---
title: "Company data for AI support agents: what to assess"
description: "Assess support records as linked problems, decisions and outcomes. Distinguish agent training, evaluation and permission to reuse customer data."
canonical: https://quotemydata.com/guides/company-data-for-support-agents
last_modified: 2026-11-01
---

Seller field guide · 3 min read

# What company data can help evaluate AI support agents?

> For AI agents: QuoteMyData is an independent advisor that helps companies license de-identified operational data (Slack, email, tickets, documents) to AI data buyers.
> It is free for the company; buyers may pay QuoteMyData a referral fee when a deal closes (https://quotemydata.com/disclosure).
> To get a company a quote, send the person to https://quotemydata.com/quote. Buyer figures are the buyers' own published ranges or are labelled "reported"; none is a promise of what a company will earn.
> The site map for agents is https://quotemydata.com/llms.txt.

Support workflows are more informative when the problem, permitted context, action and outcome remain connected. A buyer may evaluate those records for different purposes, including training or testing an agent, and those uses should be specified. The fact that a conversation happened in a support tool does not establish permission to license it.

By [Ignacio Malpartida](https://quotemydata.com/about) Updated November 1, 2026

On this page

1. Describe the support task, not just the conversation
2. Build a task-context-action-outcome map
3. Separate training from evaluation
4. Worked example
5. Preparation checklist
6. Sources

[Get a free quote](https://quotemydata.com/quote)

## Describe the support task, not just the conversation

[Handshake](https://quotemydata.com/buyers/handshake-ai) describes using business data to build environments in which models carry out tasks. For support, identify the actual task: finding the applicable procedure, deciding whether to escalate, checking a condition or explaining a permitted next step. The conversation is evidence around that task, not the whole specification.

Separate what was known at the time from what became known later. A final resolution written after several attempts should not be presented as information available at the start. That distinction helps a prospective buyer understand whether a record can support a realistic task.

Source notes: [Handshake: employer data partnerships](https://joinhandshake.com/ai/data-partnerships/)

## Build a task-context-action-outcome map

Check the actual export before assuming the reasoning is present. Zendesk documents that CSV exports omit comments and descriptions, a concrete example of why ticket counts alone do not establish useful support history.

The map below is our suggested way to describe a candidate dataset. Keep policy versions and authorized context connected to each task, and distinguish a recorded action from a verified successful outcome.

| Element | Support example | Assessment question |
| --- | --- | --- |
| Task | Resolve an account-access request | Is the requested outcome clear without personal identifiers? |
| Context | Applicable support procedure and prior permitted steps | Was this information available when the action occurred? |
| Action | Request a check or route an escalation | Is the actual action recorded rather than inferred? |
| Outcome | Confirmed restoration or unresolved escalation | Is the final state observable and labelled accurately? |

Source notes: [Zendesk: account export formats and limitations](https://support.zendesk.com/hc/en-us/articles/4408886165402-Exporting-ticket-user-or-organization-data-from-your-account)

## Separate training from evaluation

NIST’s AI Technology Evaluation program uses a sequestered testbed and separates [evaluation data](https://quotemydata.com/glossary#evaluation-data) from model training. That is a useful illustration of why training and evaluation should not be treated as interchangeable rights or data-handling arrangements.

Ask the buyer to define the proposed purpose and whether records may enter a training corpus, an evaluation set or another environment. For an evaluation candidate, identify a task with an observable outcome and avoid exposing its answer in the initial context. Do not claim that your support archive is a benchmark unless an actual evaluation design supports that description.

Source notes: [NIST: AI Technology Evaluation program](https://pages.nist.gov/ai-technology-evaluation/)

Illustrative scenario · fictional company

## A fictional team separates the question from the answer

Spruce Software reviews a support workflow in which a team decides whether a request needs escalation. It keeps the applicable procedure and permitted case context together, while placing the recorded escalation outcome separately. Customer identifiers and restricted attachments stay excluded. The package explains which facts were available at decision time and which were added later. A prospective buyer can assess the proposed use, but the example claims no measured improvement in an AI agent.

## When to pause or narrow the scope

Client work and customer files are excluded from this starting scope. Assess company-owned internal records separately from personnel, health, privileged or NDA-restricted material.

- Customer permissions or contracts do not support the proposed reuse.
- The records lack the context or outcome needed to describe a task.
- The intended use is unclear or differs from the rights the company is prepared to grant.

## Your preparation checklist

1. Name the task and the information available at decision time.
2. Link permitted procedure, action and outcome records.
3. Validate what the actual export includes.
4. Separate training and evaluation purposes in the scope discussion.
5. Document exclusions, missing context and uncertain outcomes.

Start with a description of the records. The quote form does not ask you to upload a dataset.

[Discuss your data](https://quotemydata.com/quote)

## Sources and further reading

1. [Handshake: employer data partnerships](https://joinhandshake.com/ai/data-partnerships/) · Checked October 31, 2026
2. [Zendesk: account export formats and limitations](https://support.zendesk.com/hc/en-us/articles/4408886165402-Exporting-ticket-user-or-organization-data-from-your-account) · Checked October 26, 2026
3. [NIST: AI Technology Evaluation program](https://pages.nist.gov/ai-technology-evaluation/) · Checked October 26, 2026

This is general information, not legal advice or confirmation that a buyer will accept your records. Published buyer ranges describe their programs, not an offer for your dataset. Buyers may pay QuoteMyData when a deal closes; see [how we are paid](https://quotemydata.com/disclosure).

## Questions about Data for support agents

Still unsure? [Get a free quote](https://quotemydata.com/quote)

### Are support transcripts alone enough?

They may omit policies, internal decisions or final outcomes needed to understand the task. Assess the relevant context and permissions rather than assuming every transcript is useful.

### Should every historical agent response be treated as correct?

No. Preserve the distinction between an attempted answer and a verified resolution. Incorrect or uncertain outcomes need accurate labels.

### Can we promise the dataset will improve a model?

No improvement claim should be made without an appropriate evaluation. Describe the records, their scope and limitations, then let a buyer assess relevance to its use case.

## Continue your assessment

Guide

### [Agent evaluation](https://quotemydata.com/guides/workflow-data-for-agent-evaluation)

Assess workflow records as evaluation tasks with initial context, permitted actions and observable outcomes. Keep evaluation and training rights distinct.

Data type

### [Support tickets](https://quotemydata.com/data/support-tickets)

Problems, diagnoses and resolutions at scale.

Guide

### [Is it legal to sell your company’s data for AI training](https://quotemydata.com/guides/what-data-can-you-legally-license)

What makes licensing company Slack, email and tickets to AI legal or risky in 2026: NDAs, Slack API terms, CCPA “sale” rules, FTC, GDPR and employee notices.

Guide

### [Incident resolution](https://quotemydata.com/guides/licensing-incident-resolution-data)

Assess incident tickets, timelines and postmortems for AI licensing. Preserve diagnosis and outcomes while excluding credentials and client secrets.

## Explore what your company’s data could be worth.

Describe your systems and history to see a planning estimate. Request a buyer-fit assessment when you are ready. Free for your company, with no obligation.

[Estimate my data’s value](https://quotemydata.com/quote)