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10 business days
To start a computer vision engagement
Scoping through first sprint
100%
Senior engineers, US-based
No offshore handoff mid-project
Every sprint
A pipeline running on real footage
Not a status deck between milestones
100%
Code and models you own
From the first commit
Where OpenCV fits
Where OpenCV actually earns its place
OpenCV shows up anywhere a system has to make a decision from a camera feed or an image, in real time or close to it. The use cases below share a library and almost nothing else about what the pipeline needs to do.
Quality inspection and manufacturing vision
Cameras on a production line checking for defects, misalignment, or missing parts, running fast enough to keep pace with the line rather than sampling after the fact. Lighting consistency and camera calibration matter more here than model sophistication — a poorly lit inspection station will beat the best model every time.
Retail and inventory vision
Shelf-stock detection, queue and footfall counting, and loss-prevention analytics built from existing camera infrastructure rather than requiring new hardware. The engineering challenge is usually variance — lighting, camera angle, and occlusion differ store to store in ways a model trained on one location won't generalize past.
Security and surveillance analytics
Motion detection, person and vehicle tracking, and anomaly detection layered onto existing camera systems, tuned to cut false-positive alerts down to a volume a human operator can actually act on rather than tune out.
Medical and scientific imaging
Image preprocessing, segmentation, and measurement pipelines for microscopy, diagnostic imaging, or lab automation, where accuracy and reproducibility matter more than raw frame rate and the validation requirements are as much a part of the build as the algorithm.
Robotics and AR perception
Camera calibration, feature tracking, and visual odometry — the perception layer that tells a robot or an AR system where it is and what's around it, usually running on constrained edge hardware where every millisecond of latency is a real budget line, not a nice-to-have.
Document capture and OCR pipelines
Image preprocessing — deskewing, denoising, contrast correction — ahead of an OCR engine, which is often the difference between a document pipeline that works reliably and one that only works on the clean scans from the demo.
What's involved
OpenCV engagement types
What moves the scope of a computer vision project isn't camera count — it's how far the work has to travel from a working algorithm to something that runs reliably at the frame rate and on the hardware you're actually deploying to.
| Engagement | Commitment | Timeline | What's included |
|---|---|---|---|
| CV feasibility and data audit | Fixed scope | 1 – 3 weeks | A working test against your actual footage and hardware, and an honest read on whether classical CV, a deep-learning model, or both are the right approach before committing to a build. |
| Prototype-to-production port | Fixed scope | 4 – 10 weeks | Taking a Python notebook or proof of concept and rebuilding the pipeline for the throughput, memory, and reliability a production system actually needs. |
| Real-time pipeline build | Fixed scope | 6 – 14 weeks | A new camera-to-inference-to-action pipeline built and tuned against your actual frame-rate budget, not a best-effort target. |
| Edge or embedded deployment | Fixed scope | 4 – 10 weeks | Model optimization and pipeline work for constrained hardware — quantization, hardware-accelerated inference, and the power and memory budget that server-side deployments don't have to think about. |
| Ongoing pipeline and model support | Ongoing retainer | Ongoing | Standing ownership of a live vision system through retraining, drift monitoring, and hardware changes. |
Ranges assume US-based senior engineers and include the calibration and validation work a cheaper quote often strips out. The feasibility pass exists as a first step because a demo that works on curated sample images can fail entirely on the lighting and camera angles a real deployment actually sees.
Classical CV vs. deep learning
Classical computer vision vs. deep learning, and when a pipeline needs both
OpenCV is best known as a bridge to deep-learning inference, but a large share of a good pipeline is still classical computer vision — and knowing which tool solves which part of the problem is most of what separates a fast, reliable system from an overbuilt one.
When classical CV beats a neural network
Thresholding, contour detection, edge detection, and feature matching are fast, deterministic, and don't need a training set — the right choice for well-controlled conditions like a fixed inspection camera with consistent lighting, where a neural network would be slower and less predictable for no real accuracy gain.
When a model is genuinely required
Object detection and classification across varied conditions — different lighting, angles, occlusion, object types the system has never seen labeled — is where a CNN or a detector like YOLO earns its cost. The tradeoff is a training and labeling pipeline that classical CV never needs.
Hybrid pipelines are the common real answer
Most production systems use classical CV to preprocess and narrow down a frame — cropping a region of interest, correcting for lens distortion — before handing a smaller, cleaner input to a model. This is usually both faster and more accurate than running inference on a full, unprocessed frame.
Deployment format decides more than the model architecture
A model exported to ONNX or optimized with TensorRT can run meaningfully faster than the same model in its original training framework, and the export step is where a lot of the actual production speedup comes from — often more than switching to a lighter model architecture would.
CPU vs. GPU is a cost and latency decision, not a default
GPU inference is faster per frame but adds real cost and power draw that doesn't always make sense on an edge device running off battery or a small power budget. Whether a pipeline needs a GPU at all is a question worth answering with real throughput numbers, not assumed from the start.
OpenCV's BSD license keeps the stack unencumbered
OpenCV itself is free under a permissive BSD license, which matters for commercial deployment, but any pretrained models or datasets bundled into a pipeline can carry their own separate licensing terms worth checking before they ship in a product.
Prototype vs. production
What breaks between a working demo and a production pipeline
A computer vision demo that works well in a controlled setting and a system that holds up in the field are different engineering problems, and the gap between them is where most of the real cost lives.
