C3PIO Core
Inference engine: task queue, batching, autoscaling and one API across every model.
engineAI · Computer Vision · Data Science
We turn video streams and data into decisions
We design, train and ship computer vision and machine learning models to production — from a single-camera pilot to an inference cluster serving thousands of streams.
Teaching machines to see
- fps
- 61
- latency
- 11.4ms
- streams
- 128
- helmet98.4%
- hi-vis vest96.1%
- restricted zone93.7%
- 01
A pilot in four to six weeks, on your infrastructure and your data
- 02
Quality metrics fixed in the contract, not in a slide deck
- 03
Source code, model weights and documentation stay with you
40+projects taken to production
3 500cameras under analytics
120models in the registry
8years of average engineer experience
What we do
Five practices covering the path from problem statement and data labeling to running models under production load.
01
Computer vision and video analytics
Real-time detection, tracking and event classification on existing cameras.
Learn more 02LLM and natural language processing
Assistants, document data extraction and multimodal models for business tasks.
Learn more 03Predictive analytics and ML models
Demand, equipment failure and anomaly forecasting from historical and telemetry data.
Learn more 04MLOps and inference infrastructure
A model registry, reproducible training runs and inference that holds the load.
Learn more 05Data labeling and preparation
Production-grade datasets with annotator agreement under control.
Learn moreThe C3PIO platform
Five modules a deployment is assembled from. Take the whole stack or a single piece — everything runs on your own infrastructure.
C3PIO Lens
Image and video processing: stream decoding, preprocessing, augmentation, camera calibration.
visionC3PIO Grid
Data and analytics: event marts, model quality metrics, dashboards for operations teams.
dataC3PIO Sentry
Real-time monitoring: data drift, accuracy degradation, alerts and on-call playbooks.
monitoringC3PIO Flow
Pipelines: from labeling and training to shipping a new model version without downtime.
pipelinesWhat the name encodes
C3PIO decodes twice: three pillars of the technology, and three things the system does to a stream of data.
C3three pillars of the technology
- 1
Computer Vision
Pulling meaning out of a frame: detection, segmentation, tracking, text and object recognition.
- 2
Cloud
Inference runs where you need it: in the cloud, on your own servers, or at the edge next to the camera.
- 3
Cognition
Wiring recognition into business logic: rules, events, reports, integration with your systems of record.
PIOwhat the system does with data
- 1
Perception
Cameras, sensors, documents and logs — anything that can be turned into a signal.
- 2
Intelligence
Models that decide within tens of milliseconds and can explain the decision.
- 3
Optimization
Less downtime, scrap and manual checking — measured in your own numbers.
Selected projects
Demonstration cases showing typical deployment scenarios.
Metallurgy2025
−64%violations after three months of operation
Personal protective equipment control
Automatic violation logging for helmets, vests and safety glasses across 240 shop floor cameras.
Logistics2025
99.1%recognition accuracy on live traffic
Licence plate recognition at the checkpoint
A vehicle detector cascaded with plate recognition at warehouse entry gates.
Manufacturing2024
×3.2faster batch inspection
Defect detection from product photographs
Surface defect classification for rolled metal from inspection line images.
Mining2024
−28%unplanned downtime per season
Conveyor equipment failure forecasting
A model on drive telemetry that predicts unplanned stoppages in advance.
From the engineering blog
Practical notes on training, metrics, inference and data preparation.
Real-time detection: where the time actually goes
Breaking down the latency budget of a video analytics pipeline and why speeding up the model rarely delivers the expected gain.
8 min readFine-tuning multimodal models on industrial data
What changes when you need a strict condition check on an image rather than a generated description.
12 min readDetector metrics a client can actually read
Why average precision over a dataset is a poor predictor of satisfaction with the system in operation.
7 min readNext step
Have a data or video problem?
Describe it in your own words. We will tell you whether it is solvable with the data you have and propose a pilot format.