AI RAG Proof of Concept
iwow supported their customer to accelerate global market expansion by delivering a generative AI proof of concept, building a RAG-based automation solution that intelligently adapted content to local market standards, matching the proficiency of a trained specialist.
The PoC matched the output quality of a specialist with seven months of training, passing all automated QA checks.
Vector database and FastAPI foundation validated and ready for future market expansions beyond the initial proof of concept.
Delivered a clear use-case roadmap with cost-benefit analysis per initiative, enabling confident investment decisions.
The global client needed to accelerate expansion into new markets by efficiently adapting existing content to local standards and regulations — not simple translation, but intelligent conversion complying with distinct local requirements. They needed a scalable system and a clear understanding of the financial implications of AI investment.
iwow led an end-to-end project using agile methodology, closely collaborating with end-users and IT. Using the LEAP methodology to identify and evaluate AI use cases, the team compared AI platforms, then designed and developed a custom generative AI PoC. The technical solution used Retrieval-Augmented Generation (RAG) and a vector database to ground AI output in the client's proprietary data, with automated QA checks and a Python/FastAPI backend.
- Fully functioning generative AI PoC automating content conversion to local market standards
- Output quality matching a trained specialist (7 months experience), passing all QA checks
- Validated scalable architecture using RAG, vector databases, and FastAPI
- Clear prioritized roadmap with cost-benefit analysis per use case
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