How Super K Express transformed trucking operations with Vektor TMS

Super K Express does not have a founding story that begins with a vision board and a venture round. It begins with a grandfather who knew Georgia's industrial corridors better than most people know their own neighborhoods, a father who turned regional relationships into a real fleet, and a third generation that inherited both the trucks and the obligation to keep getting better. By the time the current ownership took over day-to-day operations, Super K had grown into a 50-to-100-truck operation running simultaneous local and long-haul lanes out of Union — a genuinely unusual operating model that most TMS vendors never bothered to design for.

The complexity that comes with running both local and OTR under one roof is hard to overstate. Local drivers — the ones doing yard-to-yard shuttle runs, turning four to twelve loads a day, punching out and going home — need dispatch to be fast, paperwork to be instant, and pay to be calculated on a stop-count basis. Long-haul OTR drivers need multi-stop dispatch sheets, accurate ETA tracking, BOL capture at each stop, and settlement statements that account for fuel advances, layovers, and per-diem. The same dispatch board has to serve both realities at once.

Compounding the operational complexity was a rate book problem that had quietly metastasized over decades. Super K had accumulated more than forty active customer accounts, and almost every one had a different rate structure negotiated over years of relationship-building. Some customers paid per mile with distance-based tiers. Others paid a flat per-load rate. A handful had weight-based tables with minimums. Nearly all of them had fuel surcharge riders that referenced DOE fuel index tables and reset on a different day of the week. The institutional knowledge required to bill any given load correctly lived inside the heads of two or three people in the office — and it was becoming a fragile single point of failure.

The operation was running on spreadsheets and memory. Not because management lacked ambition — but because no system they had evaluated was willing to get into the details with them. Legacy TMS vendors wanted to simplify the rate structure. Generic freight software assumed a uniform pay-per-mile model. The tools that offered EDI capability required enterprise contracts that assumed a 500-truck fleet. Super K kept waiting for a platform that could handle what they actually did, not a sanitized version of it.

The conversation with Vektor began differently than previous TMS evaluations. Instead of a demo built around the generic carrier use case, the discussion started with a question: how many distinct rate structures do you currently manage, and what does each one actually look like? When the answer came back as forty-plus contracts with radically different logic, the response was not a workaround or a promise to handle it manually. It was a feature walkthrough of Rate Matrix.

Rate Matrix is built for exactly the situation Super K had inherited. Each customer account gets its own rate structure configured independently — per mile, per load, flat, weight-tiered, or any combination. Fuel surcharge tables are attached at the account level and update automatically when the DOE fuel index changes. When a dispatcher enters a load and selects a customer, the system calculates the correct rate without asking anyone to remember which table applies. Forty-plus years of negotiated pricing logic moved out of two people's heads and into a system that could not forget, miscalculate, or go on vacation.

The EDI integration came next. Super K's major shipper customers required FourKites and MacroPoint tracking as a condition of doing business — and their EDI tracking scores had been slipping because the manual check-call process was not keeping pace with the volume. Vektor's carrier EDI handled the full document set: EDI 204 load tenders coming in, EDI 990 tender acceptances going out, EDI 214 status updates automatically triggering at each stop milestone, and EDI 210 invoices transmitting upon proof of delivery. The tracking score did not improve incrementally. It jumped — and held above ninety percent because the automation does not depend on a dispatcher remembering to send a status update.

AI Automations addressed the load entry problem that had been quietly eating hours each week. Every inbound rate confirmation — email attachments from brokers, PDFs from direct shipper EDI portals, scanned paper documents from older accounts — now runs through Vektor's AI extraction layer. The system reads the pickup and delivery addresses, reference numbers, appointment windows, commodity details, and rate, populates the load record, and queues it for dispatcher review. What used to require keyboard entry and verification across three systems takes a fraction of the time, with fewer data-entry errors reaching the driver.

The first impact that registered was the one nobody expected to happen overnight: billing accuracy. When Rate Matrix went live with all forty-plus customer accounts configured, the number of invoices that required manual correction dropped immediately. Fuel surcharge discrepancies, which had been a recurring friction point with several customers, stopped appearing. Not because the team got more careful — but because the calculation was no longer something a human being was doing under time pressure. The institutional knowledge that had lived in two people's heads became auditable, reproducible logic inside the system.

The EDI tracking score improvement changed something more significant: Super K's standing with its major accounts. Visibility networks like FourKites and MacroPoint score carriers on the consistency and timeliness of their status updates, and those scores feed directly into shipper routing guides. A carrier below threshold gets routed around. A carrier consistently above threshold gets offered volume. Super K had the operational capability to move freight reliably — it just had not had the automation infrastructure to prove it at the frequency the networks required. Now it does. The scores reflect the operation.

The Profit Engine surfaced a set of insights that changed how the family thought about their own business. When every load carries a complete P&L — revenue, fuel cost, driver pay, estimated overhead — patterns emerge that gut feel alone cannot reliably catch. Certain local shuttle lanes that looked profitable at the invoice level were eroding margin once driver time per stop was factored in. Certain OTR lanes that seemed expensive to run were actually the highest-margin freight in the portfolio once fuel efficiency and driver type were accounted for. Decisions about which customers to prioritize, which lanes to grow, and which rate books needed renegotiating shifted from intuition to evidence.

For the drivers, the app closed a gap that had existed since the local-and-OTR model created it. Local drivers who run a dozen loads a day now check in and out of stops through the app, upload BOLs on the dock rather than bringing a paper stack back to the office at shift end, and see their pay calculations in real time. OTR drivers get their full dispatch sheet on the phone before they leave the yard, capture PODs at each delivery, and check settlement statements without calling the office. The Customer Portal gave Super K's shipper accounts the same visibility that their enterprise customers had been asking for — self-service tracking that answered the question before it became a phone call to dispatch. Three generations of operational knowledge, encoded and distributed across every device in the fleet.