Deploy a fractional Vision AI team that designs, trains, and ships production-grade computer vision systems — from object detection and image classification to multimodal AI and video analytics.
Computer vision requires rare ML expertise, large labeled datasets, and hardware-specific optimization. Building it in-house from scratch is slow, expensive, and full of surprises. We've seen them already.
Vision AI in production isn't just about model accuracy. It's about lighting variability, occlusion, class imbalance, real-time latency constraints, and graceful handling of out-of-distribution inputs.
We bring the experience of deploying vision systems across manufacturing, retail, healthcare, and real estate — so you don't discover the edge cases on your users.
We adapt state-of-the-art base models to your specific visual domain — handling class imbalance, rare defects, and challenging environments through data augmentation.
We profile and optimize inference for your target hardware — TensorRT for NVIDIA, CoreML for Apple Silicon, quantization for edge devices — hitting your latency SLA without sacrificing accuracy.
We design training sets and augmentation pipelines that account for lighting changes, camera angles, seasonal variation, and sensor differences — so models don't break when conditions change.
Specialized expertise deployed directly into your engineering pipeline.
Custom-trained detection models for your specific objects, defects, or scenarios — fine-tuned on your data and optimized for your deployment environment, whether edge, cloud, or mobile.
Systems that reason across images and text — visual question answering, document understanding, image-to-structured-data extraction, and conversational visual intelligence.
Real-time and batch video processing pipelines that track, count, classify, and analyze events across time — for surveillance, retail analytics, manufacturing QA, and media.
We don't just write code and leave. We integrate seamlessly with your goals.
We assess your image or video data, annotation quality, and use case requirements to define the model architecture and data augmentation strategy.
We curate, annotate, and validate training data — including synthetic data generation when real-world samples are scarce or imbalanced.
We train and evaluate models against your accuracy, latency, and precision targets — iterating until the system meets production requirements.
We deploy the model to your target environment — cloud API, edge device, or embedded system — and hand over full documentation and monitoring.
Every engagement ends with working software, documented systems, and a team that knows how to extend them. You own the intellectual property.
A custom-trained, domain-adapted model calibrated to your specific objects, classes, and visual conditions — with full weights and training artifacts.
Production-ready inference pipeline with preprocessing, postprocessing, batching, and output formatting — integrated with your data sources and downstream systems.
mAP, precision, recall, and inference latency benchmarked across your test set — with per-class analysis and failure mode documentation.
Model cards, integration guides, retraining playbooks, and runbooks — everything your team needs to extend, retrain, and maintain the system independently.
Real scenarios, real numbers. The specifics change — the pattern is consistent.
A factory floor was manually inspecting 2,000 units per shift for surface defects. We trained a defect detection model on their historical rejection images that runs on existing camera hardware.
A retailer needed shelf compliance monitoring across 400 stores without adding field staff. We built a computer vision pipeline that analyzes store photos and flags planogram violations automatically.
A property platform wanted to automatically tag and categorize listing photos by room type, features, and quality score. We trained a multi-label classifier on their image library.
Real engagements from this practice area — the challenge, the build, and the outcome.
Achieved 32% revenue growth, 28% faster ESG reporting, and 40% client retention in 6 months by solving data fragmentation and compliance challenges for textile sustainability reporting.
A leading US-based materials testing lab improved customer retention by 35% and captured 42% more enterprise leads within six months by deploying a domain-trained AI chatbot.
An India-based public cloud provider piloted an Agentic AI-driven competitive intelligence system for the ME region, delivering 45% faster insights, 35% better targeting, and driving 38% revenue growth.
The questions most teams ask us before they decide to move forward.
Ask us anythingIt depends on the task complexity and the availability of pre-trained base models. For object detection on common categories, a few hundred labeled examples can get you to 80%+ accuracy. For niche industrial use cases, we use data augmentation and synthetic data generation to stretch smaller datasets. We'll scope this precisely after reviewing your existing data.
Yes. We work with NVIDIA Jetson, Raspberry Pi, and common IP camera platforms. We optimize models using TensorRT, ONNX, and quantization to meet the compute and memory constraints of your target device.
It depends heavily on task complexity, data quality, and environmental variability (lighting, angle, occlusion). We set accuracy targets during scoping based on your data and use case, and we don't deploy until we've validated against real-world samples. We'd rather reset scope than ship a model that doesn't meet your business requirements.
Yes. We build real-time video pipelines for detection, tracking, counting, and event detection — including multi-camera setups and long-duration video analytics. Streaming latency and storage efficiency are always part of the design.
Book a 30-minute strategy session. We'll assess your use case, tell you what's feasible with your data, and map out exactly what an engagement looks like.
