AI customer support is one of the first places agents are being deployed, and they learn best from real resolved cases. Your helpdesk history is a library of exactly that.
What makes ticket data valuable
- Complete arcs: problem, questions, diagnosis, fix, confirmation
- Escalations: hand-offs from tier 1 to engineering
- Internal notes: the reasoning customers never see
- Categories and tags: structure that makes data easier to use
- Edge cases: the unusual problems AI systems struggle with
Polyshares lists support tickets among the operational data it licenses.
Not sure if your data qualifies? The intake takes a few minutes.
What to exclude or mask
- Customer names, emails, phone numbers, addresses and account IDs
- Attachments containing personal documents or screenshots of private data
- Payment and billing details
- Tickets involving legal disputes or regulators
Preparing ticket data
- Export tickets with comments, internal notes, tags and timestamps.
- Choose date ranges and ticket categories.
- Decide how to handle attachments (often excluded).
- Agree the anonymization method and sample review.
Who to talk to
Polyshares explicitly names support tickets as a data source and states any company with real operating history can apply. Read our Polyshares review.
Frequently asked questions
Are macro or template-heavy tickets valuable?
Less so. Buyers value visible reasoning, so tickets with real back-and-forth and internal notes are worth more than canned responses.
Do internal ticket notes count?
Yes. Internal notes and escalations show how agents and engineers reasoned about a problem, which is often the most valuable part.