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

What it changes

Less time lost to repeat work. Less anxiety about AI control.

Foundry is most useful when sensitive work keeps repeating: documents to check, tickets to triage, files to search, replies to draft, or code to review.

Book a Foundry Fit Review

Five practical changes.

Staff time

Foundry handles the first pass so people start from a draft, a summary, a flagged exception, or a prepared review queue.

Evidence examples
  • Document review time reduced to 20–30 seconds per document in the source workflow.
  • Support routine response time reduced to 12–15 minutes in the source workflow.
  • Internal knowledge answers begin in 30 seconds–3 minutes in the source workflow.

Surprise AI bills

For work moved into Foundry, the business is not paying a cloud AI provider every time that specific job runs.

Evidence examples
  • Document-processing example avoided £21,600/year in OpenAI API costs.
  • Support example avoided £5,400/year in Intercom AI/OpenAI-style costs.
  • Knowledge-search example avoided £7,200–10,800/year in cloud AI usage.
  • Code-review example avoided £9,600–14,400/year in OpenAI API costs.

Easier checking

The source, draft, flags, and approval step stay together, so managers can see what happened.

  • Source material connected to outputs where possible.
  • Review queues show what needs attention.
  • Important outputs stop for a person to check.

Fewer supplier surprises

For suitable work moved local, the process is less exposed to one provider’s price changes, rate limits, outages, model changes, or account decisions.

  • Less reliance on one outside AI account.
  • More of the work runs on equipment you control.
  • Useful when service continuity matters.

Better fit for the job

Foundry can use simple tools for simple steps, document tools for document work, and stronger AI only where the job needs it.

  • Fast sorting for incoming work.
  • Specialist tools for scanned pages and fields.
  • Careful drafting support where a person will review the result.

Example workflow outcomes from the source cases.

WorkflowBeforeAfterCommercial meaning
Document processing8–13 minutes per document20–30 seconds reviewAdmin time moves from reading and re-keying to checking exceptions
Conveyancing intake15–20 admin hours/week chasing documents4–6 hours/weekFee earners see what is missing earlier
Client support4–6 hour routine response12–15 minutesRoutine tickets stop blocking urgent cases
Internal knowledge25–45 minutes searching30 seconds–3 minutesStaff find firm knowledge faster and with source references
Code review3–5 hour review wait15–25 minutesSenior engineers review a prepared first pass

Use as illustrative case-study evidence, not as guaranteed results. Results depend on volume, tooling, data quality, approval process, hardware, and the tools used for the job.

More of the process stays in your hands.

A cloud AI service can be useful, but it is still someone else’s service. The terms, prices, limits, models, and availability can change.

Foundry helps bring suitable work into an environment your business controls. That does not remove every dependency, but it gives you more control over the process, the record, and the review steps.

The Fit Review compares your staff time, cloud AI/API spend, data sensitivity, record needs, supplier concerns, hardware position, and approval requirements before recommending a path.

Good fit signals.

  • You process many similar documents, tickets, queries, matters, or code changes.
  • The data is sensitive enough that public AI tools create a real concern.
  • You need to see source material, queue state, review steps, and approvals.
  • You are already paying meaningful AI bills.
  • Your work has different kinds of steps: sorting, extraction, search, drafting, review, or exception handling.
  • You want predictable local operation rather than sending every task to one large general model.
  • The workflow can tolerate seconds, not milliseconds.
  • You want drafts and review queues, not unsupervised automation.
  • You can accommodate Apple hardware on-site.

We will say no when the numbers or risk profile do not work.

  • Your volume is low and manual handling is already manageable.
  • You are comfortable sending the data to outside AI providers.
  • You need massive concurrency or sub-second public-user responses.
  • You want image or video generation rather than document, text, support, knowledge, or code workflows.
  • You want AI to make final legal, financial, HR, medical, compliance, or operational decisions without human review.
  • Your compliance or security requirements need a longer procurement, legal, or IT review before any implementation.
  • Your workflow is too unstructured to review safely without first cleaning the process.

Book a Foundry Fit Review

A useful no is better than an expensive AI project.