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The Platform Solves Four Critical Challenges:

1. Data overload: Reduces telemetry costs by 90-99% through intelligent event capture
2. Reactive operations: Enables predictive maintenance to prevent failures before they occur
3. Slow diagnostics: Automates root cause analysis from hours to seconds
4. Hidden patterns: Reveals cross-fleet insights to improve quality and supplier performance

Deployment Timeline

Pilot deployment (10-100 devices): 2-4 weeks from kickoff to production

Full-scale deployment (1,000+ devices): 6-12 weeks including integration, testing, and rollout

Core Design Principles

• Event-first design: Capture and process only meaningful events, not continuous streams
• Cloud-agnostic: Deploy on any cloud provider or on-premises
• Open contracts: Well-defined JSON schemas for all inputs and outputs
• Modular layers: Swap components at edge, ingestion, storage, processing, and ML layers

Fully Managed SaaS

QS hosts and manages all infrastructure in our cloud. Fastest time to value with zero infrastructure management.

• Quickest deployment (2-3 weeks)
• Automatic updates and security patching
• 24/7 monitoring and support
• Multi-tenant with data isolation

Bring Your Own Cloud (BYOC)

Deploy QS in your AWS/Azure/GCP account. You control data residency and security policies, QS manages the platform.

• Data stays in your VPC/VNET
• Customer-managed encryption keys
• Custom IAM and security policies
• Deployment via Terraform/CloudFormation

Hybrid Cloud

Edge processing on-premises with selective cloud sync for analytics and ML. Ideal for bandwidth-constrained or air-gapped scenarios.

• Edge-first with cloud bursting
• Offline operation capable
• Selective data replication
• Regional cloud endpoints

On-Premises

Complete on-premises deployment for defense, critical infrastructure, or strict data residency requirements. No cloud connectivity required.

• Air-gapped operation
• Full data sovereignty
• ITAR/FedRAMP ready
• Customer-managed infrastructure

Traceability Flow

VIN → Part Number → Batch/Lot → Assembly Line → Supplier

Example Scenario: Battery voltage degradation detected on VIN YV1ABC123DEF45678

Trace: 12V battery → Part #BT-2024-Q2-5847 → Lot LOT-2024-Q2-0847 → Assembly Line A3 (Shift 2) → Supplier-A

Discovery: 23 other vehicles from same lot showing similar patterns. Issue detected 6-8 weeks earlier than traditional warranty claims would reveal.

Core Contracts

• DiagnosticFact: Core telemetry event payload from edge to cloud with signal name, value, timestamp, and context window
• Incident: Fleet incident with context, severity, triage status, and affected vehicles
• PredictionScore: Risk assessment for individual vehicles with failure probability, horizon, and confidence
• RCATrace: Causal graph output with root cause nodes, contributing factors, and counterfactual scenarios
• DeviceHealth: Device health summary with risk score, recent incidents, and maintenance recommendations

Step 1: VIN/Device Entry

Single device (manual entry) or bulk upload (CSV with Device ID, Model, Region)

Step 2: TLS Certificate Verification

Upload and validate device certificates with detailed cert information (issuer, expiry, CN, mTLS validation)

Step 3: Edge Agent Configuration

Select deployment profile per agent (HPC, IVI, TCU):

• Standard Profile: Balanced resource usage, 5-minute rolling buffer, threshold-based triggers
• Performance Profile: Higher resource allocation, 15-minute buffer, advanced anomaly detection
• Diagnostic Profile: Maximum context capture, continuous logging, detailed trace data

Step 4: Data Stream Configuration

CAN-FD Signal Domains (250+ signals available):

• Battery: Pack voltage, cell voltages, pack temperature, SoC, SoH, current, power
• Inverter: DC voltage, AC voltage, current, temperature, frequency, modulation index
• Thermal: Coolant temp, flow rate, pump speed, radiator fan, HVAC compressor
• Powertrain: Motor RPM, torque, power, efficiency, rotor temperature
• Chassis: Wheel speeds, brake pressure, suspension, steering angle
• HVAC: Cabin temperature, AC status, blower speed, defrost mode

Additional Configuration:

• DTC code filtering by system (Battery, Powertrain, Chassis) and severity (Critical, Warning, Info)
• DDS/SOME-IP service discovery selection for middleware-based communication
• Log stream configuration (HPC syslog, IVI logcat, TCU diagnostics)

Step 5: Review & Deploy

Configuration summary with deployment confirmation and real-time monitoring of agent deployment progress

Workflow:

1. Open Fleet Ops Console → observe fleet health at 89.2% (down from 92.4%)
2. KPI shows 28 D-Rate incidents (up 175% from yesterday)
3. Geographic map highlights Texas with red circle (85% incident rate, 92°F)
4. Click Texas → filter incidents to state=TX → 48 incidents, 40 are "Battery Overheat"
5. Click top incident → review 4-signal telemetry: pack_temp spiked to 92°C
6. Click "View RCA" → see causal graph: High Ambient → Pack Temp → Coolant Degradation → Power Derate
7. Review counterfactuals: "Raise fan curve +10%" shows -34% risk reduction
8. Find 37 similar incidents, all in TX/AZ/FL, all 1pm-5pm
9. Insight: Hot ambient temperature overwhelming cooling system
10. Action: Deploy fan curve update to vehicles in hot states

Outcome: 34% reduction in similar failures after firmware deployment

Workflow:

1. Open Prediction page → review risk overview: 8 Critical (>75%), 22 High (50-75%)
2. Focus on urgent: 2 vehicles with <1h horizon (immediate failure risk)
3. Click first vehicle: VIN YV1-93A-087, 91% risk, 45-minute horizon
4. Review risk factors: pack_temp_slope +38% (top contributor)
5. Action recommendation: "Schedule Emergency Mobile Service"
6. Click "Schedule Now" → system assigns nearest mobile tech (18 min ETA)
7. Customer receives SMS: "We've detected a cooling issue. Tech dispatched to your location."
8. Mobile tech performs cooling system service + firmware update
9. Risk score drops from 91% to 34% (post-service validation)

Outcome: 2 strandings prevented, $5K cost avoidance (tow + emergency service + customer churn)

Workflow:

1. Navigate to RCA page for battery incident
2. Enterprise data panel shows: Supplier "Northvolt", Batch "BATCH-2024-0815", Affected VINs: 42
3. Click batch ID → navigate to Enterprise page filtered by batch
4. Batch analysis: 124 total vehicles, 42 incidents (33.9% rate)
5. Comparison: Other Northvolt batches average 11.2% rate → 3.0x higher failure rate
6. Incident timeline: All 42 occurred within 6-8 weeks of delivery
7. Incident types: 38 of 42 are "Battery Overheat" or "Cell Imbalance"
8. Causal heatmap: "Cell Imbalance → Pack Temp" appears in 36 of 42 traces (86%)
9. Hypothesis: BATCH-2024-0815 has manufacturing defect (cell balancing issue)
10. Generate report → contact Northvolt with data
11. Northvolt confirms manufacturing process anomaly → agrees to warranty replacement for all 124 vehicles

Outcome: Issue detected 6 weeks earlier, 82 proactive services scheduled, $1.64M warranty cost recovered

Edge Layer

• Edge Agents: Lightweight software (10-50 MB) running on device CPUs
• Local Processing: Real-time signal monitoring, threshold evaluation, anomaly detection
• Buffering: 5-15 minute rolling window (configurable)
• Event Triggers: Smart upload on rule violations only
• Protocols: TLS 1.3, mTLS certificate authentication, MQTT/WebSocket

Cloud Layer

• Infrastructure: AWS (EKS), Azure (AKS), or GCP (GKE) with multi-cloud support. Deployment models include SaaS, BYOC, Hybrid, and On-Premises
• Data Ingestion: Kafka/AWS Kinesis/Azure Event Hubs for high-throughput streaming (millions of events/sec) with guaranteed delivery
• Storage: VictoriaMetrics (time-series), S3/Azure Blob/GCS (raw telemetry), PostgreSQL (metadata), supporting petabyte-scale data
• Processing: Spark on EKS/AKS for batch analytics, Flink for real-time stream processing, distributed computing across cloud regions
• ML Platform: Azure ML, MLflow for model training, versioning, and deployment. Support for custom model serving
• API Gateway: RESTful APIs with rate limiting, OAuth 2.0/SSO authentication, and comprehensive audit logging
• Query Engine: Trino SQL for interactive analytics across petabyte-scale data with sub-second response times

Analytics Layer

• Predictive Models: LSTM, Isolation Forest, XGBoost for risk scoring
• RCA Engine: Causal inference with Bayesian networks and template matching
• Anomaly Detection: LSTM autoencoders, Z-score, statistical process control
• Query Engine: Trino for SQL analytics across petabyte-scale data

Platform Capabilities

• Device Monitoring: Millions of devices simultaneously with real-time health tracking
• Data Throughput: 10+ PB daily processing capacity with horizontal scaling
• Query Performance: Sub-second response for fleet-wide queries via Trino SQL
• Prediction Refresh: Risk scores updated continuously (minutes) as new telemetry arrives
• RCA Generation: First-pass causal graphs in ~60 seconds with 84% accuracy
• Data Ingestion Latency: <500ms from edge to cloud with streaming pipelines
• Rollback Time: ≤5 minutes for automated platform rollback via blue-green deployment
• Uptime SLA: 99.9% availability (measured monthly) for SaaS deployments

1. Fully Managed SaaS

QS hosts and manages all infrastructure. Fastest time to value with zero infrastructure management.

