Selected Work · Document Intelligence
OCR Document Archiving and Search
Archive the document; keep the information usable. A customer needed to retain a large body of documents without turning the archive into a place information went to disappear.
The problem
Long-term retention usually wins over usability. Documents get scanned, named inconsistently, dropped into folders or tape, and functionally lost: safe, durable, and unreachable the day someone actually needs what is inside them.
What We Built
Ixvara built an ingestion and archival workflow: documents flow through OCR, gain metadata and a search index, and land in Wasabi object storage for durable, low-cost retention. Retrieval works the way people think, by searching for what a document says, not guessing what a file was named.
The architecture
Source documents move through a pipeline: ingestion, OCR, metadata and indexing, object storage, and search and retrieval, with an AI layer built in for extraction, correlation, and question-answering.
AI Correlation on a Local LLM
The archive has AI built in, and all of it runs on a locally hosted large language model: document content never leaves the environment for inference.
The model correlates information across thousands of documents, connecting the people, organizations, dates, and subjects that appear in them, so a question can be answered from the archive as a whole instead of one file at a time. A search finds the document that says something; correlation surfaces the documents that belong together.
Running inference locally was an architecture decision, not a limitation: the documents are the sensitive asset, and a local model keeps the entire read path, index, and reasoning inside infrastructure the customer controls.
The result
Unstructured documents became usable digital information. The archive stopped being a liability shelf and became a queryable record, on storage economics that make retaining everything reasonable.
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