Introducing Parallel Agents: The First AI Built for Parallel Freight Execution
Death by a Thousand Papercuts
Today’s enterprise logistics teams are being squeezed. Costs are increasing from inflation and labor, while freight workflows still rely on humans processing tasks one by one.
The real cost driver nobody is talking about is the nonstop context switching of freight operators. They are often overwhelmed by a barrage of small, menial tasks including emails, quotes, documents, and more across different contexts. There’s a natural limit to how efficient a freight operator can be with each hour in this start-and-stop pattern of work.
The moment volume spikes, backlogs form, creating the real risk of missing customer SLAs and driving up cost per shipment. Staffing your way out of the problem no longer scales when labor costs are climbing faster than revenue.
Deploying AI is the path forward. A McKinsey study showed that AI-enabled supply chains can reduce administrative costs by up to 80%. The teams that pull ahead won’t be the ones adding more headcount; they’ll be the ones adding AI to streamline workflow execution at a far lower cost.
The Myth of AI Assistants
AI assistants have emerged to help workers operate faster at tasks like writing emails or streamlining research. But these tools still require real attention and manual input, which doesn’t eliminate the burden of context switching.
Within each task, coordinators deal with messy documents, multiple email conversations, unpredictable requirements, and customer context scattered across portals, calls, and spreadsheets. None of this arrives in order or neatly structured, which creates an immense amount of overhead and cognitive load.
That’s why traditional AI assistants stall in freight. They’re built for one-on-one interactions: a human prompts the AI, the AI helps with a single task, and the human completes it. It might make one person a bit faster, but it doesn’t scale across messy, concurrent contexts. If shipment volume doubles, you still need twice as many people prompting the assistant.
AI that relies on perfect inputs or human prompting collapses under real operational conditions. Freight is unstructured, high-pressure, and full of exceptions that require high context. Most AI simply isn’t built for that.
A New Paradigm: Parallel Execution for Freight
Parallelism is a foundational concept in computing, splitting work into simultaneous threads to process work in less time. The challenges of freight operations present a unique opportunity to apply this concept with AI workers.
Pallet’s Parallel Agents help logistics teams automate manual work across dynamic contexts without human prompting, dramatically increasing throughput and reducing cost to serve. Here are some examples of Parallel Agents in action:
Workflow | The Old Way: One Task at a Time | The New Way: Parallel Agents |
|---|---|---|
Getting the best quote for a shipment from your carrier network | Emailing multiple carriers, waiting for their response, and negotiating across different email threads. | Automated mini-bid Parallel Agents automatically manage multiple email negotiations and surface the best rate and performance. |
Processing multiple shipments from a packet of PDFs. | Processing each order and responding in 4 hours that the orders are received. | Parallel processing Pallet dynamically spins up multiple AI workers to process every shipment in the same time it would take to process one shipment. |
Responding to a tracking request for a list of shipments. | Tracking down the status of each order across emails, portals, and phones. | Dynamic agent actions Multiple AI workers independently decide on the best way to track a shipment. |
What once took hours of context switching now takes seconds of review. For customers using Parallel Agents to run a freight bid, they see a 10% reduction in freight costs.
Across the entire workflow, Parallel Agents take on the tasks that slow teams down. They decompose complex emails across multiple shipments, extract BOLs and invoices, and run rate checks or bids in parallel. They sync data across TMS, WMS, and carrier portals instantly, consolidate tracking from multiple sources, and fetch order updates on demand.
The result is faster decisions, fewer bottlenecks, and significantly lower operating costs.
Under the Hood: How Parallel Agents Deliver Speed and Accuracy
Pallet’s architecture was built for the messy reality of freight operations. By combining proprietary logistics data, Deep Reasoning, and the Enterprise Memory Layer, Pallet executes high-context workflows accurately in parallel.
Here’s how it works for a 3PL running a transportation bid: When a shipment request arrives, Pallet uses freight-tuned LLMs to parse emails and PDFs. The AI agent extracts information such as origin, destination, exceptions, and cargo type and stores these details as a quote request in the TMS.
Then Pallet spins up specialized AI workers who can manage the full lifecycle of bidding in parallel – this is what we call Parallel Agents. Agents scan internal data stores for price benchmarks for the lane and then send pricing emails to each carrier in its network.
As replies come in, agents actively negotiate pricing, while remaining aware of context across each conversation. Then Pallet merges workflows to combine results into a single consolidated output like a comparative bid table.
But speed means nothing without accuracy. That’s why Pallet uses field-level confidence scoring and cross-model validation. High-confidence fields are processed automatically. Low-confidence fields, such as unclear surcharges or unusual requirements are flagged for human review. Operators review the 5% of inputs that require judgment, and Parallel Agents handle the other 95%.
Non-Linear Growth: The "10x Coordinator"
Parallel Agents aren't just about saving a few minutes on an email. They're about Non-Linear Growth. Because they work in the background, they allow a single coordinator to manage 10x the freight volume across even the most complex freight use cases.
Pallet transforms the margin profiles for logistics operators, unlocking lower cost to serve while delivering faster, more accurate results.
