Build intelligent vision + robotics systems that see, understand, and act in the real world At Tendify.IO, we develop production-grade AI object detection and robotic automation solutions that combine state-of-the-art computer vision (YOLOv8–v10, RT-DETR, EfficientDet, Grounding DINO, SAM 2, OWLv2, etc.) with industrial robotics control, edge deployment, and closed-loop decision making.
Our systems go far beyond simple classification — they perform real-time multi-object tracking, instance segmentation, pose estimation, anomaly detection, 3D grasping, path planning, and adaptive behavior in dynamic environments.
One powerful real-world application we have delivered (and continue to evolve) is fully autonomous waste sorting & recycling lines — where AI vision identifies dozens of material types at high speed on conveyor belts, while robotic arms precisely pick, sort, and divert recyclables with >95% accuracy even under varying lighting, occlusion, and mixed waste conditions.
Core Capabilities We Deliver
1. Advanced Computer Vision Engines
- Real-time object detection & classification (plastic bottles, metal cans, cardboard, electronics, food waste, hazardous items, textiles…)
- Instance & semantic segmentation for precise pixel-level boundaries
- Multi-class material recognition (PET, HDPE, PP, aluminum, paper grades, glass colors…)
- Contamination & defect detection (dirty items, non-recyclables, foreign objects)
- OCR + barcode / QR reading on packaging
- 3D point-cloud generation & depth-aware grasping (Intel RealSense, Zivid, Orbbec integration)
2. Robotics & Motion Control Integration
- 6-axis industrial arm path planning & collision avoidance (ROS 2, MoveIt!, Pilz, Franka, UR, ABB, Fanuc, Yaskawa)
- Pick-and-place with suction / gripper / soft robotics
- Adaptive picking strategies (re-grasp, re-orient, skip uncertain items)
- Conveyor synchronization & dynamic speed adjustment
- Safety-rated human-robot collaboration zones
3. Edge & Cloud Hybrid Architecture
- Edge inference on NVIDIA Jetson Orin / Xavier / AGX, Hailo-8, Intel Movidius, Coral TPU
- Low-latency decision making (<50 ms per frame typical)
- Cloud model retraining pipeline with active learning from misclassified items
- Digital twin simulation for testing new classes without stopping production
4. Industry Applications We Build (Waste Sorting is Just One Example)
- Waste Management & Recycling — high-throughput MRF sorting lines (single-stream, dual-stream, C&D, commercial)
- Manufacturing Quality Control — defect detection on assembly lines (electronics, automotive parts, food packaging)
- Logistics & Warehousing — parcel dimensioning, label reading, mixed SKU depalletizing, returns sorting
- Food & Beverage — foreign object detection, ripeness grading, portion control, expiry date verification
- Agriculture — fruit/vegetable picking robots, weed detection, diseased plant identification
- Pharmaceuticals — blister pack inspection, counterfeit detection, serialization verification
- Security & Surveillance — anomaly detection in restricted areas, unattended bag detection, PPE compliance monitoring
- Retail Back-of-House — shelf restocking automation, inventory counting via ceiling cameras
Technical Highlights of Our Implementations
- Models trained on millions of labeled industrial images + synthetic data augmentation
- Custom datasets creation & annotation pipelines (including your own factory footage)
- MLOps: versioned models, CI/CD for vision pipelines, drift monitoring & auto-retraining
- Integration with PLC / SCADA / MES / WMS systems (OPC UA, MQTT, REST, Modbus)
- Full audit logs, explainable AI heatmaps, and performance dashboards
Investment Guide for Custom AI Vision + Robotics Projects
| Project Scope | Approx. Investment Range | Typical Timeline |
|---|---|---|
| Proof-of-Concept (single class / station) | $18,000 – $35,000 | 6–10 weeks |
| Full Sorting Line MVP (8–15 classes, 1–2 robots) | $45,000 – $85,000 | 12–18 weeks |
| Enterprise Multi-Line Deployment + Retraining Loop | $95,000 – $220,000+ | 5–12 months |
- End-to-end development (hardware selection → model training → deployment → commissioning)
- On-site integration & tuning
- 3–6 months post-go-live support & model improvement
- Training for your operators & maintenance team
- Source code handover + documentation
This is the exact technology stack we use to help recycling facilities, manufacturers, logistics operators, and food processors reduce manual labor, increase purity rates, lower contamination fines, and achieve ESG targets faster.
Project Showcase Reference: Inspired by advanced waste-sorting robotics deployments (similar to high-end systems seen in modern MRFs).
Ready to bring AI vision + robotics to your operation? Tell us your industry, throughput target, and biggest current pain point (contamination rate? labor shortage? new material stream?) — we’ll prepare a tailored feasibility concept + rough architecture in 72 hours.
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