How does the Tabi-Kleinschmidt integration reduce quoting time?

The integration automates the manual rate-setting process by intercepting quote requests at Kleinschmidt's network level and immediately routing them through Tabi's Pricing Logic Engine, which applies pre-configured rules based on customer, lane, equipment type, and market conditions. API-based connections return quotes in approximately 3 seconds, while RPA connections return rates in 35-50 seconds, eliminating the need for brokers to manually calculate each rate.

What is Capacity IQ and how does it impact pricing decisions?

Capacity IQ is Kleinschmidt's predictive freight-capacity platform that analyzes over 150,000 loads daily across North America to forecast where carrier trucks will likely become available. This data allows brokers to factor expected carrier availability into pricing decisions before submitting quotes, rather than after, enabling more confident rate-setting in volatile markets where capacity fluctuates rapidly.

Who currently uses the Tabi platform and what scale of volume does it handle?

Tabi is used by more than 100 logistics companies, including over 20 of the top 100 U.S. brokerages. The platform currently connects with 88+ shipper platforms and integrates with major data providers including DAT, Greenscreens, C4, Sonar, and Truckstop. It processes over 1 million freight quotes per month.

Why does pricing consistency matter for freight brokers?

According to Tabi's CEO, freight brokerage is increasingly crowded with thinning margins, making speed and consistency the key differentiators between successful brokers and struggling ones. Automated rules ensure every team applies the same pricing strategy to similar opportunities, eliminating discrepancies from manual processes and ensuring profitability targets are met consistently.

What is Kleinschmidt's role in the broader integration ecosystem?

Kleinschmidt provides managed EDI, API, and B2B integration services that connect businesses with trading partners. It recently acquired eBase Solutions to expand internal enterprise integration capabilities. The Tabi partnership leverages Kleinschmidt's external connectivity with eBase's internal integration expertise to link incoming quote requests with automated pricing and capacity intelligence.

Technology & Innovation

Moderate

Tabi & Kleinschmidt automate freight quoting with predictive capacity

Sep 22, 2026

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The signal

Tabi Connect and Kleinschmidt have launched an integrated solution that automates freight quote responses by combining rule-based pricing logic with real-time carrier capacity predictions. The integration allows freight brokers to receive quote requests through Kleinschmidt's network, automatically apply pricing rules through Tabi's engine, and factor in predictive capacity data from Kleinschmidt's platform that analyzes over 150,000 daily loads to forecast carrier availability.

This partnership addresses a critical pain point in freight brokerage: the need for speed and consistency in a competitive, margin-compressed market where manual quoting processes create bottlenecks and inconsistent pricing across teams. S.

brokerages, handling over 1 million quotes monthly.

#freight-quoting-automation

#supply-chain-technology

#rate-management

#transportation-logistics

#carrier-availability

#process-automation

Frequently Asked Questions

What This Means for Your Supply Chain

Simulation Suggestion

strategic

What if automated pricing rules reduce quote response time by 80%?

Simulate the impact of reducing average freight quote response time from 2-4 hours (manual process) to 3-50 seconds (automated integration) on win rates, customer satisfaction, and broker capacity utilization. Model how faster response times affect market share capture in competitive bid situations and volume growth across a 100-broker network.

Run this scenario

Simulation Suggestion

this month

What if predictive capacity data reduces overpriced loads by 15%?

Model the financial impact of incorporating carrier availability predictions into pricing decisions, reducing the percentage of loads quoted above prevailing market rates due to capacity scarcity misjudgment. Simulate improvement in load-to-truck matching efficiency and margin preservation across a broker's book of business.

Run this scenario

Simulation Suggestion

immediate

What if pricing rule inconsistency across teams increases by 10%?

Simulate the operational and financial risk of NOT adopting automated consistent pricing: model the cost of margin leakage due to different teams applying different pricing strategies to similar lanes, the customer service impact of quote inconsistency, and lost quote volume from customers perceiving unreliable pricing.

Run this scenario

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