Object Detection & Tracking
YOLO, DETR, and RT-DETR detectors trained on your domain images, with multi-object tracking, re-identification, and zone-based counting for fleet and floor monitoring.
- Custom detectors
- Multi-object tracking
- Zone counting
Production-ready computer vision engineers for object detection, image segmentation, OCR, video analytics, and edge inference. Onboarded inside your VPC, on your stack, on your sprint cadence, from day one.
Trusted by enterprises across Retail, Manufacturing, BFSI, Logistics, and FMCG
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With 24+ years of enterprise delivery and a bench of 500+ elite engineers, orangemantra operates as a full-cycle computer vision partner that ships production vision systems backed by accuracy harnesses, edge optimisation, and cloud-native MLOps.
Most CV demos look perfect on benchmark images, then fail on real factory floors and parking lots. Hire computer vision engineers who own the production stack: data annotation strategy, model selection, training, evaluation, edge deployment, and the retraining loop that keeps accuracy from drifting. Paired with AI development services, your computer vision programme becomes a measurable engineering practice, not a one-off proof of concept.
Our Core Computer Vision Capabilities
Every engagement moves through these three stages. Hire computer vision engineers who own each layer end-to-end, not specialists who hand off after the demo.
Real-world image and video datasets curated for the edge cases that actually break models: lighting, occlusion, motion blur, sensor variance. Annotation pipelines with reviewer queues, not crowdsource roulette.
Architecture selection across YOLO, DETR, SAM, and custom backbones. Training behind reproducible runs, and an evaluation harness tied to business outcomes, not just mAP scores.
Model quantisation, TensorRT optimisation, and over-the-air deployment to NVIDIA Jetson, Coral, and custom hardware. Drift alerts and retraining triggers keep accuracy honest in the field.
Pre-vetted CV engineers ready to start inside a fortnight. The bench covers detection, segmentation, OCR, and edge deployment without recruitment lag.
Every model ships behind evaluation suites tuned to business outcomes, not just benchmark mAP. Real-world lighting, occlusion, and motion are part of the harness.
Comfortable on NVIDIA Jetson, Coral, and AWS Panorama as well as cloud GPU clusters. The right deployment target for latency, bandwidth, and cost.
A working CV pipeline inside three to six weeks, then a hardened path to scale with quantisation, drift alerts, and inference cost controls.
PII redaction, consent capture, biometric audit trails, and regulator-ready reporting from day one. SOC 2, HIPAA, GDPR, and PCI DSS are the floor.
If a fleet model regresses at 2 am, the CV developers for hire are a Slack ping away. Coverage windows are set on the engagement, not on a generic SLA card.
The right answer depends on latency budget, bandwidth, where the camera lives, and how often models need retraining. Hire computer vision engineers who frame the trade-off before they buy GPUs.
Best when bandwidth is cheap, retraining is frequent, and the team is small. Engineers stand up SageMaker, Vertex AI, or Azure ML with autoscaling GPU inference and cost dashboards from day one.
For tight latency, expensive bandwidth, or data that cannot leave the device. Engineers quantise, optimise with TensorRT, and ship via over-the-air rollout with edge telemetry.
Inference at the edge, retraining and registry in the cloud. One model registry, one observability stack, one cost dashboard across both planes. Pairs naturally with MLOps engineers for the underlying pipelines.
NVIDIA DeepStream, GStreamer, and Triton inference graphs that hold P95 latency at fleet scale. Anomaly events route into operations dashboards and alerting.
SAM, CLIP, OWL-ViT, and similar vision-language models adapted with prompting, fine-tuning, or LoRA for domain-specific tasks where labelled data is scarce.
Short, sharp engagements to audit an existing CV estate, surface accuracy and cost risk, and produce a remediation plan you can act on next sprint.
Hire computer vision engineers who build for the line items operations can verify: defects caught, vehicles counted, documents processed, fraud signals raised, and per-camera inference cost.
Explore your CV use caseAI's impact on business is undeniable and immeasurable. Gear up with the orangemantra computer vision engineering team.
The hiring path is built around enterprise procurement reality, not freelancer marketplaces. NDA on day one, profiles inside 48 hours, interviews on your schedule, and onboarding through your security stack.
Start the Hiring BriefA 30-minute call to map your camera footprint, data annotation maturity, compliance constraints, and the shape of the team needed: detection lead, OCR specialist, edge AI engineer, or video analytics owner.
Three to five vetted computer vision engineers, ranked against the brief with prior work samples, accuracy benchmarks, and rate cards. No bait-and-switch profiles.
Technical interview on your terms, optional paid trial sprint on a sample dataset, and reference checks. Replace any engineer at no extra cost inside the trial window.
