Computer Vision That Works in the Real World.
Deployed at the Edge. Production-Ready in 8 Weeks.
Your cameras become intelligent observers. Your production line gets automated inspection. Your warehouse tracks every movement. And it all runs locally—fast, private, and reliable—without depending on cloud connectivity.
Computer Vision at the Edge: See Everything, Process Instantly
Computer vision gives machines the ability to see and understand visual information—detecting objects, reading text, identifying defects, tracking movement, and recognizing patterns that humans might miss or can't process fast enough.
Edge computer vision takes this further by running the AI directly on local hardware—cameras, embedded devices, or on-site servers—rather than sending video to the cloud.
Speed
Decisions in milliseconds, not seconds. A robotic arm needs to know where to pick. A safety system needs to detect hazards instantly. Cloud round-trips are too slow.
Reliability
Production can't stop because your internet flickered. Edge deployment keeps vision running even when connectivity fails.
Privacy
Your factory floor, your customer behavior, your operational data—processed locally, never leaving your premises.
Bandwidth
Streaming 4K video from dozens of cameras to the cloud is expensive and impractical. Process locally, send only insights.
The result: vision systems that operate in the real world with the speed, reliability, and privacy that production environments demand.
Computer Vision in Action
Object Detection
Real-time object detection identifying parts on production line with confidence scoring
Defect Detection
Surface defect detection catching issues human inspectors miss
Safety Monitoring
Automated PPE compliance monitoring for hard hats, vests, and safety equipment
Inventory Counting
Accurate inventory counting without manual scanning
Analytics Dashboard
Real-time performance monitoring and drift detection
These examples represent the types of vision systems we deploy. Your implementation will be trained on your specific environment, products, and use case requirements.
Why Computer Vision Projects Fail to Reach Production
The industry has a dirty secret: most computer vision projects never make it out of the lab.
The Demo That Impressed Everyone
Your team trained a model that works beautifully on test images. Leadership gets excited. Then reality hits: the model falls apart on real factory lighting, unexpected angles, and edge cases.
The Cloud Dependency Trap
You built a solution that works—but only when connected to cloud infrastructure. Then your network has a hiccup, and production stops. Or you calculate streaming costs, and the business case evaporates.
The Latency Problem
Your quality inspection model is accurate—but takes 800ms per frame. At line speed, defective parts are downstream before results return. The vision system becomes reporting, not control.
The Hardware Mismatch
You trained on powerful cloud GPUs, but your edge device has a fraction of that compute. The model that worked in development doesn't fit on production hardware.
The "It Stopped Working" Mystery
Six months after deployment, accuracy has quietly degraded. Lighting changed. Products changed. Nothing tracks performance, so problems only surface when customers complain.
The Computer Vision FastTrack exists because we've seen these patterns destroy promising projects—and we've built a methodology to prevent them.
From Cameras to Intelligence in 8 Weeks
The Computer Vision FastTrack isn't a research project. It's a structured program that delivers production-ready vision systems—optimized for your hardware, integrated with your operations, and built with the monitoring to keep them working.
What You Get
Production Vision System
Models trained on your data, optimized for your hardware, deployed in your environment. Not a demo—a working system handling real visual data at production speeds.
Edge-Optimized Models
Neural networks tuned for target latency and frame rate on your specific hardware—whether Jetson, x86 with accelerators, or custom platforms.
MLOps Infrastructure
Automated pipelines for model versioning, A/B testing, and retraining. When performance drifts, you know—and you have tools to fix it.
Monitoring & Observability
Dashboards tracking inference latency, throughput, accuracy metrics, and drift indicators. Know HOW your system performs, not just that it's running.
Integration Layer
APIs and connectors linking vision outputs to your existing systems—PLCs, MES, WMS, ERP, or custom applications.
Operational Runbook
Documentation for monitoring, troubleshooting, scaling, and retraining. Your team can operate and evolve the system independently.