Notebook code isn't a pipeline
A Jupyter notebook that processes a folder of sample images proves the algorithm works; it doesn't handle a live camera feed, a dropped frame, a corrupted file, or a process that has to run unattended for weeks. Rebuilding that as a real service is most of the production work.
The frame-rate budget decides the algorithm choice
A pipeline that needs to process video in real time has a hard millisecond budget per frame, and that budget — not accuracy in isolation — often rules out the most accurate available model in favor of one that's fast enough to keep up.
Lighting and camera variance breaks demos in the field
A model or algorithm tuned against demo footage shot in good, consistent light routinely fails against the actual lighting, weather, or camera placement of a real deployment. Testing against representative field conditions early avoids discovering this after rollout.
Edge constraints are a different budget than a server
Power draw, memory, and thermal limits on an embedded or edge device rule out approaches that work fine on a server with a full GPU, which is why an edge deployment often needs its own optimization pass rather than reusing the server-side pipeline as-is.
Retraining is a loop, not a one-time step
A model's accuracy degrades as real-world conditions drift from its training data — new product packaging, a new camera angle, a new season's lighting. A pipeline that has no plan for collecting new labeled data and retraining is a pipeline that gets worse over time, quietly.
Monitoring the pipeline, not just the model
A dropped camera feed, a corrupted frame, or a silently crashed inference process looks identical to "working fine" if nothing is watching for it. Production pipelines need health checks and alerting on the pipeline itself, not just accuracy metrics on the model.
What we build
The kinds of systems we actually build with OpenCV
A cross-section of the work that comes up most often, whether the starting point is a blank slate or an existing pipeline that needs to get to production.
Camera calibration and lens correction
Correcting for lens distortion and establishing an accurate mapping from pixels to real-world measurements — the unglamorous groundwork that measurement-accuracy use cases like inspection and robotics depend on entirely.
Object detection and tracking pipelines
Identifying and following objects across frames, from a single-camera setup to a multi-camera system that has to hand tracking off between overlapping fields of view without losing or duplicating a track.
OCR and document processing
Preprocessing — deskew, denoise, contrast correction — ahead of an OCR engine, plus the layout analysis that tells a downstream system which extracted text is a total, a date, or a line item rather than an undifferentiated block.
Motion and anomaly detection
Background subtraction and motion analysis tuned to flag what's actually unusual against a scene's normal activity, rather than firing on every passing shadow or gust of wind.
Integration with existing ML models
Wiring an already-trained model — yours or a third party's — into a real pipeline: preprocessing, inference, post-processing, and the application logic that acts on the result, rather than retraining a model that already does its job.
Multi-camera real-time pipelines
Synchronizing and processing feeds from several cameras at once, with the throughput and buffering work that keeps one slow camera from stalling the whole pipeline.
Related
Related services
What computer vision projects usually connect to.
Questions
Common questions about OpenCV development
What teams ask before a first call.
What drives the cost is less the algorithm itself and more how far the work has to travel from a working prototype to a production system — the frame-rate budget, the hardware it deploys to, and whether a deep-learning model needs to be trained or just integrated.
A feasibility pass against your actual footage and hardware is usually the fastest way to get a real figure, since a computer vision quote based on a generic description tends to be wrong in one direction or the other.
Builds and tunes the image-processing and computer-vision pipeline that turns a camera feed or an image into a decision — detection, measurement, classification, tracking — and, on most real projects, the surrounding engineering that gets that pipeline running reliably in production: deployment, monitoring, and integration with whatever system acts on the result.
On projects involving deep learning, that also includes preparing training data, evaluating models, and optimizing a trained model for the hardware it will actually run on.
Real-time video is one of OpenCV's core strengths — it's written in optimized C/C++ specifically to process frames fast enough for live video, not just batch-process a folder of images. Whether a specific pipeline hits real-time performance depends on the algorithm choice, the resolution and frame rate of the input, and the hardware it runs on.
That's the first thing we test in a feasibility pass, against your actual footage rather than a generic benchmark.
Yes, and this is one of the most common engagements we run. A Python prototype proves the algorithm works; production usually means rebuilding parts of the pipeline in optimized C++, handling live camera input instead of static files, and adding the monitoring and error handling a notebook never needed.
We start by profiling the prototype against your real throughput requirements so the port targets the actual bottleneck rather than rewriting parts that were already fast enough.
Yes. OpenCV handles the preprocessing — deskewing, denoising, contrast correction — that usually determines whether an OCR engine performs well or poorly, and we build the layout analysis around it that tells a downstream system what kind of text it's looking at, not just what the text says.
Yes — this is a large share of real OpenCV work. Most production computer vision pipelines pair OpenCV's classical image processing with a separately trained deep-learning model, whether that model is yours, a third party's, or one we train as part of the engagement. The integration work is the preprocessing, inference call, and post-processing logic around the model, not retraining a model that already does its job well.
Yes. Edge and embedded deployment is its own optimization pass — quantizing a model, using hardware-accelerated inference where the device supports it, and designing around a power and memory budget that a server-side deployment doesn't have to think about. We test against your actual target hardware rather than assuming a server-tuned pipeline will perform the same way on it.
Yes. Source code, pipeline configuration, and any models trained as part of the engagement are yours, in your own repository and infrastructure from the first commit. That's the only arrangement that doesn't lock a vision system you depend on to a single vendor for its next retraining cycle or its next hardware migration.