AI / ML platforms
End-to-end machine learning — from training pipelines to production inference and applied AI.
Architecture
- Kubeflow — ML pipelines, experiment tracking, hyperparameter tuning, and model registry on Kubernetes.
- Inference serving — Model deployment with autoscaling, GPU acceleration, and low-latency endpoints.
- RAGflow — Retrieval-augmented generation pipelines: document ingestion, embedding, vector search, and LLM generation.
- LLM integration — OpenAI, Anthropic, Ollama, Groq, and self-hosted models. Multi-provider routing and fallback.
- AI agents — Custom autonomous agents for workflow automation, data analysis, and decision support.
What we deliver
- ML pipeline design and deployment — data ingestion, feature engineering, training, evaluation, and model promotion.
- Inference infrastructure — GPU-backed serving, batching, caching, and monitoring for production workloads.
- RAG systems — document intelligence, knowledge bases, and conversational AI with data residency compliance.
- AI agent development — task-specific agents, tool-use patterns, multi-step reasoning, and integration with existing systems.
- MLOps — model versioning, A/B testing, drift detection, and automated retraining.
Applications
Document intelligence, conversational AI, decision support, predictive analytics, anomaly detection, automation, and any domain where ML needs to run reliably at scale. Our pipelines often run on HPC infrastructure for training at scale, and we offer consulting for custom deployments.
See Projects for examples, or get in touch to discuss your requirements.