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AI capability without sending your data to anyone

For many operations and compliance workflows, the right answer is a model you run yourself. I deploy open-source LLMs — LLaMA, Mistral, and other proven foundations — on your infrastructure or private cloud, with the selection, fine-tuning, and evaluation work that separates a production system from a weekend experiment.

Sound familiar?

Your data can’t leave

Contracts, health records, financials, or client files make third-party AI APIs a non-starter for policy, regulatory, or contractual reasons.

Per-token pricing scales badly

High-volume internal workloads on metered APIs produce bills that grow with adoption — success gets punished.

Vendor dependency is a risk

Models get deprecated, prices change, terms shift. A workflow your business depends on shouldn’t sit on someone else’s roadmap.

Which model? Nobody can say

Model cards and leaderboards don’t answer the only question that matters: which model handles your documents, your formats, your questions.

How I approach it

1

Size the model to the job

Smaller open-source models handle many document workflows well when properly configured. Candidates are evaluated on your actual tasks, not leaderboard rank.

2

Fine-tune only when measurement says to

LoRA/QLoRA fine-tuning is applied when an evaluation shows the base model falls short — not as a default ritual.

3

Harden it for production

Containerized deployment, access controls, logging, monitoring, and a documented update path — the system survives contact with real users and real ops.

4

Document the economics

You see the hardware, hosting, and maintenance picture up front, including when a managed API is honestly the better fit at your volume.

What you get

  • A production LLM deployment on your infrastructure or private cloud
  • Model selection backed by task-specific evaluation results
  • Fine-tuning (when warranted) with before/after measurements
  • Operational runbook: monitoring, updates, and maintenance procedures your team can own

Backed by published testing

From 82% to 95% on Existing Hardware — Assessing and Improving an On-Premise AI Assistant

An assessment-first engagement took a regulated, on-premise AI assistant from 82.2% to a deployed 94.8% answer accuracy on the same single GPU — adding a zero-critical-error auto-accept capability that handled roughly 1,100 answers in its first production month.

Read the full study

Fit check: self-hosting has real infrastructure costs that depend on volume. During the free discovery I’ll walk through whether the economics favor your own hardware, a private cloud, or a managed option — the published improvement engagement above shows exactly how I frame that analysis, down to when the answer is “no new hardware at all.”

Find out what this would look like for your team

The consultation and initial discovery are free — you get a preliminary recommendation whether or not we work together.

Book a Free Consultation