Production AI across document intelligence, agentic RAG, and cloud infrastructure — measurable outcomes, not slide decks.
Automate document intake from scanned images to structured data, without weekly model retraining. Production pipelines that handle messy layouts, handwriting, and multi-page forms at scale.
Replace hours of manual research with natural-language interfaces grounded in your data. Chatbots and agentic workflows that query, analyze, and answer complex multi-step questions from a single conversation.
Take AI from prototype to production with CI/CD, containerized deployments on AWS/Kubernetes, and self-hosted LLM serving, so your models ship reliably and stay running.
Custom vision models for detection, localization, and classification on real-world imagery, from handwriting regions on forms to real-time object detection in video streams.
Run open-weight models on your own hardware for lower cost, lower latency, and full data privacy. Tuned serving with KV cache and prefix caching on dedicated GPUs.
Compare cloud APIs and open-source models on your actual workload before committing. Data-driven picks on price, accuracy, and latency so you do not overpay for the wrong model.
Technical leadership for GenAI teams: product direction, sprint delivery, and the architecture decisions that keep AI initiatives shipping instead of stalling in POC limbo.
From first call to production — tuned for AI where data and deployment matter as much as the model.
Map your data, constraints, and success metrics before writing code.
Iterate in tight loops with working prototypes you can evaluate early.
Deploy to production with monitoring, CI/CD, and handoff documentation.
Open source, ML reading, travel, and gaming.
See case studies in production or get in touch to discuss your project.