How to build a rate database from unstructured carrier quotes?
To build a rate database from unstructured carrier quotes (emails, PDFs, spreadsheets with inconsistent layouts), parse each quote through an extraction step that maps free-form text and tables into a fixed schema, then store the result in a searchable database instead of keeping quotes as separate documents.
What makes carrier quotes "unstructured"
A carrier quote might arrive as a one-line email ("Chicago to Dallas dry van, $1,850, good through end of month"), a PDF rate confirmation, or a spreadsheet tab — none of them use a consistent schema. Building a usable rate database means converting all of these into the same structured fields regardless of how the quote originally arrived.
Steps to build the database
- Collect quotes as files (PDF, XLSX, CSV) — save email quotes as a document or convert them to a supported file format before upload.
- Run each file through a parser that extracts origin, destination, equipment, rate, and validity window into a fixed schema.
- Validate extracted rows against expected formats (numeric rate, valid date range) before storing — flag anything that fails rather than silently accepting bad data.
- Store validated rows in a searchable database so future quote requests become a lookup, not a re-read of the original quote.
Where RateParse fits
RateParse handles the parsing and storage steps: upload a carrier quote as a PDF, XLSX, or CSV file and it extracts lanes into the searchable schema in ~1 minute, validating output before storage and flagging rows that fail validation instead of silently storing partial data. Direct parsing of raw email text (without first saving it as a file) is not part of the current scope.
Frequently asked questions
Can I upload a carrier quote that came as a plain email, not an attachment?
The current parser accepts file uploads (PDF, XLSX, CSV) — a quote received as plain email text needs to be saved into one of those formats first before uploading.
What happens to quotes that fail extraction?
A quote that fails schema validation is flagged with a per-row error summary rather than silently stored as an incomplete or incorrect rate, so you know to review it manually.