Proof in the flow
Foundry is built for repeatable work where sensitive data, staff time, and approval discipline matter. Each example follows the same shape: work arrives, Foundry processes locally, a person reviews, and the business keeps control.
These examples show where private local AI makes commercial sense: document-heavy operations, legal intake, client support, internal knowledge, and code review. The numbers are strongest when the work is frequent, structured enough to check, and currently slowed down by manual reading, chasing, or re-keying.
Case 1 — Document processing pipeline
A 30-person operations team used Foundry to read, classify, extract, check, and queue invoices and business documents for review — without sending financial documents to a cloud AI provider.
Case 2 — Conveyancing intake desk
A small conveyancing firm used Foundry to check matter packs, identify missing evidence, flag incomplete forms, and draft chase messages for approval.
Case 3 — Client support desk
A SaaS company used Foundry to draft replies for routine tickets, route bug reports, and summarise complex cases without sending customer support content through a cloud AI tool.
Case 4 — Internal knowledge search
A professional-services firm used Foundry to search internal documents locally and answer questions with citations to source files.
Case 5 — Code review pipeline
A software team used Foundry to review pull requests for security, tests, edge cases, and consistency before human approval.
Case metrics are example/source workflow evidence, not guaranteed outcomes. Your results depend on volume, tools, data quality, and approval process.
The best first Foundry project is usually narrow: one document type, one support queue, one matter checklist, one knowledge base, or one code-review path.
We will tell you if the workflow is too low-volume, too messy, too risky, or better left in the cloud.