01
The same bug, filed under four categories
A firmware fault shows up as Connectivity, Hardware, App and Other. Each slice looks small. Together it's your biggest problem.
Issue intelligence for support and product quality teams
Your tickets already know what to fix first.
We read every support ticket you export, group them by the real problem behind them, and hand you a ranked list of recurring issues, their likely cause and what each one costs you a year. Read-only. Ten working days. A human checks every finding.
- One CSV export, no system access
- Report in 10 working days
- Every finding reviewed by a person
HH-20412
Thermostat offline again
Connectivity
HH-20413
Keeps dropping wifi
Hardware
HH-20414
Battery dead after 10 days
Hardware
HH-20415
Device unreachable every morning
App
HH-20416
Doorbell keeps dying
Other
HH-20417
Charged twice this month
Billing
HH-20418
Lost wifi since the update
Connectivity
HH-20419
Need to charge weekly
Hardware
HH-20420
Two charges on my card
Billing
HH-20421
Schedule didn't run, offline
Other
HH-20422
Battery life terrible
App
HH-20423
Refund for duplicate payment
Account
HH-20424
App says device not responding
App
HH-20425
Low battery warning again
Hardware
01Thermostat drops Wi-Fi after firmware 4.2
02Doorbell battery drains in under two weeks
03Double charge when switching plan
56% of these were filed under the wrong category. No dashboard saw the spike.
Sample data from the live demo. Fictional company.
Works with exports from
Jira
Zendesk
ServiceNow
Freshdesk
HubSpot Service
Salesforce Service Cloud
Intercom
Help Scout
Google Sheets
Excel
Linear
GitHub Issues
Jira
Zendesk
ServiceNow
Freshdesk
HubSpot Service
Salesforce Service Cloud
Intercom
Help Scout
Google Sheets
Excel
Linear
GitHub Issues
If it exports a CSV, it works. Tool names are for compatibility only; we are not affiliated with any of them.
The problem
Your dashboards count tickets. They don't read them.
Jira and Zendesk group by the category an agent picked in a hurry. The real problem is in the description, written forty different ways by forty different customers.
02
Nobody has time to read 1,000 tickets
Your support lead knows something is off. Proving it to engineering means a weekend with a spreadsheet, so it waits.
03
Slow creep never trips an alert
An issue that grows 7% a week for three months is never a spike. It is just a quietly expensive line in the budget.
04
Engineering gets opinions, not evidence
"Customers keep complaining about X" loses to the roadmap every time. "412 tickets, $38k a year, here are the IDs" doesn't.
What you get
An intelligence layer between your tickets and your decisions.
One report a manager can act on, then a weekly version that keeps itself current. Every number traceable to ticket IDs.
Recurring issues, grouped by meaning
Tickets clustered by the problem they describe, not the field an agent picked. Each cluster shows volume, trend, affected products and example tickets.
What each problem costs
Handling time, returns and refunds, scaled to a year. Ranked by dollars, not by ticket count.
Early warnings
New issues growing fast, slow creep nobody noticed, and fixes that need confirming before you close them out.
A data-health score
How many tickets are mis-filed, missing a product or closed without notes. It explains why your dashboards disagree.
Likely root cause, with evidence
For every cluster: the probable cause, a confidence level and the three facts that support it, such as "89% on firmware 4.2.x" or "71% name a mesh router".
A ranked action plan
One recommended action per issue, with an owner, an effort estimate and the yearly cost it removes. The page engineering actually reads.
How it works
Code counts. AI reads. A person signs off.
Four steps, and you only do the first one. The numbers come from deterministic code so they are exact and repeatable. AI does the reading. Nothing reaches your report until a human has checked it against the tickets.
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You · 15 minutes
Export and send
A CSV of 90 days of tickets from Jira, Zendesk, ServiceNow or a spreadsheet, plus a 45-minute call about what decisions you make each week. No logins, no integrations, no IT ticket.
