Investor Deck

Enter password to access

Incorrect password

Contact us for access credentials

  1. 1

    QuantumStream

    Next-Gen AI Fleet Intelligence

    Predictive Analytics · Instant Diagnostics · Autonomous Operations

    quantumstream.io | sam@quantumstream.io
  2. 2

    What is Fleet Intelligence?

    Connected devices generate thousands of signals for internal operations — Fleet Intelligence transforms this untapped data into business value

    Transmission
    ~100 signals
    • GPS & location
    • Crash signals
    • Compliance data
    • Alerts
    10,000+
    signals
    Operational Data
    • Component diagnostics
    • Sensor calibrations
    • Performance metrics
    • Internal behaviors
    Fleet Intelligence
    Edge AI Processing
    Predictive Maintenance
    Real-time Monitoring
    Root Cause Analysis
    Pattern Recognition
    Anomaly Detection
    Transforms Into Business Value
    Cost Savings
    25-40% reduction in maintenance & support costs
    Faster R&D
    6-8 weeks earlier issue detection from field data
    Fleet Uptime
    Predictive maintenance prevents failures
    Improvements
    Data-driven product & firmware enhancements
    If this data is so valuable, why isn't everyone using it?
    The answer reveals a massive market opportunity →
  3. 3

    The Problem

    Modern connected devices produce billions of datapoints per day — but companies can't afford to collect or analyze it

    Data Volume
    5K-10K
    Signals per vehicle
    Many at 1-10ms intervals
    →
    2-3B
    Datapoints per day
    Per vehicle
    →
    $500K-$2M
    Annual cloud costs
    For 1,000 vehicle fleet
    Financially
    Impossible
    Streaming all signals to the cloud bankrupts the business model
    • Ingestion costs explode
    • Storage grows infinitely
    • Processing bills compound
    Technically
    Impossible
    Most teams lack telemetry + timeseries expertise
    • Complex signal processing
    • Stateful anomaly detection
    • Event correlation across time
    Requires Army
    of Experts
    Every incident needs manual human analysis
    • 2-4 hours per incident
    • Hunt through raw logs
    • Build charts manually
    The Result: 99% of Operational Data is Lost
    Most OEMs collect
    ~80 signals
    At extremely low frequency
    When failures happen
    No context
    Critical event windows lost
    Teams operate
    Blind & Reactive
    Hours to answer simple questions
  4. 4

    Our Solution

    An intelligent platform that captures only the right data, processes it at the edge, and uses AI to turn it into action

    Don't Collect All Data — Capture Only What Matters, When It Matters
    Event-first telemetry + edge processing + AI-powered analysis = massive cost reduction with full intelligence
    Smart Capture
    Edge processing filters 90-99% of data at the source
    Only event windows (t₀ ± context) reach the cloud
    Unified Platform
    ✓ Real-time fleet monitoring
    ✓ Smart diagnostics
    ✓ Predictive failure models
    ✓ Root cause analysis
    Instant Action
    AI-generated insights in under 60 seconds
    Powered by QuantumStream IQ
    Telemetry Reduction
    90-99%
    Via edge processing
    Annual Savings Per Fleet
    $0.65–2.2M
    ≈$650–2,200/vehicle/year
    Faster Resolution
    44%
    MTTR improvement
  5. 5

    How It Works

    Continuous intelligence pipeline from edge to action

    Fleet Devices (Edge)
    Real-time telemetry from connected devices
    Smart Detection: ~90%+ reduction via event-first edge processing
    Cloud Pipeline
    Stream & store data at scale
    Real-time ingestion • Time-series DB • Data lake
    ML & Analytics Engines
    Process with intelligence
    ML prediction • Rule-based engines • Anomaly detection
    AI Intelligence Layer
    Make sense of everything
    Root cause analysis • Failure prediction • Cost optimization
    Automated Actions & Value Delivery
    Cost Savings
    Optimize spend
    RCA Reports
    60s diagnosis
    Predictions
    Prevent failures
    OTA Updates
    Remote fixes
  6. 6

    Why Now

    Four independent developments converging in 2024-2025

    2020-2022
    Edge Compute Becomes Standard in Modern Fleets
    New vehicles ship with sufficient edge compute for intelligent selection agents. Smart filtering at source enables 99% cost reduction vs. cloud-everything while capturing complete context when needed.
    2023
    Fleet Data Hits Economic Breaking Point
    Software-defined vehicles generate TB/day per asset. Cloud-first telemetry costs $500K-$2M/year for 1,000 vehicles—economically unsustainable at fleet scale. Traditional approach no longer viable.
    2024
    Regulatory Mandates Require Explainability
    OEMs must explain every failure with evidence, trace causality to root cause, and demonstrate proactive monitoring. "We don't have the data" is no longer an acceptable response to regulators or customers.
    2025
    Agentic AI Crosses Production Threshold
    AI agents can autonomously detect anomalies, trigger targeted data capture, and explain decisions with ≈85–90% first-pass accuracy (88% precision, 82% recall). Production-grade reliability for mission-critical fleet operations.

