Order from Lab 6D3852

Support conversations

Resolved customer support tickets that show the work behind the answer: every message, the internal notes the customer never saw, each tool the human agent used (lookup, refund, macro, article, escalation) with arguments and result, and whether the same customer came back about the same problem within 30 days. Real, from several companies' helpdesks, with people replaced by stable ids and commercial training rights.

How answering works

Any number of people fill one order. The lab receives one dataset, in its own columns.

Your agent reads the order
It looks at what you hold and says which columns you can fill, and what is missing.
Mora checks what you send
Every record: no copies, no personal data, not already public, not on Mora from someone else.
The lab pays per record it accepts
The lab named the price. You fill as much of the order as you can.

The columns the lab wants

You do not need every column. Mora says which ones each dataset fills.

Column Type What it must hold

ticket_id, customer_id, account_id id Pseudonyms, the same id each time the same person or account appears, so repeat contacts can be followed

opened_at, resolved_at, channel, language, product_area, tier date, label When, where, in what language, about what, and at which support tier

messages list of objects Each message with message_id, sent_at, author_role (customer, agent, bot, system), author_id, text

internal_notes list of objects Notes the customer never saw: note_id, written_at, author_id, author_team, text

agent_actions list of objects Each tool the human agent used: action_id, at, actor_id, tool (order lookup, refund, macro, article opened, account change, escalation), arguments, result

resolution, resolution_code, escalated_to text, label What fixed the problem and which team it went through

came_back boolean with dates Same customer, same problem, within 30 days, with next_ticket_id, next_contact_at and observed_until (the date the export stops)

csat_score, csat_comment number, text The rating the customer gave, optional

articles and macros table article_id or macro_id, title, body, updated_at: the texts agents answered from

Column	Type	What it must hold
`ticket_id, customer_id, account_id`	id	Pseudonyms, the same id each time the same person or account appears, so repeat contacts can be followed
`opened_at, resolved_at, channel, language, product_area, tier`	date, label	When, where, in what language, about what, and at which support tier
`messages`	list of objects	Each message with message_id, sent_at, author_role (customer, agent, bot, system), author_id, text
`internal_notes`	list of objects	Notes the customer never saw: note_id, written_at, author_id, author_team, text
`agent_actions`	list of objects	Each tool the human agent used: action_id, at, actor_id, tool (order lookup, refund, macro, article opened, account change, escalation), arguments, result
`resolution, resolution_code, escalated_to`	text, label	What fixed the problem and which team it went through
`came_back`	boolean with dates	Same customer, same problem, within 30 days, with next_ticket_id, next_contact_at and observed_until (the date the export stops)
`csat_score, csat_comment`	number, text	The rating the customer gave, optional
`articles and macros`	table	article_id or macro_id, title, body, updated_at: the texts agents answered from

How much, in the lab's words

400,000 resolved tickets from at least 6 companies, none above 30% of the total, at least a quarter tier 2 or 3; 60% with an internal note, 50% with at least one logged action, every ticket with 30 full days of follow-up observed. Plus 5,000 tickets exclusive, for evaluation.

tickets
messages
internal-notes
actions
resolution
return-contact
satisfaction
knowledge
source