Building Parsers and Data Processes for AI Systems
Book a call with FisayoThe problem
Working in a commercial environment a client needed to build a sophisticated parsing technology that could be leveraged via downstream AI services (RAG service) to generate relevant and contextually accurate answers. The issue was they did not know how to leverage technologies to build the necessary service to enable this functionality for the AI product.
The solution
Using my development skills (Python) and my expertise in data engineering coupled with Google Cloud's Document AI technology, I built a custom parsing technology that process the relevant details from document formats - this acted as an ETL (Extract, Transform, Load) service that would ingest unstructured data that would not suffice for precise context for a given user query and building a parser that would generate relevant text segments (tokens) that would enable downstream contextual provisioning for LLM's to improve the quality of the responses, reduce hallucinations and provide points of inference for grounding.
What you can take away from this session
It depends on what you come with - pick one focus or combine them when you book.
- Explore the tool & trade-offs - clear guidance on limits, best-fit use cases and the choices that matter before you commit.
- See it in action - a live walkthrough of a real working setup, prompts and shortcuts.
- Review your setup or solve a blocker - hands-on feedback on what you've built and concrete steps to get unstuck.