AI insights
For the helpdesk teamAI insights
Section titled “AI insights”AI insights is an experimental surface that asks a large language model to comment on the helpdesk’s recent activity — “what trends do you see this month?”, “which tickets look like they belong to the same problem?”, “where is the team spending the most time?”. The model returns text suggestions; humans decide whether to act.
Where it lives
Section titled “Where it lives”/ai/insights— the index. A list of generated insights, newest first.- Clicking one opens the show page — the full insight body plus the prompt that produced it and the data window it considered.
What generates insights
Section titled “What generates insights”Two paths:
- Scheduled — a nightly job runs each insight kind against the previous day’s data. Backlog summary, resolution-time trends, category drift, agent throughput.
- On-demand — an admin with
platform::ai::generatecan trigger a fresh generation against the current data. Useful for end-of-week reports.
The LLM provider, model, and prompt for each insight kind are configured in AI settings.
Reading insights
Section titled “Reading insights”Each insight has:
- Title — what the model says it found.
- Body — the model’s commentary, usually 200-400 words.
- Generated at — when the insight ran.
- Source window — what date range and what data slices were considered.
- Prompt — the actual prompt that ran.
- Model + provider — which LLM produced this.
The prompt and source window let you read the insight critically. “Tickets resolved 23% faster this month” is more interpretable when you know whether the window was 30 days or 7.
What to trust
Section titled “What to trust”- Aggregate signals — “the network category is twice this month vs last” — these are derived from queries the prompt described. Mostly reliable.
- Causal claims — “because the new Wi-Fi rollout” — the model is guessing. Don’t act on these without checking.
- Specific predictions — “will breach SLA at 3pm” — verify against the actual SLA timer for the named ticket; the model can hallucinate.
Gotchas
Section titled “Gotchas”- AI insights cost money — every generation hits a paid API. The scheduled job has cost guard-rails; on-demand generation is gated on a permission to prevent runaway spend.
- Provider failures (rate-limit, model unavailable) show as a banner on the index. The previous insight stays visible; the new generation just didn’t land.
- Don’t paste insight text into reports without reading it. The model can be wrong, and your name is on the report.