Samprakshi Infinity Solution

Computer Vision Solutions

Build production-ready computer vision systems — from data collection and labeling to model deployment and monitoring.

Showcase 1

Object Detection & Tracking

Accurate, low-latency object detection and multi-object tracking for live video, CCTV and robotics — optimized for both cloud and edge deployments.

DetectionTracking
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Image Segmentation

Pixel-level segmentation tailored for medical imaging, manufacturing inspection and AR compositing, with refined post-processing for production use.

SegmentationMasking
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Optical Character Recognition (OCR)

High‑accuracy OCR pipelines for documents, invoices and receipts with layout-aware parsing, language support and downstream data normalization.

OCRExtraction
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Pose Estimation & Action Recognition

Human pose and activity recognition for sports analytics, workplace safety and AR experiences, including multi-person scenarios and temporal smoothing.

PoseAnalytics
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Visual Search & Recommendation

Image-based search and product recommendation using learned embeddings and similarity search tuned for relevance and speed at scale.

SearchEmbeddings
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Model Deployment & MLOps

Production-grade model serving, monitoring, CI/CD for models, drift detection and automated retraining pipelines to keep vision systems healthy in production.

MLOpsDeployment
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Frequently asked questions

We build and deploy object detection, semantic/instance segmentation, OCR, pose estimation, and visual search solutions — each tailored to the customer dataset and latency requirements.
Yes — we optimize, quantize and convert models to ONNX/TFLite/TensorRT so they run efficiently on mobile, embedded and industrial edge hardware while preserving accuracy.
We offer managed annotation pipelines, quality control, and tooling to create labeled datasets (bbox, polygon, keypoints) plus active-learning workflows to reduce labeling cost.
We use task-specific metrics (mAP, IoU, precision/recall) and production monitoring for latency, throughput and data drift, with alerts and observability hooks.
We integrate via REST/gRPC endpoints, streaming (Kafka) pipelines, or on-device SDKs depending on throughput, latency and privacy constraints.