Vahana
vahana - vehicle, that which carries
Connected-vehicle and telematics data, made actionable.
Vahana ingests connected-vehicle and telematics data faster than any team could by hand and turns it into reliability, safety, and operational signal. High-throughput pipelines feed models that derive vehicle health and usage, surfaced through fleet-level dashboards that make the whole estate legible.
Workflow
Problem
Fleet and vehicle telemetry arrives faster than any team can model or act on it.
Outcome
A platform that turns raw telematics into reliability, safety, and operational signal at scale.
01
Ingest at scale
High-throughput pipelines absorb telemetry from connected fleets.
02
Model signals
Raw streams become health, safety, and usage signals.
03
Surface operations
Fleet dashboards give visibility across the vehicle estate.
04
Act
Signals route to the teams and systems that own the response.
Modules
Ingestion at Scale
High-throughput pipelines for connected fleets.
Signal Modeling
Derive health, safety, and usage signals.
Fleet Dashboards
Operational visibility across the vehicle estate.
Representative use cases
Fleet-Wide Fault Diagnostics from Live Vehicle Telemetry
A national freight logistics provider operating 3,500 heavy-duty commercial tractors and trailers across long-haul highway routes.
EV Battery Degradation & Thermal Runaway Monitoring for Transit Fleets
A metropolitan transit authority operating 600 electric buses under demanding urban stop-and-go driving conditions and extreme summer temperatures.
Real-Time Cargo Cold-Chain Temperature Compliance in Pharmaceuticals
A specialized healthcare logistics provider transporting high-value vaccines and biologics in refrigerated trailer units across national distribution networks.
Automated Remote ECU Diagnostics & Over-The-Air (OTA) Campaign Validation
An automotive OEM deploying Over-The-Air (OTA) software updates to 250,000 connected consumer vehicles across multiple continent regions.
Intermodal Chassis & Container Asset Tracking Across Global Corridors
A global maritime shipping line managing 45,000 intermodal chassis and shipping containers moving between ports, rail yards, and customer warehouses.
Evidence
What Wayam can show for this entry
Curated solution design with an owner and a review date. No performance or production claim.
Allowed claims at this tier: Possible workflow, typical stack, prerequisites.
- Evidence tier
- T4 · Reference pattern
- Integration status
- Typical enterprise system
- Metric status
- Scenario only
- Evidence owner
- Not yet attached
- Validated
- Not yet attached
- Valid until
- —
- Content version
- 2026.09
Pending evidence attachment
This entry represents an enterprise architectural reference pattern. Customer benchmarks, run replays, and metric verifications are established during technical discovery.
Limitations
- Headline metrics are representative until a customer result is attached.
Representative metrics · Reference · scenario only
Fleet-scale
telemetry throughput
These describe the intended outcome of the design. They are not measured customer results until a validated case is attached above.
Architecture & controls
Composed across the operating loop
Ingest
Connect the systems of record and read the signals the work already produces.
Covered by the platform
Reason
Ground context, score options against policy and the stated goal, draft the next step.
3 agent roles
Act
Execute an approved step in the system of record and keep the evidence.
6 agent roles
Govern
Set policy, gate consequential actions on a named approver, and audit what ran.
1 agent role
Pack Architecture & Agent Composition Graph
10 Composed Agents- AGT-0746
Fleet Vehicle Management Agent
Automotive
- AGT-0142
Predictive Maintenance Agent
Asset Management
- AGT-0351
Process Anomaly Detection Agent
Manufacturing Operations
- AGT-0284
Anomaly Explanation Agent
Data and Analytics
- AGT-0345
Quality Inspection Reporting Agent
Quality
- AGT-0348
Scrap and Rework Analysis Agent
Quality
- AGT-0349
First-Pass Yield Improvement Agent
Quality
- AGT-0394
Field Service Diagnostic Copilot
Field Operations
Control gates
- A named approver on every consequential write-back
- Evidence and audit trail kept with every run
- In-boundary deployment available
Capability atoms
- signal-ingestion
- system-connectors
- forecasting
- anomaly-detection
- in-boundary-deployment
Typical enterprise systems
Typical stack for solution design; compatibility is validated during discovery.
- Siemens MES MCP
- SAP S/4HANA
- Snowflake
- AWS IoT SiteWise
- PI System
- SAP PM MCP
- Maximo
- Cognex VisionPro
Designed for
automotive, logistics, energy-utilities
Business case
Model a Vahana scenario with your own baseline
Three scenarios from the numbers you enter. Capacity released is time; it becomes a saving only when roles or costs are actually removed or avoided.
| Scenario | Improvement | Capacity released | Cashable savings | Net annual | Payback |
|---|---|---|---|---|---|
| Conservative | 9% | — | — | — | — |
| Expected | 18% | — | — | — | — |
| Upside | 24% | — | — | — | — |
Enter an annual volume and a baseline cost to see figures.
Illustrative planning scenario, not a guarantee. Results depend on process baseline, adoption, data quality, integration scope, controls, and deployment costs.
Pilot this
From solution design to a bounded pilot
Step 1 · 3–4 weeks
Discovery Sprint
Validate the workflow, data, controls, baseline and business case before anything is built.
Gate: Blueprint and pilot plan signed by the business owner, technical owner and Wayam.
Step 2 · 6–10 weeks
Bounded Pilot
Prove quality and value on agreed data against agreed acceptance tests.
Gate: Acceptance thresholds met on the evaluation set; go/no-go decision recorded.
Agrani · evidence pending3 Agrani candidates in this solution: Process Anomaly Detection Agent, Change Risk Assessment Agent, Logistics Tracking and Notification AgentPairs well with