How We Deploy Production Computer Vision in 8 Weeks
Our accelerated timeline comes from parallel workstreams, production-proven components, and relentless focus on deployment—not research. Here's how the eight weeks break down:
Discovery & Data Foundation
We audit your visual environment—cameras, lighting, products, processes. In parallel, we establish data collection and begin building your training dataset.
Deliverables: Environment audit report, hardware recommendations, annotated training dataset
Model Development & Optimization
We train custom models on your data, then optimize aggressively for your target hardware. This isn't just accuracy—it's accuracy at target latency and frame rate.
Deliverables: Trained model, edge-optimized variants, performance benchmark report
Integration & Infrastructure
We deploy the optimized model to your edge hardware, build the inference pipeline, and integrate with your existing systems. MLOps infrastructure goes live.
Deliverables: Deployed inference system, MLOps pipelines operational, monitoring live
Validation & Production Launch
Production validation with real data, user training, and formal handoff. We ensure the system performs in actual operating conditions.
Deliverables: Production system live, trained operators, complete documentation, 30-day support begins
What Can You Build in 8 Weeks?
Computer vision transforms any process that currently relies on human eyes—or should have eyes but doesn't. Here's what organizations deploy with FastTrack:
Automated Visual Inspection
Detect defects that human inspectors miss or can't keep pace with. Surface scratches, assembly errors, missing components, dimensional variations—caught in real-time, every unit.
Industries: Manufacturing, Electronics, Pharma, Food Processing
Impact: Reduced escapes, consistent quality, inspection that scales
Object Detection & Counting
Know exactly what's present, where, and how many. Products on shelves, packages on conveyors, vehicles in lots, people in spaces—accurate counts without manual tallying.
Industries: Inventory, Warehouse, Retail, Traffic
Impact: Accurate inventory, automated counting, real-time visibility
Safety & Compliance Monitoring
Ensure PPE is worn, people stay out of hazard zones, safety protocols are followed—automatically, continuously, without dedicated observers.
Industries: Manufacturing, Construction, Warehouse, Labs
Impact: Reduced incidents, compliance documentation, proactive intervention
Robotic Guidance & Pick
Give robots the eyes they need. Locate parts for picking, guide assembly operations, enable navigation in dynamic environments.
Industries: Warehouse Automation, Assembly, Autonomous Vehicles
Impact: Reliable automation, handling variation, faster cycle times
Document & Text Recognition
Extract text from images, documents, labels, and signs automatically. Read shipping labels, capture form data, process invoices.
Industries: Logistics, Document Processing, License Plates
Impact: Eliminated data entry, faster processing, reduced errors
Behavior & Activity Analysis
Understand what's happening, not just what's present. Customer journeys, traffic flow, process adherence, equipment operation.
Industries: Retail Analytics, Security, Process Compliance
Impact: Operational insights, process optimization
Computer Vision for Every Industry and Scale
Manufacturing Operations
The Challenge
You need inspection that keeps pace with production, catches defects humans miss, and runs without network dependencies.
Our Approach
We deploy vision systems that integrate with your PLCs, MES, and existing automation—providing real-time quality data.
What You Get
Inspection at line speed, reduced escapes, quality data integrated with production systems.
The Right Hardware for Your Use Case
There's no one-size-fits-all for edge computer vision. The right hardware depends on your performance requirements, environment, power constraints, and budget. We help you navigate:
NVIDIA Jetson Family
The go-to platform for demanding vision workloads. From Jetson Nano (entry-level) to Orin (server-class performance in embedded form factor).
x86 + Accelerators
Standard servers or industrial PCs paired with inference accelerators (Intel OpenVINO, NVIDIA GPUs, specialized ASICs). Familiar infrastructure.
Specialized Edge Devices
Purpose-built inference hardware from vendors like Hailo, Coral, Intel Movidius. Optimized for specific performance/power/cost trade-offs.
Camera-Level Intelligence
Smart cameras with built-in inference capabilities. Processing happens in the camera itself—no separate compute needed.
