Phase 1: Unified Embedding Space

The Problem

Organizational knowledge is siloed across divisions. When Fundraising searches for a specific ongoing investment or need in the field that a funder is interested in scaling, they can't discover relevant Field Operations data without manual coordination. Generic off-the-shelf embeddings don't understand organizational domain language or cross-divisional relationships, limiting search to exact keyword matches rather than semantic understanding.

The Solution

Fine-tune custom embedding and reranker models on cross-unit data, creating shared semantic infrastructure that understands organizational relationships. Unlike generic embeddings, these models learn the organization's domain language and cross-divisional patterns. When Fundraising searches for scaling opportunities matching a funder's interests, they automatically surface relevant Field Operations project data through improved backend search, not through new processes or coordination.

The Value

Cross-divisional discovery through existing workflows. No process changes required, immediate improvement in how people find and use knowledge. Divisions maintain complete autonomy over their data while gaining cross-organizational visibility. Training costs under $1 using consumer GPUs with immediate ROI through enhanced data access. Hosting the vector database incurs a small ongoing cost, but this can reduce overall expenses by handling semantic queries through lightweight vectors rather than repeated calls to the source databases. Fine-tuned embedding model trained on cross-unit data, fine-tuned reranker model for precision ranking, ChromaDB vector database with ingested embeddings, and search interface demonstrating enhanced cross-division semantic search within existing workflows.