TICKET MANAGEMENT
Cluster and auto-link duplicate Front tickets on creation
When a new Front conversation arrives, it embeds the message, searches recent tickets for semantic near-matches.
How it runs
The automated pipeline, trigger to output.
- TriggerNew Front conversation createdFront
- ActionEmbed subject + first messageOpenAI
- ActionSimilarity search over recent ticket vectorsPostgres
- LogicTop match above duplicate threshold?
- ActionTag + link conversation to canonical ticketFront
- ActionStore new embedding for future matchesPostgres
- OutputPost duplicate note to the agentFront
What it does
Every new Front conversation is compared against tickets opened in the last 14 days. If the incoming message is semantically close to an existing one, the workflow links the two as a duplicate cluster and tells the assigned agent so they don't work the same issue twice.
When to use it
Use it when one outage or bug spawns a flood of near-identical tickets and your team burns time triaging copies. Best for support queues running on Front where embeddings beat keyword matching.
How it works
- 1A new Front conversation triggers the flow.
- 2OpenAI embeds the subject and first message into a vector.
- 3Postgres runs a similarity query against stored embeddings of recent tickets.
- 4A logic step checks whether the top match clears the duplicate threshold.
- 5If it does, Front adds a `duplicate` tag and links the conversation to the canonical ticket.
- 6The new embedding is written to Postgres so it joins the searchable corpus.
- 7A note is posted on the Front conversation naming the canonical ticket.
Set it up
What you configure once, before turning it on.
- 1Connect FrontShared inbox, conversations.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect PostgresAny Postgres URL — query, write, migrate.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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