Computer vision that works under your lighting, cameras and edge cases.
We build image and video systems for inspection, detection, counting and visual document processing — starting with the data and labeling plan, and testing under the real deployment conditions rather than a clean benchmark.
- Data collection and labeling plan
- Labeled dataset
- Trained vision model
- Deployment package
- Review interface and feedback loop
Where this helps
What we deliver
How it works
- 01
Site and data survey
We review the physical setup, cameras, lighting and existing images, and agree which errors matter most.
- 02
Pilot dataset
A first set of images is captured and labeled to test whether the visual signal is strong enough for a model.
- 03
Model development
We train and compare models, focusing error analysis on the defect types and conditions that carry the highest cost.
- 04
On-site validation
The model runs in shadow mode alongside current inspection so results can be compared on live input.
- 05
Rollout and retraining
Staged deployment with monitoring, and a scheduled process to add new examples as products and conditions change.
Design decisions we make with you
Edge or cloud
Edge inference for low latency, limited connectivity or privacy; cloud for heavier models and centralized management. See [IoT & edge](/specialist-services/iot-edge).
Capture conditions
Camera placement, resolution and lighting often improve accuracy more than a bigger model. We advise on hardware where it matters.
Accuracy measures
We agree which errors are costly — a missed defect versus a false alarm — and tune thresholds to that balance.
Specialized models or vision-language models
Purpose-trained detectors are fast and precise for fixed tasks; multimodal LLMs handle varied documents and open-ended questions. Some systems use both.
Privacy
Where video includes people, we design for minimal retention, masking and access controls from the start.
Applications
Related capabilities
- Machine Learning & Predictive AnalyticsForecasting, classification, anomaly detection and recommendation models trained on your data and evaluated against the decisions they support.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
- Generative AI & LLM DevelopmentApplications built on large language models that are grounded in your data, tested against real cases and costed before launch.
Questions buyers ask
It depends on how varied the scenes are and how rare the defects are. A pilot dataset in the first phase tells us whether the signal is strong enough and how much more data is needed.
Yes. Many vision systems run on edge devices or on-site servers. We size the model to the hardware available.
The review interface captures new cases, and the retraining process adds them to the dataset. We plan for this from the start rather than treating the model as finished.
Yes. We set up the labeling workflow and guidelines, and work with your experts on the judgments only they can make.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.