Context
Fluvora explores how a business can use its knowledge to serve customers better.
Problem
Commercial conversations need context: relevant information, business rules, and continuity across the team’s tools.
Constraints
Assistance needs to fit each operation, respect business rules, and include controls. The product is still in pilot validation.
Hypothesis
Retrieving relevant knowledge before generating a response could make customer interactions more useful and consistent.
Solution
I am developing a platform that combines knowledge bases, retrieval-augmented generation (RAG), language models, and messaging integrations to assist commercial conversations.
Architecture
Messaging channels connect to knowledge retrieval and language-model response generation. Embeddings and semantic search retrieve context; business rules and response evaluation are part of the system’s control work.
Process
Development includes knowledge preparation, channel integration, response evaluation, and adapting assistance to each business. My role spans founding, architecture, backend, AI engineering, and integrations.
Results and validation
Fluvora is a SaaS in pilot validation. Evaluation considers response quality, knowledge retrieval, latency, and how assistance adapts to each business’s rules.
Lessons
A useful response depends on available knowledge, operational rules, and how the interaction is evaluated. The objective is to support human service with faster, more useful, and more consistent tools.
Next steps
Continue validating the product, expand response evaluation, and document technical decisions in the context of each pilot.