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Pallet is a logistics-software company, not a robotic palletizing business or a consumer shipping app. Its current product, branded CoPallet, uses specialized AI agents to carry out repetitive freight workflows—such as order entry, quoting, booking, tracking, appointment scheduling, document collection and payment preparation—inside the systems logistics companies already use.
The company originally built a unified transportation-management, warehouse-management, accounting and billing platform. After selling or transferring that traditional TMS business to Tenet, Pallet shifted its public emphasis toward an AI workforce for high-volume operational work.
What Pallet is
Founders Sushanth Raman and Andrew Geisse said their experience with inefficient software workflows led them to create Pallet. In its October 2024 funding announcement, the company described a fragmented logistics industry dependent on disconnected point solutions and manual processes. Its original platform combined transportation, warehouse, accounting and billing functions in one system (Pallet’s Series A announcement).
By 2025, the company was presenting CoPallet as an “AI workforce” for freight brokers, 3PLs, carriers, freight forwarders and shippers. Pallet’s login page now describes the product as an AI logistics workforce for high-volume, mission-critical tasks (Pallet application).
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“AI workforce” is Pallet’s marketing term. A more precise description is a collection of specialized software agents that operate within bounded workflows, use business rules, take actions in connected systems and escalate exceptions to people.
The logistics problem Pallet targets
A single shipment can generate work across email, PDFs, spreadsheets, carrier portals, a TMS, a WMS, an ERP and accounting software. Staff may repeatedly re-key the same information, chase status updates, book appointments, collect paperwork and reconcile invoices.
These tasks are repetitive but not perfectly predictable. Customer instructions vary by lane and facility; documents arrive in different formats; portals change; and an apparently simple exception can affect a delivery appointment, rate, claim or payment. Pallet’s 2025 funding announcement framed the opportunity as an $11 trillion industry with roughly 10% of spending associated with manual administrative work. That figure is Pallet’s estimate, not an independently verified industry statistic (Series B announcement).
What the AI agents do
| Workflow | Typical agent action | Human checkpoint |
|---|---|---|
| Order entry | Read emails or documents, create or update shipment records, apply customer defaults and identify missing fields. | Resolve ambiguous addresses, dates, quantities or instructions. |
| Quoting | Prepare transactional quotes using shipment, lane, customer and carrier information. | Approve unusual requests, margin overrides or pricing outside policy. |
| Load booking and tendering | Post loads, communicate with carriers, record tender responses and update shipment status. | Review unusual carrier choices, rates or capacity decisions. |
| Tracking | Request updates from carriers or portals, write statuses to internal systems and notify customers. | Investigate missed, contradictory or suspicious updates. |
| Appointments | Contact facilities, book or reschedule appointments and record confirmations. | Resolve conflicts, facility restrictions or service-level risks. |
| Documents | Collect, classify and extract information from varied paperwork, then match it to shipments. | Correct missing, inconsistent or unreadable documents. |
| Billing and payments | Move verified shipment information into invoicing and carrier-payment workflows. | Approve financial exceptions, disputes and payment releases. |
Pallet has described integrations with existing TMS, WMS and ERP environments, APIs, browser portals and document-reading systems. Availability and write permissions are deployment-specific; “works with your stack” should be tested system by system (FreightWaves coverage).
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhy this is more than a chatbot
A chatbot mainly generates or retrieves text. Pallet’s proposed loop is operational:
- Receive information from an inbox, document, portal or connected system.
- Interpret the request or shipment state.
- Apply customer-specific instructions and business rules.
- Take an action in a logistics system or external portal.
- Verify the result and record it.
- Escalate an exception when confidence, authority or data quality is insufficient.
Pallet announced an AI platform for complex, exception-heavy workflow execution in February 2025 (platform announcement). That does not mean an unsupervised digital employee can make every logistics decision. Commercial authority, approval thresholds and human review remain essential, especially for rates, capacity, appointments and payments.
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“Continuous Intelligence” and the Enterprise Memory Layer
Pallet says a manual correction—such as fixing a tender field or applying a carrier instruction—can be retained as reusable operating logic. The company calls this an Enterprise Memory Layer and “Continuous Intelligence”: validated resolutions are tested against historical workflows and made available for similar future exceptions (Pallet’s announcement).
The concept could reduce repeated work, but governance determines whether it is safe. A buyer should ask who approves learned rules, whether memory is scoped by customer, lane, facility or workflow, how obsolete rules expire, whether changes are inspectable and reversible, and how conflicting customer instructions are handled. Public material describes the concept; it does not independently establish performance across all deployments.
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Not necessarily. Pallet first marketed a unified TMS/WMS/accounting platform. In 2025 it shifted toward agents that work across existing systems. Tenet subsequently announced that it acquired Pallet’s TMS business and launched an operating system for cartage, courier and expedited carriers (Tenet announcement).
