Transportation teams don’t suffer from a lack of data. They suffer from the time it takes to turn that data into decisions.
That reality is what pushed GoodShip to take a different path with its latest product launch. Rather than adding another dashboard or layering in agent-based automation, the Bellevue, Washington-based freight orchestration platform has introduced Laney, an AI transportation analyst designed to sit alongside human decision-makers, not replace them.
Laney is embedded directly into the GoodShip platform and allows users to ask complex, network-wide questions through a conversational interface, instantly returning analytics, optimization scenarios, and custom reports tied directly to their live transportation data.
According to GoodShip co-founder and CEO Ryan Soskin, the timing reflects how shippers actually operate today. “There are phases to how we’re rolling Laney out,” Soskin said. “Phase one is very clearly focused on the transportation analyst. As we build and iterate, there’s more she can do, but we’re intentionally different from agentic AI approaches. We’re focused on human-in-the-loop decision-making.”
That distinction matters in an industry where decisions around procurement, carrier performance, and service tradeoffs carry real financial and operational risk. GoodShip’s platform has always leaned into strategic planning, running procurement events, analyzing networks holistically, and then drilling down to identify improvement opportunities. Laney extends that approach by accelerating the analytical work that often slows teams down.
“Analyzing transportation networks is at the core of what GoodShip does,” Soskin said. “The value comes from having an analyst that can work faster than a human and surface insights teams might otherwise miss.”
Unlike generic AI chat tools, Laney lives entirely inside the GoodShip platform. Users can ask questions in a chat-style interface while Laney pulls from the shipper’s actual network data: loads, carriers, contracts, procurement events, service levels, spend, and third-party benchmarks already housed in GoodShip.
For transportation managers, that means questions that once required multiple reports, spreadsheets, or analyst support can now be answered on demand.
“You can ask something like, ‘What’s the impact of removing a specific carrier from our network, what’s our exposure?’” Soskin said. “Laney can access everything within that shipper’s instance of GoodShip and give you an answer immediately.”
That capability extends across day-to-day freight management. Laney can surface KPI issues, flag carrier failures, analyze procurement scenarios, and model cost and service impacts across lanes and modes. She can also generate instant reports complete with tables and charts, making it easier to share insights internally without spending hours building presentations.
The embedded nature of the AI is critical, Soskin said, both for performance and trust. Each customer’s instance of Laney operates entirely within that shipper’s own data environment, with strict isolation from other customers. Laney is purpose-built to understand the structure and context of a shipper’s historical network data. “Everything is scoped, permissioned, and controlled at the individual customer level,” Soskin said.
Trust, however, remains a central concern anytime AI is influencing freight decisions. GoodShip has built transparency and feedback directly into the experience. Users can give immediate thumbs-up or thumbs-down feedback on Laney’s responses, along with freeform comments, which the company actively monitors to correct issues quickly.
“If a user wants to validate a response, they can still go into the network and double-check it,” Soskin said. “The difference is it’s much easier to sanity-check without digging through layers of data.”
While it’s still early to quantify time savings, Soskin says the biggest shift is how teams spend their time. Instead of reacting to issues buried in spreadsheets, transportation teams can focus more on relationships, strategic opportunities, and proactive cost and service improvements. “It’s less reactive and more proactive,” he said.
Laney’s launch represents the first step in what GoodShip describes as a move toward self-orchestrating transportation networks. Soskin is careful to emphasize that this is a phased journey, not an overnight leap to autonomy.
Over time, lower-risk tasks, such as notifying carriers about performance issues, could become more automated, while humans retain control over higher-risk decisions.
“The vision is being able to dial Laney up or down depending on where you’re comfortable taking your hands off the steering wheel,” Soskin said. “Human involvement is still the competitive advantage. AI works best when it’s in the background, making people better at their jobs.”
For GoodShip, Laney isn’t about replacing transportation professionals; it’s about finally giving them an analyst that can keep up.
