AI Agents & Data Pipeline for Opportunities
A digital platform aggregates and curates entrepreneurial opportunities across the U.S. and Canada — grants, accelerators, pro bono programs, conferences, and cloud and service credits — and promises non-obvious matches and step-by-step application guidance. That depends on a high-quality, continuously updated opportunity database, but the data comes from unstructured, inconsistent sources (websites, PDFs, forms, announcements), and manual or basic-scraping ingestion leaves it incomplete, inconsistent, and slow to update. Designed and prototyped AI-driven ingestion pipelines and agents to extract, structure, validate, and enrich it.
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Design and prototype AI-driven data ingestion pipelines and agent-based systems that extract, structure, validate, and enrich opportunity data from diverse sources — a scalable, reliable foundation for the platform's recommendation engine that reduces manual curation effort while improving accuracy.
Four phases over seven weeks: discovery and requirements (current sources, ingestion methods, priority attributes, constraints); data model and architecture design (a standardized opportunity schema and a modular flow from ingestion to extraction, validation, and enrichment, with agent roles for extraction, classification, and enrichment); prototype development with weekly demos; and testing, refinement, and handover. Agent-assisted extraction goes beyond simple scraping, using existing AI APIs and tools rather than building models from scratch.
Prototype pipelines and AI agents that ingest opportunity data from websites, PDFs, and structured and semi-structured forms and produce structured output aligned to a standardized schema — covering eligibility, geography, funding type, deadlines, and requirements. Validation and cleaning routines check completeness and consistency, remove duplicates, and flag low-confidence or ambiguous entries; agents classify opportunities by industry, geography, stage, and use case and enrich them with inferred attributes. Delivered with source code, documentation of schemas, processing logic, and architecture, and integration guidance — a foundation that can be extended into production-grade systems.