Engineers onboard to your identity provider, repos, annotation tools, and data perimeter. Delivery cadence locks to your sprint rhythm from week one.
Vision economics shift by sector. The team scopes the build to where the camera count, throughput requirement, or compliance load is already heaviest.
Diagnostic imaging assistants for radiology, pathology, and dermatology shipped under HIPAA audit trails, with reviewer queues and explainable overlays.
Identity verification, document tampering detection, and liveness checks under model risk management, with audit trails for every inference.
SKU recognition for planogram compliance, AR try-on for apparel and beauty, and visual search that turns a photo into a product page in milliseconds.
Surface defect classifiers running on edge devices at production speed, with reject queue routing, audit-grade capture, and central retraining loops.
ALPR for gates, yard truck tracking, damage capture at dock, and driver assistance vision running on vehicle and gantry-mounted cameras.
Online proctoring with face and gaze analysis, handwriting recognition for assessment, and engagement signals for adaptive content delivery.
A working computer vision system is a stack, not a single model. Hire CV engineers fluent across frameworks, model families, edge accelerators, annotation, and serving layers.
OpenCV
PyTorch
TensorFlow
Hugging Face Transformers
NVIDIA Jetson
ONNX Runtime
DVC
Weights & Biases
AWS SageMaker
Google Vertex AI
Azure Machine Learning
KubernetesThree models, one delivery floor. Switch between them as the build moves from proof of value to fleet rollout, without re-signing a master agreement.
The first sprint usually delivers a working detector or OCR pipeline. The next two harden it: real-world accuracy harness, edge deployment, drift monitoring, and cost controls before traffic moves over.
Talk to Our TeamReal reviews from teams that have shipped with orangemantra. Verified on Clutch and GoodFirms.
"They built defect detection that holds up to factory lighting and conveyor speed. False positives dropped by a meaningful margin after the first month."
Mar 2025
Feedback SummaryOrangemantra computer vision engineers built surface defect classifiers running on edge devices at line speed, with reject queue routing and reviewer overlays. The team handled annotation pipelines, model selection, TensorRT optimisation, and over-the-air rollout.
"They cut shelf audit time from hours to minutes per store. Honest engineers who pushed back when our SKU dataset needed cleanup before training."
Sep 2025
Feedback SummaryA three-engineer pod built SKU recognition and planogram-compliance scoring inside a mobile app for store associates. Model quantisation and offline inference let the app run on standard Android devices without a cloud round-trip.
"Onboarded inside our VPC on day one. PHI never left the perimeter, and the reviewer queue won the radiologists over fast."
May 2025
Feedback SummaryOrangemantra delivered a radiology triage assistant with X-ray and CT classifiers, explainable overlays, and reviewer queue workflows. PHI-aware training pipelines and audit logging let the system pass clinical governance review.
"They cut yard processing time and built a damage-evidence trail that has already paid for the engagement in claims savings."
Aug 2025
Feedback SummaryThe engagement built automatic licence plate recognition for gates, multi-angle damage capture at the dock, and an event-routing layer feeding the operations dashboard. Edge inference on gantry cameras kept latency inside the SLO at peak load.
Independent recognition from industry bodies and analyst platforms. Listed only where verifiable.
CIO Choice Recognition
Top IT Service
WARC Award
Globus Certifications
NASSCOM
ISO 27001A computer vision engineer builds and ships vision models that interpret images and video in production: object detection, image segmentation, OCR, facial recognition, video analytics, and edge inference. The role covers data annotation strategy, model selection, training, evaluation, and the engineering around inference latency, accuracy decay, and retraining loops.
Every release ships behind evaluation suites tied to business outcomes, not just mAP scores. Real-world lighting, camera angle, and edge-case coverage are part of the harness. Drift detection runs continuously and triggers retraining when accuracy decays past the SLO.
Yes. Real-time pipelines run on NVIDIA Triton, DeepStream, or custom GStreamer graphs. Model quantisation, TensorRT optimisation, and batched inference keep P95 latency inside the SLO at fleet scale.
Most engagements move from first call to billable work inside five to ten business days. Profiles arrive within 48 hours of the brief, interviews run on your schedule, and onboarding happens inside your VPC.
Edge wins when latency is tight, bandwidth is expensive, or data cannot leave the device. Cloud wins when the workload is bursty, retraining is frequent, or you need to centralise model governance. Hybrid is common: inference at the edge, retraining and registry in the cloud. Pair with MLOps engineers for the centralised plane.
Cost depends on scope, data annotation maturity, and engagement model. A focused CV proof of value sits in the lower tens of thousands of dollars, an ongoing production engineering pod bills by sprint, and hourly rotations cover spike work. Orangemantra shares a fitted estimate after a scoping call.