    Agentic AI reached production reliability. Fleet data volumes made cloud-first economics unsustainable. Edge compute became standard. Regulatory requirements mandated explainability. These four conditions are now simultaneously true for the first time.

  7. 7

    Market Opportunity

    Fleet intelligence + predictive maintenance + AI analytics: fastest growing enterprise software segments

    TAM (2030)
    $67B
    Fleet + Predictive Maint. Software
    15-20% CAGR
    SAM
    $12B
    US/EU Enterprise Fleets
    Auto, DC, Defense verticals
    SOM (5-Year)
    $150M
    75-150 Customers
    $1-2M avg ACV
    Automotive OEMs
    ~30
    customers
    $1-5M ACV
    Tier 1 + Fleets
    500+
    customers
    $200K-2M ACV
    Data Centers
    100+
    customers
    $500K-5M ACV
    Defense
    20+
    customers
    $1-10M ACV
    Beachhead
    Autonomy Startups
    AV, drones, defense robotics
    Year 1
    5-10 Startups
    $1-3M ARR
    Year 3
    30+ Mid-Market
    $15-25M ARR
    Year 5
    Enterprise OEMs
    $100-150M ARR
  8. 8

    Business Model

    Simple QCU pricing, massive customer value

    Three Ways QuantumStream Delivers Value

    90-99%
    Telemetry Reduction
    Edge processing filters data at source, cutting cloud compute costs by ~60-70%
    AI First
    No Analyst Army Needed
    AI native platform automates analysis, diagnostics, and insights with human in the loop approval
    All In One
    Complete Platform
    Automated monitoring, diagnostics, RCA, and prediction in a single unified platform

    💰 Revenue Model: QCU (QuantumStream Compute Units)

    Pricing Model
    12%
    QCU on Compute
    (Storage is pass through)
    Free Tier
    15
    Active Assets
    Perfect for pilots & POCs
    Example: 1K Fleet
    $23K
    Annual QCU Revenue
    12% of $190K compute
    Customer Total Cost for 1K Fleet
    Traditional
    $500K to $2M
    →
    With QuantumStream
    $213K to $854K
    Customer saves 57% while getting complete AI platform
  9. 9

    Proven at Scale: Premium EV OEM

    6 years building and deploying this architecture in production for a major automotive manufacturer

    Infrastructure Cost per Connected Vehicle per Hour
    Year 1
    $11.30
    →
    Year 6
    $0.24
    98% reduction while adding RCA, advanced events, analysis & visualizations

    Architecture

    • Event-first telemetry
    • Edge rules engine
    • Targeted capture

    ML & Prediction

    • Survival models
    • 50-100 features/system
    • Calibrated horizons

    Production Scale

    • Trillions of data points
    • Hundreds of failures prevented
    • 44% MTTR improvement

    Technical Proof

    • Global fleet scale
    • 88% precision, 82% recall
    • RCA in minutes vs days

    Business Impact

    • Millions in prevented claims
    • Proactive service scheduling
    • Faster issue identification

    QuantumStream: Making this proven playbook accessible to any OEM or fleet operator without requiring a team of Principal Engineers.

  10. 10

    Competition & Differentiation

    Only platform unifying edge intelligence, causal RCA, and enterprise context

    Key Capability Comparison

    Capability
    QuantumStream
    AWS FleetWise
    Sibros
    Samsara
    Palantir
    Edge Rules & Compute
    ✅
    ✅
    ✅
    ◑
    ◑
    Predictive Maintenance
    ✅
    ◑
    ◑
    ◑
    ✅
    Causal RCA (EventTrace)
    ✅
    ✖
    ✖
    ✖
    ◑
    Enterprise Blend (VIN→ERP)
    ✅
    ◑
    ◑
    ◑
    ✅
    90-Day Pilot Ready
    ✅
    ◑
    ✅
    ✅
    ◑

    Unique combination: Edge-first collection + explainable causal RCA + supplier/ERP traceability. Competitors offer 1-2 of these, not all three in a single platform.