Our Guidance
We help you select based on your actual requirements—not just what's newest or most powerful. Sometimes a $150 device is the right choice; sometimes you need $2,000 in compute.
Built for Production, Not Just Demos
Deploying a model is the beginning, not the end. Production computer vision requires operational infrastructure that most POCs completely ignore.
Model Versioning & Rollback
Track which model version is deployed where. When you need to update—or roll back—you can do it confidently across your fleet.
Performance Monitoring
Track inference latency, throughput, and resource utilization in real-time. Know when performance degrades before it affects operations.
Accuracy Drift Detection
Production environments change. Lighting shifts, products evolve. Drift detection alerts you when accuracy drops below thresholds—before you discover problems through escaped defects.
Automated Retraining Pipelines
When drift occurs, retraining workflows make it efficient to correct. New data flows into training, updated models validate, deployment happens with minimal manual work.
Fleet Management
Running vision on dozens or hundreds of devices? Centralized management for deploying updates, monitoring status, and troubleshooting across your entire deployment.
The Production Vision Lifecycle
Continuous improvement loop built into every deployment
Common Questions About Computer Vision FastTrack
Why FastTrack Succeeds Where Other Projects Stall
Edge-First Architecture
We don't build for the cloud and try to fit it on edge hardware. Every architecture decision assumes edge deployment from day one.
Production Intent from Day One
From project kickoff, we're building for production. Hardware selection, integration planning, and operational requirements are addressed in week one—not month six.
Hardware Expertise
We know the trade-offs between Jetson variants, between x86+GPU and specialized accelerators. Deployment experience informs practical recommendations.
MLOps Maturity
Drift detection, retraining pipelines, model versioning—these aren't add-ons. They're foundational infrastructure delivered with every deployment.
Latency and Throughput Guarantees
We optimize for YOUR performance requirements. If you need 30 FPS with <50ms latency, we design for that constraint—not just maximum accuracy.
Knowledge Transfer
We're building YOUR capability, not dependency. Every engagement includes documentation, training, and handoff so your team can operate independently.
Investment & Engagement Options
Discovery Sprint
Visual environment assessment, use case feasibility analysis, hardware recommendations, data requirements evaluation, ROI assessment.
Best For: Organizations exploring computer vision possibilities
FastTrack Standard
Single use case, 1-4 camera deployment, edge hardware provisioning, MLOps infrastructure, integration with one target system, 30-day support.
Best For: Organizations with clear use case ready for production
FastTrack Enterprise
Multiple use cases, multi-camera/multi-location, complex integrations, advanced MLOps, fleet management, extended support, team training.
Best For: Large-scale deployments or complex environments
Ready to Give Your Operations the Gift of Sight?
Start with a conversation. We'll discuss your visual challenges, assess feasibility, and tell you honestly whether FastTrack is the right approach for your situation.
At a Glance
Key Takeaways:
- •Computer Vision FastTrack deploys production edge AI in 4-8 weeks with <200ms latency and 96% accuracy
- •Includes custom model training, edge optimization (TensorRT/ONNX), and MLOps workflows
- •Supports Jetson, x86, ARM; cloud fallback for batch processing
- •Typical deployment: 96% accuracy, 120ms p95 latency, 87% reduction in manual review
Compatibility Matrix
Safety & Governance
Complete chain-of-custody for all annotations, approvals, and model updates with reason codes and timestamps.
Configurable retention windows (7-365 days) with cold tier archival. Supports SOC2, CJIS, and industry-specific requirements.
Automatic face blurring, license plate redaction, and configurable masking zones for privacy compliance (GDPR, CCPA).
Industry Deployment Patterns
How different industries use Computer Vision in production environments.
