Connect freight, carrier, lane, cost and delivery data. Mimasa AI helps logistics teams investigate performance, find operational patterns and turn freight analytics into governed action.
Ask questions in natural language. Compare routes, carriers and service outcomes. Generate dashboards, reports and presentations without replacing the TMS, ERP or logistics systems your teams already use.
Governed data | Natural-language analysis | Human approvals | Auditable workflows | Cloud, private-cloud/VPC and on-premise deployment
Transportation performance (illustrative)
Last 90 daysTransportation teams generate data through bookings, shipments, carrier events, warehouse movements, invoices, service commitments and delivery records.
The problem is rarely a complete absence of data. The problem is that operational and financial signals live in different systems, use inconsistent identifiers and reach decision-makers at different times.
Mimasa connects the required data, applies governed definitions and makes the analysis available to authorized teams.
Freight analytics is the process of analysing transportation data to understand cost, service, carrier, lane, transit and delivery performance.
Useful analysis connects operational events with commercial and business context. A late shipment, for example, may need to be understood by carrier, route, customer commitment, facility, cost and exception cause.
Mimasa combines freight data analytics with AI agents for logistics analytics and workflows. Teams can investigate results, generate reports and coordinate approved follow-up without moving every decision into another disconnected tool.
For teams evaluating a transportation analytics platform, Mimasa provides a connected analysis and action layer around their existing operational systems. Its freight data analytics software capabilities remain grounded in the sources, definitions and permissions approved by the organization.
Connect data, prepare trusted measures, ask and analyse, compare and explain, predict and prioritize, approve and act, then report and monitor.
Bring together authorized data from TMS, WMS, ERP, carrier, telematics, finance, database, spreadsheet and document sources.
Input: authorized logistics sources
Match shipment, carrier, lane, customer, facility and cost identifiers. Define approved KPI logic and surface missing or inconsistent records.
Output: governed measures
Use natural-language questions or reusable analyses to explore cost, service, route, carrier and delivery performance.
Input: a business question
Compare periods, regions, modes, lanes and service providers. Identify the records and factors behind each result.
Output: evidence-linked findings
Apply predictive analytics in logistics when sufficient data exists. Prioritize material risks, anomalies and opportunities for review.
Decision: what deserves attention
Create tasks, request investigation, prepare an alert or update a connected workflow within configured permissions.
Control: human approval
Publish governed dashboards, scheduled reports, Excel outputs and presentation-ready reviews.
Output: recurring reporting
Logistics analytics becomes useful when one question can move from a number to the shipments behind it.
Compare spend by carrier, lane, route, mode, customer, facility or business unit. Break down base freight, fuel, accessorial and other available charge categories.
Use freight cost analysis to understand what changed and identify the underlying shipments or records.
Combine approved freight and finance data to examine spend patterns over time. Compare contracted, expected, invoiced or allocated cost where the required records exist.
Use transportation cost analysis to identify changes by carrier, lane, mode or business unit.
Evaluate carrier performance metrics such as on-time pickup, on-time delivery, transit variance, dwell, exception frequency and claims where source data is available.
Mimasa does not provide universal carrier benchmarks. Comparisons use your authorized data and defined measures.
Compare volume, cost, transit time, variance and service outcomes by origin-destination pair. Identify lanes with repeated delays, cost changes or inconsistent carrier performance.
Connect dispatch, milestone, delivery and service data. Use a delivery KPI dashboard to compare delivery outcomes across routes, customers, facilities and time periods.
Measure how long shipments remain at facilities, hubs, ports or handoffs when event data supports it. Use dwell time analytics to identify recurring congestion or process gaps.
Keep invoice-level audit decisions on the dedicated freight audit and invoice automation page.
A logistics dashboard should help a user move from a metric to the records behind it.
Build a logistics KPI dashboard, transportation KPI dashboard or supply chain analytics dashboard from governed definitions. Users can drill into supporting data based on their access permissions. See visualization and dashboards.
Authorized users can ask questions such as:
Natural-language analysis reduces dependence on one fixed dashboard. It does not bypass governed data models, definitions or access controls.
Machine learning in logistics can help detect patterns that static reports may miss.
Every predictive result must be validated for your data and operating context. Mimasa does not publish a universal accuracy claim.
Shipment status, milestones and live ETA risk are covered by shipment visibility and exception management, including shipment tracking analytics.
Identifies missing, inconsistent or unmatched transportation records before analysis.
Explores freight and logistics cost changes and links findings to supporting data.
Compares approved carrier KPIs across lanes, modes and periods.
Collects relevant shipment, route and facility evidence for a selected performance issue.
Prepares recurring dashboards, management summaries and presentation-ready reviews.
Creates an investigation task, prepares an alert or invokes a permitted workflow after required approval.
AI agents operate within configured tools, data access, confidence thresholds and approval policies.
Freight analysis becomes valuable when the result enters an operating workflow.
This connects transportation analytics software with day-to-day execution while keeping people in control. See governed workflow automation.
Compare carrier, lane, route and delivery performance without waiting for another manual report.
Connect transportation outcomes with inventory, supplier and customer commitments.
Understand cost composition and transportation spend using approved financial data.
Review carrier service and cost patterns when preparing sourcing or performance discussions.
Access approved performance context for recurring service issues.
Review network cost, service and operational trends in a consistent view.
Supply chain and inventory automation remains the primary business-function owner of this use case. Data and decision intelligence is the supporting cross-functional solution.
Depending on the approved implementation, Mimasa may connect with these sources.
Mimasa is not a TMS, freight marketplace or proprietary data provider. Integration availability depends on source access, permissions, data quality and implementation scope. Review logistics data extraction and transportation data preparation.
For a Dubai-based freight organization, Mimasa AI has worked on a scope covering ETA prediction, logistics optimization, exception alerts, SLA dashboards and natural-language operational questions.
This illustrates how connected transportation data can support investigation, performance monitoring and faster operational response.
These are measurement areas, not guaranteed outcomes. Establish an approved baseline and target for each implementation.
Explore insights and reporting for recurring transportation reviews.
Transportation analysis may involve customer data, commercial rates, shipment locations and operational decisions.
Organizations decide which users and agents may access data, prepare recommendations or execute actions. Explore governed logistics data.
Common questions about freight analytics, transportation analytics software and governed logistics reporting.
Agents that check data quality, investigate cost and prepare reviews.
Approvals, permissions and connected-system actions after analysis.
Read shipment, carrier and freight documents into structured records.
Match identifiers and shape freight data into trusted measures.
Access control, definitions and lineage behind every metric.
Role-based logistics KPI views with drill-through to records.
Scheduled transportation reviews and presentation-ready outputs.
Detailed pages for the remaining solutions are in preparation.
Milestone risk detection and coordinated logistics response.
Invoice verification, discrepancy review and approved finance handoff.
Logistics Control Tower
A network view across shipments, inventory and carriers.
Vehicle, trip, fuel and maintenance analysis from connected data.
Start with one carrier, lane, cost or service question. Connect the required sources, define the trusted measures and show how Mimasa AI can improve analysis and follow-through.
