When a broker receives a load tender or a carrier gets a rate confirmation, AI can instantly extract all the key information – addresses, appointment times, freight details, rates, reference numbers, etc. – and populate it into their system.

Freight matching platforms use AI to connect loads with the ideal carriers in real-time. They consider equipment type, location, hours of service, and historical lane preferences. For brokers, AI can suggest which carrier is best suited for them and even automatically send the load offer.

Beyond just matching one truck to one load, AI load building can optimize more complex scenarios. AI load planning tools can consolidate shipments to maximize trailer utilization and minimize empty miles. This is especially valuable for LTL carriers or any fleet trying to reduce “empty space” on trucks.

Once a load is built, getting it delivered efficiently is the next challenge. An AI system can detect that a truck is about to hit a major traffic jam and proactively re-route it to save time. Or it might reschedule a pickup if it sees a delay that will cause a missed appointment, notifying all parties automatically.

Rather than a dispatcher manually refreshing multiple load boards, AI-powered tools can integrate with load board EDIs/APIs and continuously scan for matches. They can factor in a carrier’s preferred lanes, average rate per mile needs, hours of service, and past loads to rank new postings. Over time, the AI gets smarter at picking the loads you’re most likely to want.

By verifying and filtering out incomplete or misleading load posts, AI load sourcing spares the frustration of chasing loads that aren’t actually viable. The result is that carriers only see accurate, ready-to-book loads, and brokers get interest from carriers that truly fit the load’s requirements. This improves trust on both sides of a transaction.

Forecasting algorithms can match freight not just on current data but on predicted market conditions. For example, a broker’s AI might notice that every Thursday afternoon a certain region has empty trucks heading home. The broker could then advise shippers or adjust pricing to route freight into that capacity. This predictive element is something only AI can do at scale by crunching historical patterns and real-time signals.

Brokers can cover loads faster when the system is doing a mini “email blast” of a new load to the top 5 carriers who’d likely want it. If a match is found, the load can be booked with minimal human involvement. On some platforms, carriers can even configure an “auto-book” setting where the AI will automatically book them on a recommended load if it meets their pre-set price and parameters.

You drop in any source: rate confirmations, PDFs, emailed docs, spreadsheets, even handwritten notes. Our AI extracts data (stops, pickup/delivery times, line-items, rates, PO numbers, equipment needs).

Our engine maps extracted fields to the TMS data model (addresses → geo, dates → timezone-normalized windows, rate lines → linehaul/fuel/fees). This removes manual labor and standardizes formats.

Our automated checks flag missing/contradictory fields and apply business rules (e.g., equipment mismatch, weight over limits) while also auditing invoices/BOLs/RCs to reduce rejections.

Our planner uses that clean data to create an optimized load: match equipment type, combine stops where appropriate, sequence pickups/deliveries, and calculate estimated deadhead and drive time to propose optimal truck/trailer/driver assignments.

Our integrations ecosystem helps you connect with ELDs, accounting systems, load boards, fuel platforms, ERPs, factoring, and carrier portals, enabling bi-directional data flow, faster decision-making, and quick solutions with a truly unified logistics stack.

Once a load is built, the system can auto-tender to preferred carriers, publish to integrated load boards or digital freight marketplaces, or initiate a bid process with carriers, using preconfigured rules (price thresholds, preferred carriers, etc.).

Integrated driver app/EDI/API connections provide live tracking, proof of delivery capture, and exception handling that feeds back into the TMS.

Completion triggers invoicing and Smart Validation checks (matching PODs, rates, accessorials) to reduce disputes and payment delays.

By cutting out manual typing, companies have seen 60% less manual effort on load processing tasks.

Why it matters: More loads processed per day and less backlog during peak times.

Accurate data ingestion, parsing, and verification ensure 80% fewer errors in load data on average.

Why it matters: Fewer load board fall-offs, denied pickups, or billing corrections.

Helps match loads to carriers (or trucks to loads) within seconds, considering dozens of factors (location, capacity, past performance, etc.).

Why it matters: Improves load coverage speed and reduces truck idle time.

Carriers using AI-driven planning have cut deadhead by 15–25%.

Why it matters: Every empty mile is fuel burned for zero revenue. 

Improves on-time delivery rates and fleet efficiency (by roughly 10–20%.

Why it matters: Drivers spend less time in traffic or taking long detours.

The right driver and equipment for each load, considering hours of service, driver home time requirements, certifications (like Hazmat), and even personal preferences.

Why it matters: The best-fit assignment with safe, on-time delivery and happy drivers.

One AI system cut order delays and miscommunications by 30% by automating updates and follow-ups.

Why it matters: Dispatchers and carrier reps spend less time on the phone repeating the same information.

AI to answer 2,000+ emailed quote requests daily with instant pricing.

Why it matters: Brokers can turn around customer quotes in seconds, increasing win rates. 

Can prevent problems and flag unreliable partners or potential service failures or hurdles ahead of time.

Why it matters: Dispatchers and carrier reps spend less time on the phone repeating the same information.

After delivery, AI can audit documents (PODs, invoices) to cut invoice rejects and fast-track billing rejections by up to 90%.

Why it matters: Brokers can turn around customer quotes in seconds, increasing win rates.