The practical distinction is that Tenet is the reported successor-oriented home for the traditional operating-system/TMS product, while current Pallet messaging emphasizes an AI automation layer. Asset scope, customer migration terms and product continuity should be confirmed directly before a purchasing decision.
What evidence exists that it works?
Funding and deployment claims
- Pallet announced an $18 million Series A on October 2, 2024, reporting $21 million in total funding at that point (announcement).
- It announced a $27 million Series B on May 27, 2025, reporting $50 million in total funding (announcement).
- A later Pallet post said more than 70 logistics organizations were running the product in production and named Mallory Alexander International Logistics, Knight-Swift Transportation, STG Logistics and Everest Transportation Systems. That is a company-reported count, not an independently verified current customer list.
Reported customer outcomes
Pallet said a midsized carrier reallocated 25 employees who had been doing repetitive order entry, with savings described as being in the millions. “Reallocated” does not mean those employees were laid off.
Everest Transportation Systems was reported as achieving up to a 15% operating-cost reduction and a 30% productivity increase. FreightWaves reported customer claims of 50%–70% reductions in staffing costs and throughput increases of up to tenfold. These figures come from company announcements or customer claims, use different measures and conditions, and are not independently audited in the cited material.
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Public sources do not establish Pallet’s average customer ROI, error rate versus trained staff, percentage of workflows completed without intervention, retention rate, uptime, security certifications or data-residency guarantees. Claims such as “10x faster” or “half the cost” should therefore be treated as workflow-specific marketing claims, not expected results for every buyer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why logistics is a promising AI market
Logistics combines high transaction volume, structured fields, unstructured messages and frequent exceptions. Existing TMS and ERP systems are deeply embedded, so an automation layer that can work through APIs, email and browser portals may be easier to adopt than a full rip-and-replace project. That is a product-design rationale, not proof that Pallet has solved general logistics automation.
Risks and failure modes
- Bad data can move faster: a misread order or outdated instruction can be propagated across systems unless validation and audit controls stop it.
- Browser automation is brittle: portal layouts, authentication and anti-automation controls change. Ask about change detection, API fallbacks and proof that a booking completed.
- Learned rules can become stale: memory needs versioning, approval, expiration, rollback and testing.
- Errors have financial consequences: a wrong carrier, quote, appointment or payment can cause claims, chargebacks, penalties and disputes.
- Integration may dominate implementation: data cleanup, permissions, master-data matching and exception taxonomy often require substantial operational work.
- Contract terms matter: review data processing, subprocessors, retention, cross-border transfers, audit access and responsibility for automated errors.
How Pallet compares with alternatives
| Category | Strength | Typical limitation or fit |
|---|---|---|
| Traditional TMS platforms such as Descartes, MercuryGate and Trimble Transportation | Mature systems of record, structured workflows and established integrations. | May need configuration or separate automation for unstructured email, documents and portals. |
| Visibility platforms such as project44 and FourKites | Shipment tracking, network data and customer-facing status intelligence. | Visibility does not automatically execute order, booking, document or billing work. |
| RPA or general AI-agent tools | Can automate narrow tasks across legacy applications. | Customers or integrators usually design, maintain and govern the workflows. |
| Custom internal automation | Maximum control over proprietary rules and data. | Requires continuing engineering, security and support investment. |
| Tenet | Core operating-system orientation for cartage, courier, expedited and related operations. | More relevant to a TMS replacement than to a lightweight automation layer over an existing stack. |
Buyer’s checklist
- Choose a measurable workflow: quantify order-entry, quote, tracking, appointment, document, tender or billing volume.
- Inventory integrations: list every TMS, WMS, ERP, portal, inbox, API, EDI feed and authentication method involved.
- Define authority: specify which actions are read-only, which require approval and which may be executed automatically.
- Test exceptions: use ambiguous addresses, conflicting instructions, missing documents, portal outages and duplicate shipments.
- Require controls: ask for confidence thresholds, approval queues, audit trails, role permissions, rollback and emergency shutdown.
- Measure economics: compare implementation and review costs with avoided hiring, reassignment, overtime, error exposure and service improvements.
- Plan exit and change management: confirm data export, rule ownership, connector maintenance and what happens if the system is unavailable.
Bottom line
Pallet’s credible opportunity is not a generic chatbot and not physical pallet robotics. It is software that attempts to move information and take bounded actions across the messy systems used by freight and logistics teams. That can be valuable where transaction volume is high, rules are repeatable and staff spend much of the day re-keying data or chasing updates.
Its success depends less on whether an AI model can understand an email than on whether the product can act safely, audibly and reversibly inside real operational environments. Buyers should evaluate a specific workflow, demand customer-specific benchmarks and keep humans in control of high-consequence decisions.
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