Works in the lab, fails in the field
Accuracy drops on real footage: lighting, noise, camera angles, and rare classes break a pipeline trained on a clean dataset.
Computer vision and edge AI for real-world production
We develop computer vision and AI systems that run on-device and in production, not just in a researcher’s notebook:
- detection & segmentation
- defect detection
- edge inference
- multimodal AI
- local analytics
From our projects
¶ Client pain points
¶ What we do03 areas
What we design
We take AI from hypothesis to stable operation in real-world conditions.
01 / 03
Computer vision systems
Object detection, segmentation, and tracking for quality control, security, and system behavior analysis.
- object detection
- segmentation
- tracking
- defect detection
- OCR & scene analysis
02 / 03
Edge AI & on-device inference
We optimize and deploy models directly on embedded hardware — no cloud, no latency.
- ONNX, TFLite, TensorRT
- quantization & pruning
- real-time inference
- local analytics
03 / 03
Applied R&D and optimization
We train models on proprietary and small datasets, run experiments, and apply domain adaptation to your use case.
- small-data training
- domain adaptation
- optimization algorithms
- generative methods
¶ How we work06 steps
ML-Ready Engineering
- 01Stage
Scoping & metrics
We formalize the task, quality metrics, and business impact.
- 02Stage
Data & labeling
We collect, clean, and label data and build a labeling pipeline.
- 03Stage
Training & experiments
Baseline model, iterations, architecture comparison.
- 04Stage
Hardware optimization
Quantization and conversion for the target edge device.
- 05Stage
Deployment
We integrate the model into your product and infrastructure.
- 06Stage
MLOps
Quality monitoring in production, retraining, A/B tests.
¶ Case studies03 projects
87%
Anomaly detection system
Multimodal model for aircraft fault analysis
Project highlightRare classes and noisy real-world data.
9 mo
Wall-climbing robot
A magnetic-adhesion robot for surface inspection
Project highlightOn-device inference in a physically demanding environment.
91%
Eye-tracking system
Device control through gaze tracking
Project highlightReal-time tracking on constrained resources.
¶ Who it’s forWhere our models are used
Manufacturing
Quality control, defect detection
Security
Video analytics and monitoring
Robotics
Machine vision and navigation
R&D labs
Applied research with results
¶ Why BonsaiWhy clients ship AI with us
01
Edge-first
We design models for real hardware, not just a GPU server.
02
Data for the task
We build labeling and training even with small or proprietary datasets.
03
Engineering, not demos
We take models to stable 24/7 operation, not just to a polished prototype.
04
Full-stack ML
From data collection to deployment and MLOps, under one roof.
Assess your AI use case
We’ll run a technical review of your task:
- —check whether the target metrics are realistic
- —verify that your data is sufficient
- —select the architecture and hardware
- —estimate timeline and cost