What problem does it solve?
Every software product generates a stream of bug reports, crash logs, feature requests and app store reviews that has to be read, categorized and sent to the right engineering team before anyone can fix anything. A person triaging by hand has to judge severity, guess which component is at fault, check whether the same issue was already reported under different words, and decide whether it is even a bug rather than a support question. That judgment call repeats every time a new report comes in, at whatever volume the company's own reports and feature requests arrive, and getting it wrong sends a report to the wrong queue, where it waits until someone routes it again.
Simple keyword rules can route on an exact error code but miss paraphrased duplicates, mixed reports that touch more than one component, and anything that needs the stack trace read to understand. Classifying the text of a report, its logs and its similarity to past tickets against the categories an engineering team already uses changes the economics: a report can be triaged in the seconds it takes to read it, at the volume a support queue actually receives, while the decision to merge any code stays with an engineer.
How does it work?
- Ingest every report. Error logs, crash reports, support tickets and app store reviews land in one queue with their stack traces and metadata (product, version, user segment).
- Classify and deduplicate. The model reads the report, tags severity and likely component, and matches it against open tickets to catch reports of the same underlying issue.
- Route to the right queue. Each ticket goes to the owning team with the model's reasoning attached, instead of a general backlog a person has to sort by hand.
- Draft a starting point for clear defects. When a report includes a reproducible stack trace or failing test, the model proposes a code change or, when it cannot, reproduction steps for an engineer to pick up.
- Check against the human call. The model's category is compared to what the engineer actually decided; repeated disagreement on a category is a signal to retrain it, not just fix the one ticket.
- Audience
- Employee facing
- Autonomy
- Supervised agent
- Adoption
- Emerging
- Channels
- Internal tools, API and system to system
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Accuracy | Too few to pool | 90% | 1 | 1 vendor |
Value drivers: Employee productivity, Speed and cycle time, Lower cost to serve.
Indicative value
A software company that receives 5,000 bug reports and feature requests a year
USD 20,833 to USD 120,000
Engineering triage time avoided per year
How this is calculated
Formula: reportsPerYear * (triageMinutes / 60) * shareAutomated * engineerCost. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Bug reports and feature requests per year reportsPerYear, reports per year | 5,000 | 5,000 | The reference organization. |
| Manual time to read, categorize and route one report triageMinutes, minutes per report | 10 | 20 | Editorial assumption, replace with your own average triage time. |
| Share of reports the model routes without a later reassignment shareAutomated, fraction of reports | 0.5 | 0.8 | Conservative against the benchmark on this page (Google Cloud reports Gelato raised ticket assignment accuracy from 60% to 90%), because ambiguous or high severity reports still get a human check before routing. |
| Fully loaded engineer cost engineerCost, USD per hour | 50 | 90 | Editorial assumption, replace with your own. |
What it leaves out: Counts only the time saved routing and categorizing reports that the model handles without a later reassignment. It leaves out the cost of running the AI, the time still spent on reports that reach a human first, and the larger value of duplicates caught earlier and faster time to a fix.
Who already uses it?
2 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Gelato
Europe · Technology and software · 2025
Gelato, a Norwegian software company that enables local production for global ecommerce through a network of more than 140 printers in 32 countries, uses Gemini models on Google Cloud to automate engineering ticket triage across its 15 engineering teams and customer error categorization.
- Accuracy: 90%
"The AI-powered system increased ticket assignment accuracy from 60% to 90% and reduced the time to deploy ML models from two weeks to one or two days using Vertex AI."
Claimed by: vendor - Hours saved: 120 hours, per week
"Gelato has saved 120 hours of weekly labor as a result, meaning it no longer needs to assign dedicated resources to triage."
Claimed by: vendor
Regnology
Europe · Technology and software · 2024
Regnology, a provider of regulatory reporting software, built a Ticket-to-Code Writer tool using Gemini 1.5 Pro to automate the conversion of bug tickets into actionable code changes, aiming to streamline its software development process. Google Cloud's own summary does not disclose an accuracy, acceptance or time saved figure for the tool.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- A history of resolved tickets labelled with their final category, severity and owning team
- Access to the stack traces, logs or crash reports attached to each report
- A written definition of the severity levels and routing rules the team already uses
Systems to integrate
- Issue tracker (Jira, GitHub Issues, Linear or similar)
- Support or feedback intake tool, including app store review feeds
- Source control and CI, for reports that reach the code drafting step
- Error monitoring and crash reporting tools
Complexity: Low
Classifying a bug report against categories a team already uses is a mature text and log classification task. Most of the effort goes into connecting the issue tracker, giving the model the right context (stack traces, prior tickets) and setting the confidence threshold for when it also drafts a fix rather than only a category.