• Multi-region availability (US, EU, APAC)
• Automatic scaling and load balancing
• Continuous platform updates and security patches
• 24/7 monitoring with QS on-call support

2. Bring Your Own Cloud (BYOC)

Deploy QS in your AWS/Azure/GCP account with full data residency control.

• Data stays in your VPC/VNET with customer-managed encryption keys
• Custom IAM policies and security groups
• Infrastructure-as-Code via Terraform or CloudFormation
• Shared operational responsibility (QS manages platform, you manage infrastructure)

3. Hybrid Cloud

Edge processing on-premises with selective cloud sync for analytics and ML.

• Edge-first architecture with offline operation capability
• Selective data replication to cloud for training and analytics
• Regional cloud endpoints for data residency compliance
• Ideal for bandwidth-constrained or partially connected scenarios

4. On-Premises (Air-Gapped)

Complete on-premises deployment for defense and critical infrastructure.

• No cloud connectivity required, fully self-contained
• Full data sovereignty and control
• ITAR, FedRAMP, and defense-grade compliance ready
• Customer-managed infrastructure with QS platform software

In-Transit Security

• TLS 1.3: with mTLS for vehicle-to-cloud communication
• Certificate Management: Automated rotation and revocation
• Secure Protocols: MQTT over TLS, HTTPS, WebSocket Secure

At-Rest Encryption

• AES-256 encryption: for all stored data (telemetry, incidents, predictions)
• Customer-Managed Keys: Support for AWS KMS, Azure Key Vault, GCP KMS
• Key Rotation: Automated key rotation policies
• Right to Forget: Key revocation for GDPR compliance

Access Control & Authentication

• SSO Integration: OIDC, SAML 2.0 for enterprise identity providers
• RBAC: Role-based access control with least-privilege principle
• MFA: Multi-factor authentication for sensitive operations
• Service Accounts: API keys and service principals for programmatic access
• Audit Logging: Complete audit trail of all actions, data access, and configuration changes

Compliance & Data Residency

• Data Residency: Multi-region deployment (US, EU, APAC) with data residency controls
• GDPR: Right to access, right to forget, data portability, consent management
• CCPA: California Consumer Privacy Act compliance
• SOC 2 Type II: Annual audits for security, availability, confidentiality
• ISO 27001: Information security management system certification
• ITAR Ready: Architecture designed for International Traffic in Arms Regulations
• FedRAMP Ready: Federal Risk and Authorization Management Program compliance path

Cloud Providers

• AWS: EKS (Kubernetes), S3 (storage), Kinesis (streaming), MSK (Kafka), RDS (PostgreSQL), FleetWise integration
• Azure: AKS (Kubernetes), Blob Storage, Event Hubs (streaming), Azure Database for PostgreSQL, Azure ML integration
• GCP: GKE (Kubernetes), Cloud Storage, Pub/Sub (streaming), Cloud SQL, BigQuery export

Data Platforms

• Databricks: Delta Lake export for advanced analytics and ML development
• Snowflake: Incident and prediction data sync for enterprise BI
• Apache Iceberg: Open table format for data lake interoperability
• Trino/Presto: SQL query engine for federated analytics

Enterprise Systems

• ERP Systems: SAP (OData, BAPI), Oracle ERP (REST API) for part numbers and supplier data
• PLM/MES: Manufacturing execution system integration for VIN-to-batch traceability
• Quality Systems: CSV export, REST APIs for defect tracking and quality metrics
• Service Management: ServiceNow, JIRA for incident ticketing and service workflows

Total Annual Value

Platform pays for itself in 2-4 weeks

How long does deployment take?

Pilot deployment (10-100 devices) typically takes 2-4 weeks from kickoff to production. This includes connectivity setup, edge agent deployment, data stream configuration, and initial RCA/prediction model tuning.

Full-scale deployment (1,000+ devices) takes 6-12 weeks including enterprise system integration, extensive testing, and phased rollout.

What deployment models do you support?

We support four deployment models to match your requirements:

• Fully Managed SaaS: QS hosts and manages all infrastructure (fastest time to value)
• Bring Your Own Cloud (BYOC): Deploy in your AWS/Azure/GCP account (full data residency control)
• Hybrid Cloud: Edge processing on-premises with selective cloud sync (bandwidth-constrained scenarios)
• On-Premises (Air-Gapped): Complete on-premises deployment (defense and critical infrastructure)

What hardware requirements are needed for edge agents?

