AI Guidance for Physical Work

Build a real-time multimodal AI copilot that gives physical workers instant skill augmentation through live guidance. Objective: Enable technicians, operators, and frontline workers to perform complex physical tasks with minimal prior training by using AI that sees what they see and guides them step-by-step. This is not task management software. It is an AI skill layer for the physical world. Core vision: Physical jobs require months or years of apprenticeship because knowledge is trapped in manuals and experienced workers. This platform compresses expertise into real-time AI coaching that: - Observes the worker’s environment through phone or wearable camera - Understands tools, equipment, and physical context - Guides each step with clear instructions - Detects errors and unsafe actions - Suggests troubleshooting paths - Escalates intelligently when confidence is low The goal is to dramatically reduce training time and increase first-pass success rates. Core system architecture: 1. Live Multimodal Guidance Engine - Real-time camera input from phone or smart glasses - Visual recognition of equipment, components, and tools - Context-aware step instructions (“turn off that valve”, “use ⅜ inch wrench”) - Hazard detection and PPE reminders - Step validation before proceeding AI guidance adapts dynamically based on what the worker is seeing. 2. Skill Compression Layer - Contextual micro-training during live jobs - On-demand explanations of why a step matters - Automatic surfacing of relevant prior cases - Progressive skill scoring per worker - Transition from “guided mode” to “assist mode” as skill improves The system should meaningfully reduce onboarding time. 3. AI Troubleshooting Brain - Symptom-based reasoning - Real-time comparison to known failure modes - Suggest diagnostic tests ranked by probability - Predict next likely failure - Recommend parts and tools The AI should reduce second visits and unnecessary part swaps. 4. Supervisor Escalation + Remote Assist - Escalation when AI confidence drops - Auto-generated case summary including video snapshots - Supervisor review and override - Feedback loop to improve model guidance 5. Safety Intelligence Engine - Mandatory confirmation for hazardous steps - Detection of unsafe positioning or missing PPE - Incident logging with contextual evidence - Pattern detection for repeated safety risks 6. Knowledge Flywheel Every completed job feeds: - Asset-specific resolution database - Failure mode clustering - Best-known-fix ranking - Time-to-resolution optimization - Continuous model improvement The system becomes smarter as it is used. Roles: - admin - dispatcher - field-worker - supervisor - safety-officer Data model: - sites - assets - work-orders - procedures - live-session-streams - checklist-runs - evidence-photos - incidents - escalations - worker-notes - training-records - audit-events - failure-modes - resolution-patterns Operational safeguards: - High reliability in low-connectivity environments - Encrypted video and photo storage - Deterministic audit logs - Strong role-based permissions - Clear human override mechanisms Success metrics: - Reduction in training time - First-pass completion rate - Reduction in supervisor escalations - Time-to-resolution improvement - Safety incident reduction Positioning summary: This platform gives physical workers AI superpowers. Instead of replacing workers, it upgrades them — allowing novices to perform skilled tasks immediately, reducing training time, improving safety, and increasing productivity. It is the physical-world equivalent of giving developers access to an AI coding agent. Create a modern startup design inspired by Y Combinator (YC) companies. Choose one bright primary color and build a clean, minimal color scheme around it. The design should feel bold, simple, and product-focused with strong typography, generous whitespace, and clear hierarchy.