- 1
Label a real ticket history first
Pull a year of resolved tickets with their final category, severity and owning team, and test the model against it before it touches a live queue.
- 2
Route on confidence, not on the output alone
Auto assign only the categories where the model matches history closely; send low confidence and cross team reports to a person with the model's reasoning attached.
- 3
Keep duplicate detection separate from severity scoring
Match new reports against open tickets by similarity before scoring severity. A report that turns out to be a duplicate does not need its own triage decision.
- 4
Gate code drafting to well scoped defects
Only propose a starting fix when the report includes a reproducible stack trace or failing test; for anything else, draft reproduction steps for a human instead of guessing at code.
- 5
Compare against the engineer's final call every week
Track where the model's category disagreed with what an engineer actually decided, and use the pattern to retrain the categories, not only to fix the individual ticket.
Guardrails
- No automatic merge of any drafted code; it goes through the normal review and test suite like any other change
- Routing confidence thresholds set per category, with low confidence reports going to a person
- A visible label on every automated classification and drafted change identifying it as AI produced
- Regular comparison of the model's category against the engineer's final triage decision
KPIs to instrument
- Ticket assignment accuracy against the engineer's final category, per team
- Share of reports auto routed without a later reassignment
- Time from report received to first engineering action
- Duplicate reports caught before they reach a team queue
Human in the loop
Engineers approve every category change that crosses a team boundary, review and test every drafted code change before merge exactly as they would a colleague's pull request, and a team lead checks a sample of automated classifications each week to catch drift as the product changes.
Common failure modes
- Confident misclassification
- The model assigns a plausible but wrong team or severity with high confidence, delaying a real fix. Track disagreement with the engineer's final call, not only the model's own confidence score.
- Drafted fixes that look right and are not
- A generated code change compiles and matches the report's description but misses the actual root cause. Require the same test suite and review on every drafted change as on any other code, with no exception for AI generated changes.
- Duplicate detection missing paraphrased reports
- Users describe the same bug differently across channels, and near duplicates get filed as new tickets, inflating the backlog count. Match on stack trace and error signature, not only on text similarity.
What are the risks and rules?
EU AI Act
Minimal risk
Triaging internal engineering tickets and drafting code for a human to review has no listed use in Annex III and does not produce a decision with legal or similarly significant effects on a person outside the company, so it carries no use case specific AI Act obligation beyond the general purpose model duties in Chapter V that apply to the provider of the underlying model.
Rules that apply
Controls to put in place
- An inventory entry for the triage and drafting tool with an accountable owner
- No automatic merge of AI drafted code; standard review and CI gates apply unchanged
- An audit log of every automated routing decision and its confidence score
- A periodic accuracy review against the engineer's final triage decision, per team
Frequently asked questions
- How accurate is AI at triaging bug reports?
- It depends on how close the categories are to what the model was tested against. Google Cloud reports that Gelato raised its ticket assignment accuracy from 60% to 90% using Gemini models. That is one company's reported figure; measure accuracy against your own resolved ticket history before trusting it on a live queue.
- Can AI actually fix bugs, not just triage them?
- Google Cloud reports that Regnology built a Ticket-to-Code Writer tool with Gemini 1.5 Pro to turn bug tickets into code; neither Google Cloud page gives its scope, volume or an acceptance rate for the drafts. As editorial guidance rather than something this evidence shows, code drafting is best gated to well scoped defects with a reproducible stack trace or failing test. Treat any AI drafted change like a colleague's pull request: it still needs review and the normal test suite before merge.
- What should stay with a human?
- Any report that crosses a team boundary at low confidence, anything the model cannot match to a known category, and every code merge decision, which should go through the same review as any other change.
- How is this different from AIOps incident triage?
- AIOps incident triage groups monitoring alerts about infrastructure and services into one probable incident. This use case triages reports that people submit about the product itself, such as a crash, a broken feature or unexpected behaviour, before an engineer has confirmed there is an incident at all.
How to cite this page
Blits.ai AI Use Case Library, "AI for product feedback and bug report triage", last verified 30 September 2026, https://www.blits.ai/ai-use-cases/product-feedback-and-bug-report-triage. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 30 September 2026: First published