Rule-based extraction
Use regex, CSS selectors, XPath, or source field mapping when the source layout is stable.
Product
Extract structured data
Polytrace helps teams turn emails, attachments, notices, files, and web pages into usable fields, reviewed extracts, and one latest row per tracked item. It supports rule-based extraction when a value lives in a stable location, AI extraction when the language is too variable for rigid rules, and field-level evidence and review so structured outputs stay tied to the underlying record.
Highlights
Core capabilities
AI extraction
Handle variable language and inconsistent documents without forcing a brittle rule set.
Field-level evidence
Keep every extracted value tied to supporting source evidence and review metadata.
Tracked items
Create one latest row per contract, vendor, policy, or other tracked item when teams need a usable day-to-day table.
Workflow
How this works in practice
Define the field set
Choose the fields that matter to the decision, not every value that happens to exist.
Assign the method per field
Use rules where the source is stable and AI extraction where variability is high.
Review evidence
Inspect field results with supporting evidence and correction controls where accuracy matters.
Publish the right output shape
Use reviewed extracts, one latest row per tracked item, exception views, or downstream exports based on the workflow.
Feed downstream work
Push trusted values into monitoring, sharing, routing, or operating views.
Comparison
Choose the right extraction method
| Method | Best when | Typical example |
|---|---|---|
| Regex | A value follows a reliable text pattern | Reference number in a subject line or attachment body |
| CSS selector | A web page exposes the value in stable HTML | Status badge or effective date on a portal page |
| XPath | The document structure is predictable but needs precise element targeting | Nested values inside structured page markup |
| Source-path mapping | The source already exposes a clean path to the value | Metadata fields from a structured source payload |
| AI extraction | The signal is expressed in variable language or inconsistent layouts | Free-form email threads, mixed attachments, or long notices |
01
Choose the extraction method field by field
Some values should be extracted with explicit rules because their location is stable: a notice number in the subject line, a date beside a label, or a status in a predictable HTML element.
Polytrace can handle those cases with regular expressions, CSS selectors, XPath, and source field mapping. When the signal is buried in free-form language or inconsistent attachments, the same workflow can use AI extraction.
02
Keep every value tied to evidence
Extraction only helps if the team can trust it. Polytrace writes field results with evidence and review metadata so reviewers can see where a value came from and whether it needs correction.
That matters in high-stakes workflows such as renewal dates, vendor issues, claims status, or referral routing, where a downstream decision needs to be explainable.
03
Keep one latest row per tracked item
Many teams do not want a long stream of extracted facts. They want one latest row per contract, vendor, policy, shipment, or case.
For those workflows, Polytrace can keep one latest row per tracked item, based on the fields that define it. Teams get a usable day-to-day table without losing result history.
04
Let review and correction update downstream output
Human review is part of the workflow. When a reviewer corrects an extraction result, Polytrace can update the result and refresh the latest row without waiting for a full rerun.
That keeps alerts, operating tables, and downstream deliveries aligned with the reviewed truth instead of lagging behind correction work.
05
Start with one field set that drives action
The best first extraction workflow is a compact field set tied to one operational decision: the date that triggers a follow-up, the status that drives routing, or the party that determines ownership.
Once that first field set is producing trustworthy outputs, expanding the model becomes much easier because the source capture, review path, and delivery path are already in place.
Where this fits
Common structured outputs
- Renewal and obligation dates that drive follow-up work
- Vendor issue or claims status fields for routing and review
- Up-to-date tables for contracts, vendors, policies, or cases
- Exception views that isolate missing or risky values
Related pages
Go deeper from here
Use the closest product, workflow, or security page to continue the evaluation.
Review and correct extracts
Polytrace helps teams review important outputs before they spread, and correct the record without losing the source trail.
Open pageRenewal and obligation tracking
Use Polytrace for renewal and obligation tracking.
Open pageVendor delay tracking
Use Polytrace for vendor delay tracking.
Open pageHow it works
See how source capture, extraction, monitoring, and controlled sharing fit together.
Open pageFAQ
Common questions
What inputs can teams extract from?
Polytrace can work across messages, attachments, contracts, forms, notices, reports, file previews, and captured web content. Exact rollout scope depends on the workflow and how much of the source record the team needs to keep available.
Can we mix rules and AI extraction?
Yes. Many strong workflows mix both. Teams use explicit rules where the source is predictable and AI extraction where the language or layout is too variable for rigid selectors alone.
What if our data is mostly unstructured?
That is a common reason teams use Polytrace. When fixed rules are too brittle, AI extraction can turn variable input into reviewable structured fields while keeping the output tied to source evidence.
Can Polytrace maintain one latest row per tracked item?
Yes. For workflows that need one latest row per vendor, contract, case, or other tracked item, Polytrace can keep one up-to-date row based on the fields that define it.
Can teams review extracts before they are shared?
Yes. Review and correction are built into the structured-data workflow so teams can confirm or fix important values before those values drive alerts, shared outputs, or downstream delivery.
Next step
See structured extraction in a live workflow
A strong demo starts with one real document set and one field set the team needs to trust.