Manufacturing
Defect detection & assembly verification
Identify surface defects, misalignments, and missing components on production lines with 96%+ accuracy
Transportation & Rail
Track/ROW inspection & rolling stock monitoring
Automated detection of rail surface defects, vegetation encroachment, and equipment anomalies reducing manual inspection time by 87%
Warehousing & Logistics
Package counting, damage detection & compliance
Real-time package damage detection and volumetric counting with sub-second latency for high-throughput operations
Retail
Shelf compliance & queue management
Monitor product placement, planogram compliance, and customer queue depth for operational optimization
Architecture Decision Guide
Choosing the right deployment architecture for your Computer Vision system.
| Approach | When to Use | Tradeoffs | Best For |
|---|---|---|---|
| Edge-Only | On-prem requirements, no cloud connectivity, <100ms latency needed | Lower cost, data sovereignty, limited model size, manual updates | Government/DOT, utilities, remote sites |
| Hybrid Edge + Cloud | Real-time inference at edge + batch analytics in cloud | Best of both worlds, resilient to connectivity loss, moderate complexity | Manufacturing, retail, logistics |
| Cloud-Fallback | Primary cloud inference with edge backup during outages | Simpler edge footprint, higher latency, cloud dependency | Enterprise with reliable connectivity |
Procurement & RFP Readiness
Common requirements for Computer Vision vendor evaluation and compliance.
Need vendor compliance docs? Visit Trust Center →
When to Choose What
Computer Vision FastTrack builds visual detection models. For text-based AI features, consider GenAI Accelerator.
Computer Vision FastTrack
Best for visual detection/tracking/classification
- ✓Object detection and tracking (people, vehicles, defects)
- ✓Quality inspection and defect classification
- ✓Edge deployment with low-latency requirements
- ✓Real-time video stream analysis
Computer Vision Deployment Outcomes
See the math →- •Model meets precision/recall target on your footage
- •Edge pipeline ≤ target latency; stable FPS
- •Ops workflow (review, re-label, retrain) live
- •Measurable reduction in manual review time (typically 87%)
- •Drift monitoring and automated retraining triggers
What You Get: CV Pipeline Deliverables
Our standards →Industry Benchmarks & Performance
Representative performance metrics from typical Computer Vision deployments.*
*Representative industry examples based on typical deployments. Actual results vary by use case, data quality, infrastructure configuration, and deployment environment. See our methodology →
Hardware & Technology Compatibility
Proven deployment stack across edge devices, streaming protocols, and inference frameworks.
Edge Hardware
Inference Frameworks
Streaming Protocols
Model Formats
Integration Points
Timeline
4 weeks (PoC), 8 weeks (production-ready with MLOps)
Team
CV lead, MLE, edge dev, FE, QA; ops reviewer for deployment
Industry Benchmarks & Statistics
Based on 40+ edge CV deployments across manufacturing, warehousing, and retail operations.
Inputs We Need
- •Sample footage or image sets (200-500 frames minimum)
- •Labeling guidelines or existing annotations
- •Target edge hardware specs (Jetson/x86/ARM)
- •Precision/recall targets and acceptable latency
- •Ops workflow requirements (drift alerts, retraining triggers)
Tech & Deployment
Edge hardware: NVIDIA Jetson (Nano/Xavier/Orin), x86 (Intel/AMD), ARM (RPi/custom). Models: YOLOv8/v11, EfficientDet, custom CNNs; ONNX/TensorRT export; INT8 quantization. Frameworks: PyTorch/TensorFlow → ONNX; TensorRT optimization for 3-5x speedup. Streaming: RTSP/RTMP/USB; frame buffering and batching; resilient reconnect with backoff; timestamp sync with clock drift checks. Deployment: Docker/K3s on edge; REST/MQTT for results; centralized model registry. Edge management: heat throttling guard; GPU temp monitoring; rolling buffers with cold tier (S3/Blob) lifecycle rules. MLOps: Prometheus/Grafana for drift; Label Studio/CVAT for re-labeling; DVC for dataset versioning. Cloud fallback: AWS/GCP/Azure for heavy inference or batch processing.
Proof We Show
Full evidence list →Frequently Asked Questions
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