Training data for customer support AI agents
A customer support AI agent needs resolved cases with their outcomes, the policies and knowledge articles human agents followed, the actions they took in billing and order systems, and how QA reviewers scored the work, most of which never appears in public FAQ or forum data. SourceX sources multi-year ticket and call histories, SOPs and macros, QA scorecards and linked back-office action records from established companies, de-identified and licensed for an agreed permitted use.
Dataset types to start with
- Customer support ticket datasets
Resolved threads with internal notes, escalations, dispositions and CSAT teach the conversation itself: what to ask first, how to diagnose, and when a case is actually finished rather than just closed.
- Contact center call recordings and transcripts
Call transcripts add identity verification, holds, transfers and live de-escalation, and dispositions show which calls were settled on first contact and which were passed on.
- SOPs, playbooks and internal knowledge bases
Refund limits, eligibility rules, troubleshooting trees and macros give the agent the rules it must follow. With version history, you can check each answer against the policy that applied on the day.
- Human feedback and QA-scored work
QA scorecards grade accuracy, policy adherence and tone on real cases, and supervisor corrections show what a better reply would have been, which makes them usable as reward signals and as eval rubrics.
- Enterprise workflow and task execution histories
The refund, credit, replacement or account change behind a reply happens in another system. Linked action records turn conversations into tool-use trajectories with the parameters the human agent actually used.
Why this data is hard to get
Public conversations stop at the answer
Forum threads, FAQ pages and public social replies show a question and a polished response. They do not record whether the fix worked, whether the customer came back, or what the agent checked before replying.
Policy is private and keeps changing
Refund thresholds, warranty terms, verification steps and goodwill limits are internal and revised often. An old case followed the rules of its day, so tickets without the matching policy version teach outdated behavior.
The work happens outside the help desk
A reply says a refund was issued, but the refund sits in a billing or order system. Help desk exports alone lose the lookups and actions, which are what a tool-using agent has to learn.
Histories already contain bot and macro text
Many support archives include chatbot turns, auto-replies and lightly edited macros. Unless every turn is attributed, a model trained on them learns to imitate a previous bot rather than skilled human agents.
From conversations to tool-using trajectories
A support model that only drafts replies can learn from threads, but one that resolves cases needs trajectories: the customer's message, the lookups an agent ran, the policy that applied, the action taken and the reply, in order. Building them means joining ticket events to action records from billing, order management or account systems by case ID and timestamp, then attaching the policy version in force that day. The joined record shows that a tier-1 agent found the order outside the return window and asked for a goodwill exception, and that a specialist issued store credit the next morning. Reopens and CSAT on the same case label whether that sequence worked.
Handoffs deserve their own labels. A deployed agent can fail in two directions: escalating a case it could have closed, or holding on to one it should have passed on. Escalation events with reasons give you examples of each side of that decision, and bot-to-human handoffs in older histories show exactly where earlier automation gave up.
Grading against what happened next
A support eval should score the resolution, not the prose. For each held-out case, check whether the agent's actions and their parameters match a resolution the policy allows, whether it escalated where the human did, and how it scores on the company's own QA rubric. Watch for false resolutions: replies that read well but, given what the customer did next, would have ended in a reopen. Hold out the most recent months rather than a random sample, because policies, products and macros change, and a random split leaks near-identical macro replies into the test set. The evaluation sets page covers held-out design in more depth.
What to include in a support data request
List the intents and product areas the agent will handle, the channels and languages, and the years of history, marking which period reflects current policy. Name the systems where actions happen if you need linked action records, and set a minimum coverage for QA scores or CSAT. Say how bot-authored turns should be treated and whether identifiers should be masked or swapped for stable pseudonyms. Mark which part of the data is for training and which for evaluation, since a license can treat the two differently. SourceX matches the spec to companies whose support operations fit it; what can be delivered depends on which of them agree to license.
What good data looks like
- Every case has an outcome beyond a closed status, such as a disposition code, reopen flag, CSAT or QA score, and every escalation records why.
- Policies, macros and knowledge articles are versioned, so each case can be paired with the rules in force on its date.
- Refunds, credits, replacements and account changes are linked to the case with timestamps and parameters.
- Every turn is attributed to the customer, a human agent, a bot or an automation rule.
- Intent coverage is reported, including policy exceptions, angry customers and multi-issue cases, not only routine requests.
- Pseudonyms stay consistent through each thread, including quoted replies, signatures and attachments, so de-identified cases still read coherently.
Questions buyers ask
Are support transcripts enough to train an agent that takes actions?
No. A transcript shows what the agent said, not the lookups and changes made in billing, order or account systems behind it. A model trained on replies alone learns to say a refund was issued without issuing one. For tool use, request workflow histories linked to the tickets, so each conversation carries the actions taken, their parameters and their results.
How do I keep a support agent from learning outdated policies?
Pair each case with the policy, macro and knowledge-article versions in force on its date, and state in the request that version history is required. You can then drop cases that conflict with current rules, give the rule text as context during training, or weight recent periods more heavily. Evaluate only on cases handled under the current policy.
Can I train on conversations handled by an earlier chatbot?
Yes, if every turn is attributed. Bot turns, and the points where a bot handed a case to a person, are useful labels for when automation fails. Training on bot replies as if they were expert behavior copies the old system's mistakes, though. Ask for an author role on every event, and agree during scoping whether bot turns are removed, kept as context or labeled.
Do I need call data if my agent only handles chat and email?
Often, as a source of hard cases rather than of style. Customers tend to call when an issue is urgent, emotional or has already failed in another channel, so call transcripts hold escalations, identity checks and de-escalation that written channels show less often. Use them for intent coverage and test scenarios, and train tone and formatting on the written channels your agent will serve.
Can support data come from a specific industry?
You can target one, and you should, because policies and vocabulary rarely transfer well between industries. Supply in software, retail, telecom, financial services or travel turns on which companies in that sector keep usable histories and are willing to license them. Regulated domains add work: financial or health details in tickets call for stricter de-identification and a closer review of customer terms.
Tell us what you are building
Describe the model or agent, the tasks it must handle, and the volume, format and permitted use you need. SourceX will match it to partner data.
Updated 3 October 2026.