Edge agents are lightweight (10-50 MB) and run on standard automotive compute platforms:

• Recommended: NVIDIA DRIVE Orin, TCU with ARM64/x86_64 CPU, 512MB RAM minimum
• Minimum: Any Linux-based compute with network connectivity and CAN/DDS access
• Storage: 1-5 GB for rolling buffer and local event storage

Can we use our existing cloud infrastructure?

Yes! QuantumStream is cloud-agnostic and designed to integrate with your existing stack. We support:

• AWS: Native integration with EKS, S3, Kinesis, MSK, and FleetWise
• Azure: Full integration with AKS, Blob Storage, Event Hubs, and Azure ML
• GCP: Support for GKE, Cloud Storage, Pub/Sub, and BigQuery
• On-Premises: Deploy on your own Kubernetes cluster or bare metal

Our philosophy: Plug QS into your stack, not the other way around.

What third-party platforms can QS integrate with?

QS integrates with leading enterprise platforms:

• Data Platforms: Databricks (Delta Lake), Snowflake, Apache Iceberg
• ERP/PLM: SAP, Oracle ERP, manufacturing execution systems (MES)
• Incident Management: JIRA, ServiceNow, PagerDuty
• Communication: Slack, Microsoft Teams for real-time alerts
• OTA Platforms: Sibros OTA, custom OTA systems for firmware updates

Do you provide APIs for custom integrations?

Yes. QS provides comprehensive RESTful APIs for all platform features:

• Fleet Health: GET /api/v1/fleet/health for real-time fleet status
• Predictions: GET /api/v1/predictions for at-risk vehicles
• Incidents: GET /api/v1/incidents for incident data
• RCA: POST /api/v1/rca/generate for causal graph generation
• Data Export: S3/Blob export APIs for bulk data access

All APIs support OAuth 2.0/SSO authentication and comprehensive audit logging.

What about data residency and compliance?

We provide multiple options for data residency and compliance:

• Multi-Region Deployment: US, EU, APAC regions available for SaaS deployments
• BYOC Model: Deploy in your own cloud account for complete data residency control
• GDPR Compliance: Right to access, right to forget, data portability, consent management
• CCPA Compliance: California Consumer Privacy Act requirements
• SOC 2 Type II: Annual third-party audits for security controls
• ISO 27001: Information security management certification
• ITAR/FedRAMP Ready: Architecture designed for defense and federal requirements

How is data encrypted?

QS implements defense-in-depth encryption:

• In Transit: TLS 1.3 with mTLS for vehicle-to-cloud communication
• At Rest: AES-256 encryption for all stored data
• Key Management: Customer-managed keys via AWS KMS, Azure Key Vault, or GCP KMS
• Key Rotation: Automated rotation policies and revocation for GDPR compliance

How much does QS reduce telemetry costs?

QS achieves 90-99% cost reduction through edge-first architecture:

• Event-First Design: Only upload data when interesting events occur (threshold violations, anomalies, pattern matches)
• Rolling Buffers: 5-15 minute context windows captured locally at the edge
• Smart Triggers: Machine learning models determine what's worth uploading
• Example: From $2M/year to $20K-100K/year for 1,000-vehicle fleet

What are the platform performance SLAs?

QS provides the following performance guarantees for SaaS deployments:

• Platform Uptime: 99.9% availability (measured monthly)
• RCA Generation: ~60 seconds for first-pass causal graphs
• Prediction Refresh: Risk scores updated continuously (minutes) as data arrives
• Data Ingestion: <500ms latency from edge to cloud
• Query Performance: Sub-second response for fleet-wide queries
• Rollback Time: ≤5 minutes for platform version rollback

Can QS scale to millions of devices?

Yes. QS is designed for massive scale:

• Device Monitoring: Millions of devices simultaneously
• Data Throughput: 10+ PB daily processing capacity
• Horizontal Scaling: Automatic scaling across cloud regions
• Event Processing: Millions of events per second via Kafka/Kinesis

Our architecture is proven at scale with automotive OEMs managing global fleets.

What ROI can we expect from deploying QS?

For a 1,000-device fleet, typical annual value includes:

• Telemetry Cost Reduction: $1,200/device/year (90-99% data reduction)
• Failure Prevention: $2,400/device/year (80% of failures predicted)
• Warranty Reduction: $550/device/year (33% reduction through early detection)
• MTTR Improvement: 1.4 hours saved per incident (44% faster resolution)
• Downtime Prevention: $800/device/year (proactive maintenance)
• Service Efficiency: $540/device/year (optimized scheduling)

Total: $7,140/device/year with 13-35x ROI. Platform pays for itself in 2-4 weeks.

Contact Us

Have questions about implementation, pricing, or custom deployments?