What teams can usually answer
- Which bugs are open
- Which incidents happened
- Which issues feel painful right now
Evidence-backed bug analysis pilot
See where your bug history concentrates leverage
Analyze tickets and linked code changes to reveal recurring failure modes, systemic patterns, and code hotspots - without prescribing what to do next.
For teams that know recurring bug pain is real, but need evidence before committing to quality investment.
- Surface recurring failure modes across tickets
- Expose hotspot files and systems absorbing fix effort
- Leave with a decision-ready analysis for planning
The problem
Most teams can count bugs. Few can explain the patterns that produce them.
Recurring causes live across tickets, merge requests, diffs, and memory, making it hard to build a shared view.
What is much harder to answer
- Which failure modes keep repeating
- Which code areas absorb most fix effort
- Where the highest leverage sits
When no one can explain why bugs cluster, quality investment loses to short-term delivery pressure.
What Ticket Triage does
Turn bug history into leverage insight
Not another dashboard. A focused analysis that makes bug history usable for decisions.
Import real bug history from GitLab
Bring together bug tickets, linked merge requests, and diffs so analysis starts from real engineering history.
Surface recurring patterns and hotspots
Expose failure modes, RCCF categories, and code areas that repeatedly show up in bug-fix work.
Clarify leverage without prescribing actions
Provide evidence-backed leverage areas and suggested directions so teams decide what to do next.
How it works
A focused analysis flow for busy engineering teams
Keep the scope tight, keep the inputs real, and end with an evidence-backed analysis.
Three steps from historical bug evidence to a decision-ready readout.
- 1
Set the scope and connect your data
We define the dataset together and connect your GitLab tickets, merge requests, and diffs.
- 2
Uncover patterns behind recurring bugs
The analysis surfaces recurring failure modes, systemic patterns, and hotspot areas driving repeated fix effort.
- 3
Review and trace the results
You can explore the outputs, understand the patterns, and trace every insight back to the underlying tickets and diffs.
What you get
Outputs built for decisions, not directives
Engineering leaders can review, challenge, and use the results to focus their own plans.
Recurring failure-mode map
See which RCCF categories dominate your history and where leverage is likely to be highest.
Systemic patterns across tickets
Spot cross-cutting issues that show up across tickets, teams, or parts of the stack.
Hotspot concentration view
Highlight files and areas that repeatedly absorb bug-fix effort.
Decision-ready result
All insights bundled together – including in-product PDF export.
Proof
Reviewable outputs grounded in real tickets and diffs
Examples of the evidence layer teams can inspect, challenge, and use.
Recurring patterns
See which failure categories dominate
Understand what drives bug-fix effort so planning starts from evidence, not opinion.
Traceability
Inspect the evidence behind a category
Review the reasoning, subpatterns, and linked tickets behind each pattern.
Grounding
Stay close to ticket reality
Open individual tickets to confirm the analysis reflects real context.
Why teams trust it
Built for engineering organizations that want conclusions they can verify
What it is
- Evidence-backed recurring bug analysis
- Reviewable outputs tied to tickets and diffs
- A way to focus quality investment with shared evidence
What it is not
- Not another error-monitoring dashboard
- Not static analysis noise detached from bug reality
- Not a consulting plan that tells teams what to do
Pilot analysis
Start with a focused pilot, not a heavyweight rollout
The pilot is scoped to validate fit quickly, produce a useful artifact, and keep commitment risk low.
Best fit
Engineering teams with real bug history and recurring quality pain
- Head of Engineering, CTO, EM, Staff Engineer, or Tech Lead sponsor
- Enough historical bugs to reveal recurrence patterns
- A single system or team to focus the analysis
- Teams that want evidence before committing to quality investment
Included in the pilot
A concrete, decision-ready output - not just access to software
- Recurring failure-mode distribution and systemic pattern summary
- Code hotspot concentration view
- Leverage areas with suggested directions (no implementation plans)
- Evidence layer with ticket and diff traceability
- In-product PDF export
Pilot scope: Single system or team, GitLab issues, one code host, single analysis run, ~2-4 weeks.
Pilot price: EUR 18,000 total (EUR 12,000 setup and baseline, EUR 6,000 pilot platform and support).
Trust model: Runs in your environment, with your own OpenAI access (BYOK).
Additional connectors: Integrations beyond the default setup: alternatives to OpenAI or GitLab, scoped individually, typically EUR 4,000-EUR 12,000.
FAQ
Questions teams usually ask before a pilot
How is this different from Sentry, Datadog, Sonar, or CodeScene?
Those tools help with monitoring, rule-based issues, or code-level signals. Ticket Triage focuses on historical bug reality: recurring failure modes, linked fixes, systemic patterns, and leverage areas backed by evidence.
Do we need GitLab and OpenAI?
For the current offer, yes. The pilot is designed around GitLab bug tickets and OpenAI. Other setups are possible, depending on scope and via additional integrations.
What do we get at the end of the pilot?
You get a recurring failure-mode view, systemic pattern summary, hotspot concentration view, and a decision-ready readout with an evidence layer, including in-product PDF export.
Is this a software product or a pilot engagement?
The software powers the analysis, and during the pilot you get read-only access to explore the results - not just a static report. The engagement is a productized, single-scope pilot before any broader rollout.
How much setup is needed?
The pilot is intentionally scoped: one system or team, one ticketing source (GitLab), one code host, and an agreed historical window.
How do you handle trust, privacy, and reviewability?
The model is customer-hosted with BYOK. Outputs stay reviewable and traceable back to real tickets and diffs.
Do you tell teams what to do?
No. We show where leverage concentrates and provide suggested directions, but teams decide the actions.
Ready to focus on leverage?
Talk through your bug history and see whether a focused pilot makes sense
Start with one team or system, review recurring patterns in real tickets and diffs, and leave with a shared evidence base for planning.