Haink builds custom machine learning systems that run in production — computer vision, signal processing, identity verification, content moderation, recommendation and forecasting. A senior-only team owns the full lifecycle: data pipelines, training, evaluation, MLOps and model governance, sized to the GPU hardware it actually runs on.
Demand, risk and time-series forecasting and classification on your data, with monitoring for drift and clear business metrics.
Detection, classification, OCR and quality inspection for images and video — from edge devices to data-center GPUs.
ML-driven filtering, noise suppression and recognition for audio and sensor signals in demanding real-world conditions.
Document authenticity, face matching, liveness detection and behavioral analysis to cut fraud while raising conversion.
Real-time moderation and recommendation models with precision/recall tuned to your policy and product.
Reproducible training, CI/CD for models, monitoring, retraining pipelines and governance for production scale.
Typical stack:
Production systems delivered by our engineering team. Client names withheld under NDA; sectors shown to indicate context. See full case studies →
Multi-stage pipeline: document authenticity, face matching, liveness detection and behavioral analysis, replacing slow manual review.
Adaptive IIR/FIR filter correction driven by ML for a global automotive manufacturer, keeping voice commands accurate over engine and road noise.
Geospatial ML, genetic-algorithm placement optimization and digital-twin simulation to validate a service-network plan before any office moved.
It depends on the problem. For structured prediction, vision and signal tasks, classical and deep ML are usually more accurate, cheaper and faster than LLMs. We recommend the right tool — and tell you honestly when AI is not needed at all.
It varies by task. We start with a data audit in discovery, and can use transfer learning, augmentation and pre-trained models to work with limited data.
MLOps: reproducible training, automated evaluation, monitoring for data and concept drift, and retraining pipelines — plus governance and audit trails.
Yes. We deploy on private infrastructure and quote right-sized NVIDIA GPU hardware in the same proposal, from workstations to clusters.
Most engagements reach first working results in 2–4 weeks after a discovery and data-audit phase, then iterate to production.
Let's shape a clear plan with milestones, architecture options and an implementation roadmap — with right-sized GPU hardware if AI workloads are involved.