iwow supported a construction data company improve its global tech delivery by bringing teams together, modernizing platforms, connecting legacy systems, and introducing AI‑driven development practices.
The global client needed to accelerate their expansion into new markets by efficiently adapting their existing content. The core challenge was not simple translation, but rather intelligently converting content to comply with specific local standards and regulations, such as adapting materials between different countries speaking the same language, with distinct local requirements.
The client required a scalable system capable of delivering high-quality, market-ready content that matched human proficiency, along with a clear understanding of the financial implications of such AI investments.
iwow led an end-to-end project to map AI and automation potential and build a tangible Proof of Concept (PoC). Using agile methodologies to manage the project, we collaborated closely with both end-users and the IT department to design and develop the solution. The work included:
- Utilizing the LEAP-methodology to identify and evaluate suitable AI and automation use cases across the organization, building a roadmap
- Comparing various AI platforms against alternative technical solutions to ensure the best fit
- Designing, developing, and integrating a custom generative AI PoC tailored to the client’s existing systems
A key part of the technical execution involved developing a scalable architecture utilizing Retrieval-Augmented Generation (RAG) and a vector database. RAG bridges the gap between large language models and proprietary data by retrieving relevant information before generating a response. Combined with a modern tech stack, this approach makes it possible to:
- Intelligently adapt existing materials to strictly adhere to the specific local standards and structural requirements of new target markets
- Ground the AI’s content conversion in the client’s specific context and proprietary data to ensure high accuracy
- Perform automated Quality Assurance (QA) checks to verify grammar, consistency, and overall content quality
- Build a robust, high-performance backend using Python and FastAPI
The project concluded with the successful delivery of an AI-based automation solution to the global enterprise. The engagement provided the client with:
- A fully functioning generative AI PoC that automated the conversion of content to meet local market standards, directly enabling faster and more compliant establishment in new countries
- Proven, exceptional output quality, with the automated solution successfully applying complex local standards and passing QA checks at the same level of proficiency as a new specialist employee who has been in training for seven months
- A validated, scalable technical foundation utilizing RAG, vector databases, and FastAPI for future expansions
- A clear, prioritized roadmap with cost-benefit analysis per use case