Ship computer vision from edge to cloud—fast

Capture on device, infer at the edge or in your VPC, and monitor everything with one observability spine. Launch detection, segmentation, and video analytics without rebuilding the stack each time.

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Why teams choose Lid Vizion for CV

 Edge + Cloud, one interface

Run ONNX/TensorRT/Core ML at the edge or scale bursts in your AWS; same job/event model

 Vendor-neutral, VPC-first

 Your infra, your data, open interfaces. No lock-in.

 Production starters

 Ingestion, queues, workers, review UI, vector search, and webhooks ready on day one.

 Built-in cost & reliability

 Per-tenant limits, autoscaling, retries, and audit trails.

See what you can  build

Ingestion & I/O

Uploads, presigned S3, Drive/OneDrive, RTSP, and batch jobs with type checks and AV scanning.

Block library

 Detection / segmentation / classification, tracking, post-processing, and sinks (S3, webhooks, alerts).

Review & labeling loop

 Keyboard-first QA, corrections, export to COCO/YOLO/CSV, and push deltas back to training.

Eval & golden sets

Compare models, measure mAP/precision/recall, track drift and edge-case performance.

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 Deploy

Cloud APIs in your AWS

API Gateway + queues + workers (Lambda/Fargate/ECS) with private networking and least-privilege IAM

Edge inference

ONNX Runtime / TensorRT / Core ML for Jetson, x86, iOS, Android, and browser (WebGL/ONNX Web). Offline-first optional.

Streams & video

RTSP/MP4 ingest, smart frame sampling, timeline events, and clip exports.


Model rollouts

Stage → canary → prod with signed artifacts and quick rollback

 Monitor

Metrics & traces

Latency, throughput, errors, and spend—by tenant and by job—with end-to-end traces.

Quality & drift

Outcome dashboards, alerts on regressions, and canaries on new models.

Human-in-the-loop

Queue low-confidence cases for review and route fixes back to datasets.

 How it works

Discover

Goals, constraints, success metrics.

Launch

Edge/cloud deploy, dashboards, alerts.

Blueprint

Data model, pipeline design, cost plan.

Improve

HITL loop, evals, and regular model releases.

Build

Wire ingestion, models, review, and exports.

How this runs on our BaaS

Our BaaS (Backend-as-a-Service) architecture powers the solution pages by abstracting the backend complexities and allowing rapid deployment and scalability.

Edge-first, cloud-backed

Jobs try on-device first (ONNX/TensorRT/Core ML) and automatically burst to cloud workers when needed.

Queue + worker model

Every job flows through API → Queue → Autoscaled Workers with retries and idempotency.

Registry + canary

Models promote stage → canary → prod with rollback and per-tenant targeting.

Works with your stack

Connect cloud, models, labeling, and devices in minutes.

Popular use cases

Manufacturing quality

Defects, misalignment, missing parts

Safety & PPE

Helmet/vest detection, zone alerts

Retail ops

Shelf gaps, price tags, planograms

Logistics & yard

Package detection, LPR, flow counts

Property & Real-Estate

Insurance inspections, pre-work analysis, and report exports

Sports & motion

Player tracking, events, highlight cuts

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 Ready to see it with your data?

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