Building Local LLM Workflows Without External APIs

Not every enterprise can send data to a third-party model. Government, HR and healthcare workloads often have strict data-residency requirements. That doesn't mean you have to give up AI — it means you run it in-house.

Why local LLMs

• Data never leaves your environment.
• Predictable cost — no per-token bill that scales with usage.
• Control — you choose the model, the version, and the guarantees.

The pattern

1. Serve a model locally (e.g. via Ollama) behind a small internal API.
2. Keep prompts and grounding data inside your VPC.
3. Use the LLM for the bounded tasks it's good at — parsing, extraction, drafting — and keep humans in the loop for the rest.

Where I've used it

Resume parsing in TalentPike, grounded outreach drafting in the marketing platform, and the no-external-LLM assistant concept for PeopleSoft all run this way.

This is a placeholder draft — the full article is coming soon.