I design practical AI solutions for enterprise environments, connecting architecture, product thinking, and delivery.
My focus is on turning AI opportunities into usable systems — from semantic retrieval and document intelligence to internal tooling, estimation support, and user-facing product improvements.
I work across solution design, use-case shaping, architecture decisions, local versus cloud deployment models, stakeholder alignment, and practical implementation paths, with strong attention to security, sensitive data, and enterprise adoption.
This combines AI architecture with product thinking: not just building technology, but shaping solutions that solve real operational problems and can be deployed, maintained, and used in practice.
I design practical AI and product improvement concepts in an enterprise context, including document retrieval, estimation support, portfolio ideas, and decision-support thinking for existing solutions. The focus is on solutions that are usable in real organizations, not just technically interesting prototypes.
Enterprise AI & product initiatives
Designed and built a full AI knowledge base as a Python application using local semantic retrieval and modern RAG principles, intended for organizations that cannot rely on public cloud services or need stronger control over sensitive data. The solution supports semantic search, document enhancement, and practical knowledge access in secure environments. It was applied to CRIF product portfolio use cases and also to budget-rule navigation scenarios for the Ministry of Finance.
Internal innovation initiative focused on improving how enterprise and SaaS solutions are estimated, scoped, and turned into technical proposal inputs. The goal is to make estimation faster, more structured, and less dependent on fragmented individual know-how.
AI-assisted workflows, semantic retrieval, document intelligence, estimation support, and practical internal tools shaped around real operational use.
Solution design across local and cloud-ready approaches, sensitive data constraints, integration thinking, and practical deployment models for enterprise environments.
I connect business needs, AI capabilities, product thinking, and delivery constraints to shape solutions that are useful, deployable, and maintainable.
AI solution design, product-oriented architecture, semantic search and RAG thinking, structured execution, and translating complexity into usable systems.
If you are building or improving enterprise AI solutions, product concepts, retrieval systems, or decision-support workflows, feel free to get in touch.