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Code · deterministic
Clean and count
Duplicates merged, dates and product names standardised. Weekly volume, aging, open backlog and handling time per issue. No AI touches these numbers.
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AI · reads the text
Read and cluster
Every title and description is read and grouped by the underlying problem, even when customers worded it differently or agents filed it in the wrong place. Root causes and actions are drafted with ticket IDs as evidence.
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Human · signs off
Review and decide
An analyst checks every cluster and cause against sample tickets before anything is published. You get the report, a 45-minute readout and a plan you can defend in a roadmap meeting.
Why this and not another tool
Jira stores your tickets. We tell you what they are saying.
Meaning, not fields
Dashboards count the category field. We read the description. Forty tickets described forty ways become one ranked problem.
All your sources, one picture
Support tickets, field reports, complaint emails and spreadsheets land in the same analysis. Your ticket tool can't see outside itself.
Decisions, not charts
You don't get another dashboard to maintain. You get the five things to fix this quarter, ranked by impact, with the evidence attached.
Read-only and deletable
One export, no credentials. Your data is deleted when the engagement ends. You own every report and every finding.
Proof
No client logos yet. Here is the work instead.
We would rather show you the method than borrow someone else's testimonial. The live demo runs the full analysis on 1,081 sample tickets from a fictional smart-home company: clusters, costs, early warnings, action plan, raw tickets and an assistant you can question.
Built by a senior systems engineer in connected-product quality
Years spent at a vehicle manufacturer turning thousands of field cases from infotainment and telematics systems into the ranked lists engineering actually fixed. This is that work, offered to teams who don't have a quality department.
0sample tickets analysed in the demo
0%of them caused by six recurring problems
$0kestimated yearly cost of those six, in the sample
0%of repeat-issue tickets filed under the wrong category
Figures from the demo dataset, not from a client.
This month
Three pilot companies. No fee.
We are taking on three teams this month and running the full Issue Intelligence report at no cost. In return we ask for honest feedback and permission to describe the anonymised results.
You send
- A CSV export of roughly 90 days of tickets
- 45 minutes on a call about how you work
- Release dates, if you have them
You get
- The full report in 10 working days
- A 45-minute readout with your team
- Your data deleted after, unless you want to continue
A good fit
- Connected hardware, devices or IoT, or a managed IT provider
- 300 or more tickets a month
- Someone who owns the pain and can act on the answer
Request a pilot slot
If you are not a fit we will say so on the first call and point you somewhere useful.
Questions
The things people ask on the first call.
We already have Jira dashboards. Why would we need this?
Dashboards group by the category field, which is often wrong. We read the ticket text and group by the actual problem, then tell you what each one costs and what to do about it. Jira stores the tickets; this is the layer that reads them.
Do you need access to our systems?
No. The first engagement runs on a CSV export you send us. If you move to the monthly service we use a read-only API token that you can revoke at any time.
What fields do you need in the export?
At minimum: ticket ID, created date, status, title and description. Category, priority, product or version, customer, team and resolution notes make the analysis much better. Release dates let us tie spikes to a specific build.
How accurate is the AI clustering?
Good enough to find the pattern, not good enough to publish unchecked. That is why a person reviews every cluster against sample tickets before the report goes out, and why every finding cites ticket IDs you can verify yourself.
What happens to our data?
It is processed on infrastructure we control, never used to train any model, and deleted at the end of the engagement unless you ask us to keep running the weekly report. We can sign your NDA before you send anything.
How long does it take?
Ten working days from the export to the readout call. The weekly version runs itself after that.
What does it cost after the pilot?
A fixed-price report, then an optional monthly service that keeps the analysis current and adds alerts. We price it on what the problems are costing you, and we will tell you the numbers on the readout call rather than making you guess here.
Who is this not for?
Teams with under a few hundred tickets a month, where there is not enough volume for patterns, and large enterprises that already have an analytics team and a vendor. We will tell you if that is you.
Find out what your tickets are saying.
One export. Ten working days. A ranked list your engineers will actually read.