Reinventing Freight Quotation with AI

Freight quotation is traditionally slow, often manual, and prone to errors—especially when working with diverse formats and outdated pricing models. Our AI-powered customized freight quotation solution revolutionizes the quoting process by parsing complex rate sheets, predicting surcharges, filling data gaps, and dynamically optimizing rates based on context.
Developed in collaboration with FreightExchange, this platform leverages AI, machine learning, and natural language processing to boost quote accuracy, improve acceptance rates, and eliminate inefficiencies across thousands of shipping lanes.

Why Freight Quoting Needs a Rethink

Traditional freight quoting faces several operational bottlenecks:

• Quotes built from static tables, often outdated or incomplete

• Manual effort required to parse carrier spreadsheets and surcharges

• Low acceptance rates (~35%) due to hidden costs or unclear pricing

• No feedback loops to learn from rejected quotes

These issues not only slow down operations but also reduce competitiveness in the freight market.

Our Intelligent Quoting Solution

We built an intelligent freight quotation engine that automates and optimizes every step of the process:

• ^{Parsing & Normalization}
  AI reads Excel files from carriers (even with inconsistent formats) and maps them to a unified schema.
  

• ^{Gap Detection}
  The system identifies missing lanes, outdated rates, and inconsistencies using AI prompts and historical data.
  

• ^{Data Inference}
  When data is missing (e.g., for new routes), AI estimates values using trends, surcharges, and similar lane patterns.
  

• ^{Dynamic Quote Generation}
  The final quote includes base rates + relevant surcharges, delivered with a clear natural-language explanation.
  

Inside our Freight Quotation Custom Solution

• ^{AI-powered parsing of unstructured rate sheets}
  Our solution can automatically read and interpret complex Excel rate files from multiple carriers, regardless of their structure. It extracts meaningful data—even from footnotes and merged cells—without the need for hardcoded templates.
  

• ^{Gap detection logic with natural language reporting}
  The system identifies missing routes shipping lanes, outdated rates, or incomplete categories using AI prompts. It generates human-readable summaries, such as “Rate missing for Jakarta to Brisbane,” or “Rates increased by 12% since last quarter.”
  

• ^{Machine learning models to estimate and fill missing values}
  AI models analyze trends from similar routes and historical data to predict missing or future rates. The system provides estimates like: “Base rate for Fremantle–Singapore is $1,200 based on Q1 surcharges and similar lanes.”
  

• ^{Dynamic surcharge prediction based on shipment metadata}
  Based on shipment details (e.g., container type, delivery urgency, location type), the system applies relevant surcharges such as tail-gate delivery, dangerous goods handling, or residential drop-offs—ensuring full cost transparency.
  

• ^{Smart quote generation with full breakdown and explanations}
  The quoting engine builds a complete pricing breakdown using base rates, modifiers, and surcharges. Quotes are delivered with a clear explanation, increasing customer trust and reducing the need for back-and-forth clarification.
  

• ^{Customer feedback loop to continuously improve pricing models}
  Every quote outcome—won or lost—is captured and analyzed. This feedback informs future pricing, helping the system avoid overquoting and better adapt to customer expectations.
  

• ^{Dashboard for quoting history and insights}
  A visual interface provides access to historical quote performance, win/loss trends, lane profitability, and suggested optimizations—empowering sales teams with real-time insights.

Outcomes That Drive ROI

• ^{Quote acceptance rate increased from 35% to 50%}
  Personalized pricing, AI-driven logic, and transparent breakdowns significantly improved customer trust and quote conversions.
  

• ^{Error rate in quotes dropped by 70%}
  Intelligent data parsing and dynamic surcharge application reduced human errors and outdated information in final quotes.
  

• ^{Time-to-quote reduced from hours or event days to minutes}
  AI automation replaced manual spreadsheet processing, enabling scalable quoting for thousands of lanes.
  

• ^{Back-and-forth cycles reduced by 40%}
  Clear natural-language explanations in each quote helped customers understand pricing immediately—minimizing delays and revision requests.
  

• ^{Customer feedback integrated into pricing models}
  Lost quotes are now a source of insight—feeding continuous improvements in surcharge logic and pricing strategy.
  

• ^{Increased transparency = increased trust}
  Every quote now includes a line-by-line explanation, making pricing logic visible and easy to understand for both internal teams and customers.

Key Features & Capabilities

• ^{Intelligent Excel parsing (even with inconsistent or ambiguous headers)}
  Uses AI to semantically classify column names (e.g., “POL” vs. “Port of Loading”) and map them to a unified schema, allowing clean data extraction even from disorganized spreadsheets.
  

• ^{Gap and anomaly detection in rate tables}
  Detects when expected shipping combinations (e.g., port pairs or container types) are missing or when sudden rate shifts occur, offering proactive recommendations to complete or correct data.
  

• ^{Inference of missing rates using contextual patterns}
  Combines historical data, seasonal context, and domain knowledge (e.g., known surcharge periods) to estimate plausible rate values when information is incomplete or outdated.
  

• ^{Surcharge application based on free-text, metadata, and historical logic}
  Applies surcharges intelligently by analyzing contextual hints (e.g., “manual consignment note” or “overlength cargo”), without needing users to manually flag such cases.
  

• ^{Natural language generation of quote explanations}
  Every quote is accompanied by a clear, human-readable summary of what’s included and why—removing ambiguity and improving customer confidence in pricing.
  

• ^{Continuous learning from rejected quotes and customer feedback}
  Reasons for lost quotes (when provided) are fed back into the system, helping it adapt and avoid similar issues in future quoting—leading to smarter, more competitive offers over time.
  

• ^{Seamless integration with quoting tools and logistics platforms}
  Designed to plug into existing freight management systems, CRMs, or custom portals, the engine can be deployed with minimal disruption while boosting end-to-end quoting efficiency.