We build AI systems that see, listen, think, and act. Not demos — production infrastructure. Computer vision, speech AI, autonomous agents, decentralized compute.
Zpoken is a Ukrainian AI engineering studio founded in 2017. We develop custom neural networks and deploy production-grade AI systems across computer vision, speech analytics, LLM agents, and decentralized infrastructure. Every solution we ship replaces manual work, eliminates human error, and scales.
Object detection, facial recognition, satellite image analysis, behavioral analytics. Production CV systems that see what humans can't.
Voice bots, speech recognition, call analytics, automatic summarization. Neural networks that understand language in real-time.
Autonomous systems that think, decide, and execute. LLM-powered agents that replace entire workflows and operate 24/7.
End-to-end data science — from research and model training to integration and maintenance. Full-cycle ML engineering for any domain.
AI infrastructure on blockchain — consensus mechanisms for compute verification, distributed inference networks, tokenized GPU coordination.
Trusted Execution Environments, secure enclaves, and privacy-preserving inference. AI that processes sensitive data without exposing it.
Enhanced Gamaya's digital agronomy platform with neural networks that process satellite and drone imagery to detect crop diseases, monitor growth patterns, and predict sugarcane yield. Our models analyze hyperspectral data across thousands of hectares, enabling precision farming decisions that improve yield while reducing environmental impact.
Built the vision system enabling Rope Robotics' BR-8 robot to autonomously latch onto wind turbine blades without human input. Developed fisheye image processing, edge distance measurement via classic CV, and a lightweight neural network for geopositioning calibration — all running on a Raspberry Pi for field deployment.
Engineered core infrastructure for Gonka's decentralized AI network — LLM inference optimization with vLLM, a novel Proof-of-Compute consensus mechanism that allocates ~100% of GPU power to useful AI work, and TEE integration for secure inference verification across a distributed network of 6,000+ H100-equivalent GPUs.
Describe your challenge. We analyze feasibility, data requirements, and architecture options before writing a single line of code.
Model selection, data pipeline design, proof-of-concept. We validate the approach with real data before committing to full build.
Full development — model training, API integration, infrastructure setup. Continuous delivery with weekly demos and metrics.
Production launch with monitoring, auto-scaling, and ongoing optimization. We don't disappear after launch — we maintain and improve.