  11. 11

    Defensibility: Hard to Replicate

    Four pillars built over 6+ years of production experience

    Connected Device Expertise
    • Telemetry & log processing at scale
    • Time-series data handling
    • Edge-native algorithms
    AI Agent Orchestration
    • Multi-agent coordination system
    • Prediction & RCA automation
    • Self-improving pipelines
    Petabyte-Scale Infrastructure
    • Real-time stream processing
    • Distributed query optimization
    • High-throughput pipelines
    Edge-Cloud Architecture
    • Intelligent data selection
    • Seamless edge-cloud handoffs
    • Unified device-to-dashboard
    The Moat: Years to Replicate

    Not just software—deep expertise + production-hardened systems refined over years.

  12. 12

    Team

    6 years building this in production + deep AI/ML research expertise

    Sam Jafari

    Sam Jafari

    Co-Founder & CEO
    Former Director at Lucid Motors | AWS | Twitter
    16+ years in Big Data, ML & AI
    • Built Data, AI & IoT orgs at Lucid Motors
    • Led edge systems processing trillions of daily records
    • Developed fleet intelligence & RCA at scale
    • Expert: Cloud-edge, distributed systems, ML
    Dr. Mohsen Babaeian

    Dr. Mohsen Babaeian

    Co-Founder & CMO
    Ph.D. Engineering & AI/ML
    Researcher & Lecturer at CSU
    • PhD research: ML algorithms for real-time signals
    • Founder & CEO, Intelligent eHome (AI IoT)
    • Expert: Real-time ML, IoT monitoring
    • Specializes in hardware-software integration
    Hiring founding engineers (Edge/Backend/ML) • Leveraging investor network
  13. 13

    Roadmap & Ask

    M0
    Launch
    M6
    MVP
    M12
    Scale
    M18
    Series A
    2-3
    Design Partners
    $100K
    ARR Target
    10+
    Customers

    Product

    • Core MVP → Intelligence
    • Prediction + RCA
    • Multi-vertical platform

    Go-to-Market

    • Design partners → Sales
    • Defense, EV, Data centers
    • Case studies + channels

    Team

    • 2 → 5 → 12 people
    • Eng (edge/ML) + GTM
    • Series A ready

    Partnership Value

    Talent Network
    Industry Connections
    Strategic Guidance
    Market Credibility
    Seed Round
    Investment Opportunity
    ✓ 12-18mo runway ✓ 6yr proven tech
    ✓ Partners ready ✓ Series A path
  14. 14

    Platform Demo

    Experience QuantumStream in action

    Launch Platform Demo

    Click anywhere to open in new tab

  15. 15

    System Architecture

    Data flow from edge to cloud, through intelligence layers, to user interfaces and automated actions

    Data Sources
    Processing
    Intelligence
    AI Agents
    Interface
    Actions
    💡 Hover/Click for details
  16. 16

    Appendix: Platform Features

    Real-time fleet monitoring and AI-powered intelligence

    FleetOps Console

    FleetOps Console

    Zero-click fleet health understanding in 3 seconds

    • Real-time monitoring dashboard
    • Fleet-wide health scoring
    • Anomaly detection & alerts
    • Customizable KPI tracking

    QuantumStream IQ

    QuantumStream IQ

    Agentic AI analyzer and system

    • Makes sense of complex data patterns
    • Interacts with users in natural language
    • Answers business & technical questions
    • Provides data-driven insights & recommendations
  17. 17

    Appendix: Platform Features

    Intelligent prediction and automated fleet operations

    Predictive Maintenance & RCA

    Predictive Maintenance

    Predict failures 30min-48hr before they happen

    • ML-powered failure prediction
    • 60-second root cause reports
    • Supplier part traceability
    • Automated remediation recommendations

    Action Hub

    Action Hub

    Centralized command center for fleet operations

    • Automated ticketing & scheduling
    • OTA updates & config changes
    • Remote diagnostics & data capture
    • Intelligent alert management
  18. 18

    Appendix: Platform Features

    AI-powered data exploration and sensor configuration

    Data & ML Studio

    Data & ML Studio

    96% time savings from 50 min to 2 min per analysis

    • Natural language data queries
    • Automatic visualizations
    • Collaborative analysis canvas
    • Custom ML model training

    Sensor Studio

    Sensor Studio

    Visual sensor configuration without code

    • Drag-and-drop sensor mapping
    • Real-time signal validation
    • Custom aggregation rules
    • Edge processing configuration
  19. 19

    Appendix: Platform Features

    Device intelligence and enterprise ecosystem integration

    Device360

    Device360 View

    Complete device intelligence in one unified view

    • 360° device health visualization
    • Component-level diagnostics
    • Historical trend analysis
    • Predictive failure warnings

    Enterprise Integration

    Enterprise Integration

    Seamless ecosystem connectivity

    • VIN to ERP traceability
    • Supplier part tracking
    • JIRA, ServiceNow integration
    • Real-time data synchronization