10x the Speed, 100% of the Accuracy: Unstructured Data at Scale
Phase2 built Resume Sizzler using multi-modal AI to parse unstructured documents with near-human accuracy—replacing years of engineering effort with weeks of development while delivering 2-10x faster cycles and enterprise-scale quality.
Unlike rigid OCR or keyword matching that breaks under complexity, this multi-modal AI uses a council-of-agents architecture to understand context and meaning like a trained analyst—cheaper and more reliable than traditional OCR or even human data entry.
The Challenge
Organizations drown in unstructured documents like resumes, contracts, claims, and reports where critical insights sit locked in formats humans can read but systems can't use at scale. Tasks that once required years of engineering effort and large teams now bottleneck business operations.
Phase2_Approach
We built Resume Sizzler using LangGraph and multi-modal AI with a council-of-agents architecture that parses any document with near-human accuracy, matches context across large text blocks, transforms natural language into structured forms, and enables dynamic conversations through voice, text, or chat interfaces.
Phase2_Result
What previously took years of engineering or large specialized teams now happens in weeks with better accuracy. Document parsing that beats traditional OCR, context matching that works at scale, automated form filling from unstructured sources, and multi-language support that opens global access.