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Freight
CS

Freight quote request email parsing

Automate freight quote request ingestion from emails to speed up response times, leading to higher win rates. Extract key shipment details from unstructured emails, standardize data, and respond faster with Parabola.

How to automatically parse freight quote requests with AI

Email remains an account manager’s most critical tool, with revenue-driving quote requests constantly flowing in. A slow response to a quote request could result in lost business, but replying quickly is easier said than done since quotes are determined based on dozens of variables often found within unstructured email bodies.

Leading freight teams use Parabola to automatically ingest quote requests, leveraging AI to translate unstructured email bodies into standardized, structured data. Beyond using AI to extract key details like the freight method, pickup and delivery information, and cargo details, teams are using Parabola to pipe real-time notifications into places like Slack and Teams to quickly provide visibility into open requests. By taking it a step further and pushing request data into internal systems, teams can generate quotes in near real-time to boost win rates.

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As soon as we launched the first component of the automation, we saw an increase in our top-line compliance figure. It’s almost like every week we hit a new all time best.
Alastair Streitz
Sr. Manager, Strategic Operations at Uber Freight
Freight quote request email parsing FAQs
What is freight quote request ingestion?
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Freight quote request automation is the process of extracting shipment details from emails, PDFs, or other unstructured formats and converting them into structured data for quicker processing. This enables freight teams to reduce manual data entry, improve response times, and increase win rates by responding to customers faster.

How to use AI to process freight quote requests in Parabola?
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  1. Use a Pull from inbound email step to fetch incoming freight quote requests — explicitly pulling in the email subject and body.
  2. Apply the Extract with AI step to parse key shipment details, such as pickup and delivery addresses, freight method, container type, weight, and dimensions.
  3. Standardize the extracted data using steps like Edit Columns and Standardize with AI to ensure consistency in format and structure.
  4. As necessary, use an Add text column step to add additional columns required to match the output format required by external systems.
  5. Push the cleaned and structured data into a pricing tool, database, Google Sheet, or channel like Slack using a step like Send to API or Send to Google Sheets.
Tips for parsing freight quote requests in Parabola?
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  • Set up auto-forwarding rules to ensure relevant emails will always flow into Parabola.
  • Use AI-powered extraction to capture data accurately from varying email formats and structures.
  • Standardize column names and data fields early in your flow to maintain consistency across requests.
  • Integrate directly with pricing tools and TMS platforms to provide instant quotes where possible.
  • Automate customer replies to speed up response times and improve win rates.

What other resources are available on parsing freight quote requests?
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