Since January 2025, our system has processed 10,969,710 freight quotes. Not web traffic. Not page views. Actual freight quotes.

Since January 2025, our system has processed 10,969,710 freight quotes.

Not web traffic. Not page views. Actual freight quotes — origin, destination, pallet count, mode, rate returned.

In February 2026 alone, we processed 1.27 million quotes in a single month. That is 45,000 quotes per day. 1,875 per hour. 31 per minute.

To put that in context: Old Dominion, the best-run LTL carrier in the industry, moves 47,288 shipments per day. We process nearly as many quotes per day as ODFL moves shipments — and our volume is growing 3.3x year-over-year while their tonnage declined 8.2% in Q4 2024.

The Growth Curve

Month	Quotes Processed	Quotes Priced	Pricing Coverage
Jan 2025	387,188	51,088	13.2%
Mar 2025	773,378	135,865	17.6%
Jun 2025	694,621	170,864	24.6%
Sep 2025	915,189	355,315	38.8%
Nov 2025	828,566	391,495	47.3%
Feb 2026	1,267,661	550,460	43.4%
Mar 2026	1,029,955	466,102	45.3%

Quote volume grew 3.3x in 13 months. Priced quotes grew 10.8x — meaning the system is not just processing more queries, it is covering more lanes.

In January 2025, 13% of quotes received a rate. By March 2026, 45% did. The system is learning more lanes, covering more markets, and pricing more freight every month.

Why This Matters: The Pricing Data Moat

Traditional LTL pricing is a snapshot. A sales rep negotiates a tariff discount. That discount applies until the next contract cycle — typically 6–12 months. The rate has little relationship to real-time supply and demand on a specific lane.

Here is how the six public LTL carriers price freight:

Carrier	Pricing Model	Data Input	Update Frequency
Old Dominion	Tariff + negotiated discount	Historical shipment data, cost model	Contract cycle (6–12 months)
XPO	AI-driven yield management	Internal shipment + demand forecast	More frequent, but still lane-by-lane
Saia	Dynamic pricing (in development)	CEO Holzgrefe: "yield over volume" — still tariff-based	Contract cycle
TForce	Tariff + discount	CEO Bedard: "We lost so many SMB accounts and replaced them with corporate accounts at sometimes negative margins"	Reactive
FedEx Freight	National tariff	Revenue per shipment up 1.2%, but volumes down 6%	Contract cycle
ArcBest	Tariff + negotiated	Pricing up 5.9%, but tonnage down 14.3%	Contract cycle

Every one of these carriers faces the same dilemma in a soft freight market: raise prices to protect yield and lose volume, or cut prices to protect volume and lose margin. TForce chose volume and ended up with negative-margin corporate accounts. Saia chose yield and is defending an 87% operating ratio. Neither approach is structural.

An AI pricing engine with 11 million data points prices differently. It sees:

• Which lanes are densifying (more quotes = more demand)
• Where capacity exists (carrier availability signals)
• How rate sensitivity varies by pallet count, distance, and market
• Seasonal and weekly patterns that static tariffs miss
• Most importantly: how price responds to density — as more freight flows through a lane, the actual cost per shipment declines

The more quotes the system processes, the better the pricing gets. This is a flywheel that tariff-based carriers structurally cannot build — their pricing is a negotiation, not a data product.

What 11 Million Quotes Reveal

1. Shippers Quote Constantly Because They Don't Trust the Rate

The average shipper is not quoting once and booking. They are quoting the same lanes repeatedly — comparing, benchmarking, checking rates before committing.

This behavior signals that the market lacks pricing transparency. Legacy LTL pricing — tariffs, discounts, fuel surcharges, accessorials — creates uncertainty. Shippers quote frequently because they do not trust yesterday's rate.

An AI pricing engine turns this behavior from noise into signal. Every quote teaches the system something about demand on that lane.

2. Density Creates Itself

The lanes with the most quotes are the lanes with the most shipments. The mechanism: as quote volume increases on a lane, the pricing engine has more data → more accurate pricing → higher conversion → more shipments → better carrier rates → even more competitive pricing.

The highest-volume markets — LA, NY/NJ, San Francisco, Dallas, Chicago — are the markets where conversion is highest. This is the density flywheel, and it is visible in the data.

Compare this to what TForce is experiencing: CEO Bedard described density as "[expletive]" and said "if you can't get the density organically from your sales team, you have to do more M&A to improve your density." TForce is trying to buy density. This system builds density from quote data.

3. Coverage Is the Constraint — and the Roadmap

550,460 priced quotes out of 1,267,661 total in February means 56.6% of quote requests did not receive a rate. Every unpriced quote is demand signal: a shipper wanted to move freight on this lane, at this time, with this pallet count.

When enough unpriced quotes accumulate on a lane, it becomes a candidate for carrier procurement and coverage expansion. The quote data is not just a pricing tool — it is a network planning tool.

No legacy carrier has this. ODFL plans network expansion by building terminals — a $350 million/year decision process. Saia opened 21 terminals in 12 months based on where Yellow had facilities, not where demand data pointed. This system tells you exactly where to add coverage next, based on millions of actual shipper queries.

The 15-Market Map

Quotes concentrate in 15 major markets:

Market	Position	Density Status
Los Angeles	#1 — 147K completed shipments, 287 customers	Critical mass — flywheel spinning
New York/New Jersey	#2 — 76K shipments, 243 customers	Critical mass
San Francisco	#3 — 38K shipments, 146 customers	Approaching critical mass
Dallas	#4 — 35K shipments, 166 customers	Approaching critical mass
Tampa	#5 — 31K shipments, 77 customers	Building
Orlando	#6 — 30K shipments, 86 customers	Building
Columbus	#7 — 29K shipments, 95 customers	Building
Chicago	#8 — 24K shipments, 142 customers	Building
Miami	#9 — 22K shipments, 133 customers	Building
Atlanta	#10 — 15K shipments, 106 customers	Building

The pattern: markets that crossed critical mass early now have the best pricing, highest conversion, and most active shippers. Markets below critical mass are approaching it — and the quote data shows exactly how close each one is.

What Comes Next

At current growth rates, the system will process over 15 million quotes in 2026. Each quote makes the pricing engine smarter, the coverage map wider, and the density on existing lanes deeper.

Meanwhile, the incumbent carriers are fighting a different battle: Old Dominion spent $771M in 2024 capex to maintain ~260 terminals with declining tonnage. XPO carries ~$3.3B in long-term debt while opening 25 new facilities. Saia burned through $276M in cash opening terminals at 95% OR. TForce is running at 97.3% OR and calling the results "a disaster."

11 million quotes is not a vanity metric. It is the foundation of a pricing engine that compounds — where every new quote, every new shipper, and every new lane makes the entire system cheaper and more accurate. That is a fundamentally different model than negotiating tariff discounts on a carrier-by-carrier basis.