Auto-Extract Order Numbers and Account IDs
AI scans customer emails to auto-populate order numbers, account IDs, invoices, and claims without manual entry.
What it does
Pull order numbers, account IDs, invoice numbers, and claim numbers directly from customer emails without manual typing. The AI automatically populates these fields to save time and reduce data entry errors.
Step by step
- Configure the Hiver webhook to send new incoming emails to the extraction endpoint.
- Map specific regex patterns in the configuration to identify order numbers and account IDs within the email body.
- Set up a database trigger that automatically writes extracted values into the CRM fields upon successful parsing.
Where the LLM does the work
- Use an LLM to classify ambiguous text fragments as valid identifiers when standard regex fails to match.
- Prompt the model to extract nested claim numbers from complex invoice descriptions where formatting varies wildly.
- Generate a confidence score for each extraction so human agents can review low-confidence results before saving.
Watch out for
Customer emails often contain multiple invoices in one thread, so ensure the logic splits and processes each distinct document separately.
PII like account IDs can appear in signatures or headers rather than the body, so scan the entire message structure before extracting data.
Language variations in customer names can confuse the parser, so add a fallback step to flag non-English emails for manual review.
Tools that fit
AI Extract Feature LLM extracting order numbers, account IDs, invoice numbers, and claim numbers
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Auto-Extract Order Numbers and Account IDs: AI scans customer emails to auto-populate order numbers, account IDs, invoices, and claims without manual entry. ## Specification - What it does: Pull order numbers, account IDs, invoice numbers, and claim numbers directly from customer emails without manual typing. The AI automatically populates these fields to save time and reduce data entry errors. - Trigger: Triggered by an event (Manual · on demand) - Autonomy: You approve along the way - Expected setup effort: under an hour - Tools/services involved: - Hiver: identifying crucial data points in customer emails - AI Extract Feature: extracting order numbers, account IDs, invoice numbers, and claim numbers ## Known pitfalls, handle each one explicitly in your implementation 1. Customer emails often contain multiple invoices in one thread, so ensure the logic splits and processes each distinct document separately. 2. PII like account IDs can appear in signatures or headers rather than the body, so scan the entire message structure before extracting data. 3. Language variations in customer names can confuse the parser, so add a fallback step to flag non-English emails for manual review. ## Reference implementation https://www.youtube.com/watch?v=GdArsnASMdA (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/auto-extract-order-numbers-and-account-ids/ via usecasesforagents.com
Frequently asked questions
Can I use a different Support & Ticketing than Hiver?
Yes. Hiver is only the example support & ticketing in this recipe. The same flow works with Intercom, Marker.io, Syncro and WHMCS API. Swap the support & ticketing connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for support teams drowning in inbound and looking to triage faster, not just harder.