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How a High-Growth Ready-to-Drink Cocktail Brand Built Freight Procurement and Execution Visibility Across Three Countries as It Scaled Toward National Distribution

3 countries

Manufacturing plants in Mexico, California, and Canada — unified under one AI procurement platform

5 warehouses

Agua Calientes, Laredo, Buffalo, Detroit, and Ontario — all outbound OTR on one execution layer

8 carriers

Arrive Logistics, Axle Logistics, TQL, and others — managed through structured AI procurement events

Growth-ready

Platform scales with the brand — no headcount additions required as shipment volume compounds

We were growing faster than our logistics infrastructure. Every new distributor, every new retailer, every new SKU added complexity we couldn't manage manually. We needed a platform that could grow with us without us having to grow the team at the same rate.

VP, OPERATIONS — HIGH-GROWTH READY-TO-DRINK COCKTAIL BRAND

The Problem

The company makes party-sized ready-to-drink cocktails and has grown from a Shark Tank investment into one of the fastest-growing beverage brands in the United States. Its manufacturing footprint spans three plants — Agua Calientes in Mexico, Stockton in California, and Ontario in Canada — with outbound shipments flowing from five warehouses to a national distribution network covering retail chains, convenience stores, and on-premise accounts. $15M in annual freight spend across that network managed through 8 carriers on OTR FTL and LTL moves. The logistics operation was scaling with the brand. The infrastructure supporting it was not.

Freight procurement ran manually — carrier selection based on relationships and routing guides, RFQ processes that required someone to build the event structure in a spreadsheet, distribute templates by email, collect responses from carriers in varying formats, normalize them manually, and then build the analysis the operations team needed to make a decision. With three manufacturing plants operating on different cycles and five warehouses with distinct outbound patterns, coordinating a procurement event across the full network required coordination overhead that slowed everything down. Pre-bid intelligence — what are market rates doing, which carriers are performing, which lanes are overpriced relative to alternatives — did not exist in a systematic form.

Shipment visibility was equally fragmented. Without a unified execution layer, tracking outbound shipments from the Mexican plant to Laredo, from Stockton to national retail DCs, and from Ontario to US border crossing points required carrier-by-carrier portal access and manual status consolidation. The operations team that needed to tell a retailer whether a shipment was on track had to check three or four systems before they could answer confidently. Exception management — late pickups, missed delivery windows, detention charges accumulating at shipper facilities — happened reactively because the data to act proactively was not in one place.

Microsoft Business Center (Dynamics) was the ERP. No TMS existed. The company was growing 40-plus percent year-over-year and adding distribution points continuously. Every new customer, every new lane, every new carrier relationship added complexity to a manual process that was already at capacity. The decision to deploy Freehand was not a response to a crisis. It was a deliberate choice to build the logistics infrastructure before the growth curve made the manual approach untenable.

What Freehand Did

Freehand deployed freight procurement and execution visibility across the full three-country, five-warehouse, eight-carrier network — covering outbound OTR FTL and LTL from all manufacturing plants to domestic distribution points. The AI Procurement Analyst replaced the manual RFQ cycle with structured automated events: lane and lot construction from the company's own shipment data, carrier notification via pre-built bid templates, bid ingestion and normalization regardless of carrier submission format, anomaly detection for deviations beyond configured thresholds, and scenario modeling across cost and service-level parameters — all before the operations team reviews the output.

Rate benchmarking integration connected the AI Procurement Analyst to live market data for domestic OTR lanes, giving the operations team access to current market intelligence during active bid events rather than relying on carrier-submitted rates and historical relationships. When a carrier's bid deviates significantly from current market rates, the system flags it automatically. The procurement cycle that had run on team capacity now runs on market conditions — when rates move, the platform identifies which lanes warrant re-bidding without waiting for an annual cycle.

The Rate Manager Agent replaced the manual rate card management process with a live repository covering all carrier contracts across all lanes — FTL, LTL, and the Mexico-to-US cross-border configurations that required separate accessorial logic for border crossing fees, drayage, and customs clearance charges. When a new carrier is added, the onboarding happens in days rather than weeks. When contract rates change, the update happens in one place and applies to every procurement evaluation and audit validation that follows.

Execution visibility gives the operations team a unified view of all outbound shipments — from Agua Calientes to Laredo, from Stockton to national DCs, from Ontario to cross-border points — in a single platform with milestone tracking, exception alerts, and carrier performance scoring. When a retailer needs a shipment status, the answer comes from one system rather than from a circuit of carrier portal logins. The Spend Intelligence Agent provides real-time freight cost visibility by carrier, lane, plant, and warehouse — the data foundation the operations team needs to make the next procurement cycle smarter than the last as the brand continues to grow.

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