Trip intake
Structure origin, destination, cargo, windows, vehicle requirements, contacts, and commercial conditions.
AI Logistics Copilot reduces dispatcher paperwork and repetitive communication while routing, safety, pricing, and carrier commitments remain under human control.
Structure origin, destination, cargo, windows, vehicle requirements, contacts, and commercial conditions.
Extract and compare waybills, delivery evidence, invoices, and missing documents against the trip record.
Prepare approved updates, flag delays or mismatches, and collect the evidence needed for a claim.
A focused review of the current process, data, integrations, risks, and measurable automation opportunity.
One live workflow is connected, tested with the team, and placed behind clear human approval controls.
Ongoing monitoring, prompt and workflow improvement, support, and expansion into the next process.
We map the real workflow, exceptions, data sources, decision rights, and current performance baseline.
We define the integrations, approval gates, security boundaries, and the first measurable use case.
The workflow runs with a limited team, traceable outputs, and human review before critical actions.
After the KPI review, we stabilize the system and expand only where the evidence supports it.
For owner-led transport companies with dispatchers coordinating trips across calls, chats, and spreadsheets.
For teams that need cleaner document handoff, customer updates, and exception management.
Not autonomously. It can prepare options and surface exceptions; authorized dispatchers retain operational and safety decisions.
Yes, subject to image quality and document type. Important fields can require validation before they update a trip record.

Map the current workflow, data, risks, approval gates, and the first measurable pilot.

Use structured research and operational baselines before deciding what should be automated.

Follow the systems, governance patterns, and market signals shaping applied AI.