Document Processing · Enterprise Knowledge Search
Avila needed to transform fragmented grant documents and opportunity sources into a structured, searchable system that could help public-sector and nonprofit users find, understand, and draft grant applications faster.
Reported impact
Avila reduced grant search and application time by 90%, indexed about 100,000 opportunities, sped development by 10x, and reached product-market fit with paying customers in about four months.
Avila built its grant-intelligence platform on MongoDB Atlas so it could combine structured grant databases, unstructured NOFO PDFs, and user-uploaded planning documents inside one flexible operational store. Atlas acts as the foundational document and file repository, while MongoDB Atlas Search handles lexical retrieval across aggregated grant data and MongoDB Atlas Vector Search provides the semantic matching needed for AI-assisted discovery and RAG. The platform continuously matches user priorities against new funding opportunities, extracts narrative structure, compliance requirements, and required forms from unstructured notices, and then uses OpenAI and Anthropic model calls to help generate proposal drafts. Because Atlas supports dynamic schema changes, Avila can onboard new grant sources and data shapes without slowing development. That lets the startup operate without a dedicated database team while still delivering an end-to-end workflow from search to application writing on a single data foundation.
Grant data from sources such as grants.gov and disparate state sites is stored in MongoDB Atlas, searched lexically with MongoDB Atlas Search and semantically with MongoDB Atlas Vector Search, then combined with user-uploaded planning documents so OpenAI and Anthropic model calls can draft NOFO summaries and proposal narratives.
Avila currently indexes around 100,000 grant opportunities across various sources.
It empowers organizations to find more opportunities and navigate the process 10 times faster.
“Avila uses AI and semantic search to automatically and intelligently match a user’s funding priorities with relevant grants.”
“Avila’s AI also simulates a seasoned grant writer to simplify NOFOs.”
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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