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We build blazing-fast, AI-powered web apps using the latest tech. From React to GPT-4, our stack is built for speed, scale, and serious results.
What Powers Our Projects
Every project gets a custom blend of tools—no cookie-cutter code here. We pick the right tech for your goals, so your app runs smooth and grows with you.
“Great tech is invisible—until it blows your mind.”
We obsess over clean code, modular builds, and explainable AI. Weekly updates and async check-ins keep you in the loop, minus the jargon.
Trusted by startups, educators, and SaaS teams who want more than just ‘off-the-shelf’ solutions.
We don’t just follow trends—we set them. Our toolkit is always evolving, so your product stays ahead of the curve.
From MVPs to full-scale platforms, we deliver fast, flexible, and future-proof solutions. No tech headaches, just results.
Ready to build smarter? Let’s turn your vision into a launch-ready app—powered by the best in AI and web tech.
Lid Vizion: Miami-based, globally trusted, and always pushing what’s possible with AI.
From Miami to the world—Lid Vizion crafts blazing-fast, AI-powered web apps for startups, educators, and teams who want to move fast and scale smarter. We turn your wildest ideas into real, working products—no fluff, just results.
Our Tech Stack Superpowers
We blend cutting-edge AI with rock-solid engineering. Whether you need a chatbot, a custom CRM, or a 3D simulation, we’ve got the tools (and the brains) to make it happen—fast.
No cookie-cutter code here. Every project is custom-built, modular, and ready to scale. We keep you in the loop with weekly updates and async check-ins, so you’re never left guessing.
“Tech moves fast. We move faster.”
Trusted by startups, educators, and SaaS teams who want more than just another app. We deliver MVPs that are ready for prime time—no shortcuts, no surprises.
Ready to level up? Our team brings deep AI expertise, clean APIs, and a knack for building tools people actually love to use. Let’s make your next big thing, together.
From edge AI to interactive learning tools, our portfolio proves we don’t just talk tech—we ship it. See what we’ve built, then imagine what we can do for you.
Questions? Ideas? We’re all ears. Book a free consult or drop us a line—let’s build something awesome.
Fast MVPs. Modular code. Clear comms. Flexible models. We’re the partner you call when you want it done right, right now.
Startups, educators, agencies, SaaS—if you’re ready to move beyond just ‘playing’ with AI, you’re in the right place. We help you own and scale your tools.
No in-house AI devs? No problem. We plug in, ramp up, and deliver. You get the power of a full-stack team, minus the overhead.
Let’s turn your vision into code. Book a call, meet the team, or check out our latest builds. The future’s waiting—let’s build it.
• AI-Powered Web Apps • Interactive Quizzes & Learning Tools • Custom CRMs & Internal Tools • Lightweight 3D Simulations • Full-Stack MVPs • Chatbot Integrations
Frontend: React.js, Next.js, TailwindCSS Backend: Node.js, Express, Supabase, Firebase, MongoDB AI/LLMs: OpenAI, Claude, Ollama, Vector DBs Infra: AWS, GCP, Azure, Vercel, Bitbucket 3D: Three.js, react-three-fiber, Cannon.js
Blogs
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Real-time sports analytics blends on-device vision, ML, and cloud to deliver instant insights during play. In a pickleball tracking scenario, an iOS/Swift app can capture live video and track a ball with a Kalman filter (robust for following the ball’s position. A bounce/hit detector then analyzes the trajectory to classify events. To scale, we use a hybrid pipeline: on-device inference for low-latency tracking, optional AWS GPU offload for heavy ML, and MongoDB to store match metadata and detailed logs. A React dashboard replays rallies, visualizes trajectories, and surfaces analytics.
On iOS, use Vision/Core ML in Swift to localize the ball in real time (e.g., lightweight detector or color segmentation), then smooth with a Kalman filter for stable trajectories and occlusion robustness (works well for ball position + requirements; Vision/CoreML real-time pattern).
For event detection, favor kinematics over fixed thresholds:
If you need occasional heavy inference (e.g., higher-accuracy model or 3D trajectory), send cropped frames or event snippets to the cloud. Many iOS apps rely on Vision + CoreML for real-time on-device detection and only escalate when needed (pattern).
A sports-AI pattern is edge for instant feedback, cloud for heavier analysis—minimizing latency while preserving accuracy at scale (edge-cloud roles). On device, optimize models (quantization, compression) to sustain real-time throughput and battery life; this is a common technique to keep inference fast on phones (edge optimization & compression). The app streams ball positions + event flags (and, when needed, short clips) to the backend; the cloud can batch-reprocess full matches (e.g., higher-fidelity models) without realtime deadlines.
Both paths persist to MongoDB: match metadata (players, scores), events (bounces/hits), and dense logs (frame-wise positions, velocities). A NoSQL store is a strong fit for high-volume, semi-structured sports/video data and real-time ingestion (NoSQL for sports analytics). One case study highlights storing all raw athlete data first so future features have history available—a philosophy we adopt for trajectories and event logs (capture first, analyze later).
Use MongoDB collections for:
MongoDB’s flexible documents and time-series features handle dense sequential data and evolving schemas common to CV analytics (scaling real-time sports data). Store only references to large media; keep full video in object storage when needed.
Build an interactive React dashboard for coaches/players: live events, replayable trajectories (canvas/WebGL), speed charts, bounce-height plots, and shot heatmaps. Real-time updates via WebSockets/SSE keep the UI in sync with new events. This approach—React + charting libs—maps directly to modern sports analytics dashboards that update live (dashboard pattern).
On-device: ultra-low latency and offline operation with zero per-request cloud cost, but constrained compute/battery and app size (device vs cloud trade-offs).
Cloud GPUs: run larger/more accurate models or many streams at once, at the cost of network latency and per-request billing (many services price per N requests) (cost model overview). Optimize spend by sending only what’s needed (key frames/features) and using cost savers like EC2 Spot (up to ~90% discount) or specialized silicon (Inferentia/Trainium) for lower $/inference (AWS cost optimization options).
Net strategy: Default to on-device for tracking + event detection; escalate to cloud for heavy, non-urgent, or post-match processing. This keeps in-play feedback snappy while still benefiting from cloud-scale analysis.
The system pairs Swift + Core ML for immediate on-court ball tracking and event detection (on-device pros), a MongoDB backend for rich match/event/trajectory logs (sports NoSQL fit), optional AWS GPU analysis for heavy lifting (edge vs cloud roles), and a React dashboard for live insight and replay (live analytics UI). This hybrid edge–cloud architecture delivers instant feedback during play and deeper insights afterward—while keeping latency, bandwidth, and cost in balance.