everything Manual triage does — plus what only one platform can.

Capability
Them
The usual support → eng hand-off
The one platform
Avoryx
Ticket classification & priority
Manual
AI, auto-applied >0.85
Root-cause correlation
Manual log-digging
vs real commits & incidents
Repo & file selection
Manual
AI reads the tree via Gitea
Drafts the fix diff & PR
Not available
minimal per-file diffs
Human review gate
n/a
on low confidence
Support, source & pipelines connected
Separate tools
one platform
AI is metered & audited
Varies
one callAI path

Avoryx is licensed at the platform level — every module is included in one seat, not billed per module. See pricing. Competitor capabilities vary by plan; verified June 2026. Avoryx's Merchant-of-Record and payouts are on the roadmap; SOC 2 Type II is in progress, not certified.

The difference

Why one platform wins here.

Collapses the hand-off

The re-reading, re-filing and context-gathering between support and engineering disappears — the AI carries the ticket to a proposed PR with the evidence attached.

Autonomy with oversight

It drafts the diff and the PR; a human reviews and merges. Low-confidence work is gated by design, and high-confidence critical tickets escalate to a Sev1 incident automatically.

Everything stays linked

Because support, source, incidents and pipelines are one platform, the ticket, the diagnosis, the PR and the deploy stay connected the whole way — nothing to reconcile later.

FAQ

Common questions

An AI workflow that takes a customer ticket, triages and root-causes it against your real commits and incidents, ranks the likely repos, reads the relevant files, generates a minimal code diff, and opens a branch and pull request to fix it — with a human review gate when confidence is low.

See it on your real numbers.

15 minutes, your stack, your per-seat math, the revenue back-office live.

Avoryx
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