Foundry
Coming soon. LLM Foundry is being prepared for early Fit Reviews. To register interest, email info@llm-foundry.com.

Proof in the flow

Five small stories. One repeated pattern.

Foundry is built for repeatable work where sensitive data, staff time, review discipline, and operational control matter. Each example follows the same shape: work arrives, Foundry processes locally, the source remains visible, a person reviews, and the business keeps control.

Book a Foundry Fit Review

The right test is not “can AI answer?” It is “can this workflow improve safely?”

These examples show where private 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, re-keying, or first-pass review.

These are source case-study examples and modelled workflows, not guaranteed outcomes. The Fit Review checks your volume, tools, data quality, hardware, approval process, and risk constraints.

Case 1 — Document processing pipeline

From manual document handling to a review queue your team can check.

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 for the agreed job.

Read the document-processing story

Case 2 — Conveyancing intake desk

Matter readiness without three weeks of document chasing.

A small conveyancing firm used Foundry to check matter packs, identify missing evidence, flag incomplete forms, and draft chase messages for approval.

Read the conveyancing intake story

Case 3 — Client support desk

Routine support drafted locally, complex support escalated faster.

A SaaS company used Foundry to draft replies for routine tickets, route bug reports, and summarise complex cases without sending agreed customer-support content through a cloud AI tool.

Read the client-support story

Case 4 — Internal knowledge search

The firm’s own precedents, searchable in plain English.

A professional-services firm used Foundry to search selected internal documents locally and answer questions with citations to source files.

Read the knowledge-search story

Case 5 — Code review pipeline

A first pass before senior review.

A software team used Foundry to review pull requests for security, tests, edge cases, and consistency before human approval.

Read the code-review story

Examples, not promised results. Your results depend on volume, tools, data quality, hardware, AI tools, and approval process.

What every case has in common.

  • The workflow already exists: email, folders, case files, helpdesk, shared drives, GitHub.
  • The data is sensitive enough that general cloud AI creates a concern.
  • The task is repeatable enough for a first pass to help.
  • The source material remains available for checking.
  • Foundry uses the right tool for each part of the job.
  • The business still wants human approval before anything important happens.
  • Managers can see where the work is up to, what needs checking, and what happened next.

Bring one workflow. We will test the fit.

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.

Book a Foundry Fit Review

We will tell you if the workflow is too low-volume, too messy, too risky, too dependent on cloud systems, or better handled another way.