CHATBOTS

Conversational Warranty Eligibility Bot

A chat bot that takes a customer's order or serial number, checks the purchase record and warranty window, and tells them in plain language whether their item is still covered.

CategoryChatbots
Enginepaperclip
Difficultybeginner
Triggerchat
Steps5
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerCustomer starts chat, bot requests order/serial number
  • ActionLook up purchase record and SKU in PostgresPostgreSQLPostgres
  • LogicCompute warranty status from purchase date + SKU term
  • LogicBranch: covered vs. expired vs. not found
  • OutputReply in chat with verdict and covered-until date

What it does

Gives customers an instant, self-serve answer to "Is my product still under warranty?" The bot collects an order ID or serial number in chat, looks up the original purchase in your records, calculates remaining coverage against the product's warranty term, and replies conversationally with the verdict and the covered-until date.

When to use it

Use this when support agents waste time manually looking up purchase dates and warranty terms for the same eligibility question over and over. Ideal for hardware, electronics, or appliance brands with a fixed warranty period per SKU.

How it works

  1. 1A customer opens a chat and the bot asks for their order ID or serial number.
  2. 2The bot queries Postgres for the matching purchase record, SKU, and purchase date.
  3. 3A logic step compares purchase date plus the SKU's warranty term against today to compute coverage status.
  4. 4If covered, the bot replies with the covered-until date and next steps; if expired or not found, it explains why and offers a paid-repair path.
  5. 5The full chat outcome is delivered back to the customer in the conversation thread.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  3. 3
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  4. 4
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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