Reduce technical support costs by up to 40% by diagnosing issues remotely via live video, eliminating unnecessary truck rolls and parts dispatches.
Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Deploy real-time AI that analyzes live video to diagnose issues, guide customers, and slash field service costs.
Reduce technical support costs by up to 40% by diagnosing issues remotely via live video, eliminating unnecessary truck rolls and parts dispatches.
Our systems combine computer vision for object/gesture recognition with natural language processing to guide customers through troubleshooting in real time.
TensorRT or ONNX Runtime pipelines.Zendesk, ServiceNow, or custom mobile apps.Key Deliverables:
Move beyond reactive support. Explore our broader capabilities in Multimodal Customer Experience and Voice AI, or learn how we build secure, sovereign AI infrastructure with Confidential Computing for AI Workloads.
Our Live Video Diagnostic AI Systems are engineered to deliver concrete, quantifiable improvements in technical support operations, directly impacting your bottom line.
Our multimodal AI guides customers through visual troubleshooting steps in real-time, resolving issues remotely. This directly cuts the high costs and delays associated with field technician dispatches.
By combining computer vision for object/gesture recognition with contextual NLP, our systems provide agents with precise diagnostic insights, empowering them to solve complex issues on the first call.
Deployable as APIs that integrate directly into your existing CRM, helpdesk software, or mobile apps. Our systems are architected for elastic scaling to handle peak support volumes without degradation.
A transparent roadmap for developing and deploying a custom Live Video Diagnostic AI System, from initial scoping to full-scale integration.
| Phase & Deliverables | Starter (4-6 Weeks) | Professional (8-12 Weeks) | Enterprise (12-16+ Weeks) |
|---|---|---|---|
Phase 1: Discovery & Scoping | |||
Custom Diagnostic Workflow Design | 1-2 Standard | 3-5 Custom | Fully Bespoke |
Phase 2: Model Development & Training | |||
Computer Vision Model (Object/Gesture) | Fine-tuned Open-Source | Custom CNN/Transformer | Ensemble + SLM Edge |
Multimodal NLP for Guidance | Pre-built Intent Library | Custom Domain-Specific Tuning | Full DSLM + RAG Integration |
Phase 3: System Integration | Basic API Endpoints | Full SDK + CRM/CCaaS Connectors | End-to-End with Legacy Systems |
Real-Time Video Processing Latency | < 2 seconds | < 500ms | < 200ms |
Phase 4: Security & Compliance | Base Encryption | Data Anonymization + Audit Logs | Full Confidential Computing + Regional Data Engineering |
Ongoing Support & Maintenance | Email Support | SLA (99.5% Uptime) + Quarterly Updates | Dedicated Engineer + 99.9% Uptime SLA + AI Red Teaming |
Typical Investment | $40K - $80K | $120K - $250K | Custom Quote |
Our Live Video Diagnostic AI Systems deliver immediate operational impact by reducing on-site dispatches and accelerating resolution times. We engineer solutions for complex, real-world troubleshooting scenarios.
Guide customers through self-installation of routers and modems via live video. Our AI identifies incorrect cable connections, missing components, and LED status patterns, reducing technician dispatches by up to 40%. Integrates with existing CRM ticketing systems.
Enable remote technicians to diagnose machinery faults via live feed. Combines computer vision for part identification and gauge reading with NLP to interpret operator descriptions and procedural manuals. Critical for maintaining uptime in manufacturing and energy.
Provide step-by-step visual guidance for installing smart thermostats, security cameras, and home automation hubs. AI verifies physical placement, network connectivity, and device registration status, cutting support call duration and improving customer satisfaction scores (CSAT).
Allow customers to show dashboard warning lights, unusual sounds, or exterior damage via smartphone. Our multimodal system cross-references video/audio with vehicle make/model and service history to triage issues, schedule precise repairs, and verify warranty coverage.
Assist patients with at-home medical devices like CPAP machines, glucose monitors, or infusion pumps. Ensures correct usage and setup through compliant, secure video analysis, improving therapy adherence and reducing readmission risks. Built with HIPAA-compliant data pipelines.
Rapidly diagnose issues with point-of-sale systems, kiosks, or digital signage. AI analyzes error screens, peripheral connections, and receipt printer behavior to provide store staff with exact troubleshooting steps, minimizing downtime during peak hours.
A structured, iterative process to deliver production-ready diagnostic AI systems that reduce on-site dispatches.
We deliver a functional MVP in 2-4 weeks, focusing on a core diagnostic workflow to validate accuracy and latency.
Our methodology is built on three iterative phases designed for rapid validation and enterprise-grade scaling:
Phase 1: Foundation & MVP
OpenCV/MediaPipe for vision and a fine-tuned Whisper/Gemini model for NLP.Phase 2: Scaling & Optimization
Phase 3: Production & Autonomy
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get clear answers on how we build and deploy real-time AI systems that analyze live video for technical support and diagnostics.
From initial scoping to production deployment, a typical project takes 6-10 weeks. This includes 2 weeks for data pipeline setup and model selection, 3-4 weeks for core development and integration, and 2 weeks for testing and optimization. For complex integrations with legacy ticketing systems or custom hardware, timelines may extend to 12-14 weeks. We provide a detailed project plan with weekly milestones from day one.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
