Nate Nead
CEO, Nead, LLC
Nate Nead is the CEO of Nead, LLC, a specialized AI development and private AI implementation firm. He and his team help organizations design, build and deploy private AI systems, including on-premise and private-cloud language models, retrieval pipelines and AI agents, that keep sensitive data under the client's control. Over more than 15 years, Nate has founded and grown more than a dozen technology and services brands, and has worked with teams at some of the most well-recognized companies in the Fortune 1000.
Closing the Loop with Langfuse, RAG and Expert Feedback
How to wire Langfuse, a RAG pipeline and expert human feedback into a private LLM so it improves on its own data without leaving your network.
How to Build a Secure Enterprise RAG Pipeline on PostgreSQL and pgvector
A concrete reference architecture for building an auditable, row-level-secured RAG pipeline on PostgreSQL and pgvector inside a regulated enterprise.
Fine Tuning Your Open Weights Model for Private LLMs
A practical walkthrough of fine tuning Llama, Qwen, and Mistral inside your own VPC or air gap, covering LoRA, QLoRA, data curation, eval, and rollout.
Securing a Self-Hosted LLM Behind a Firewall
A concrete network and access-control blueprint for locking down a self-hosted LLM behind the enterprise firewall, from egress rules to SIEM and SSO.
How to Rip Out a Public LLM API Contract Without Breaking Production
A step by step plan for replacing an OpenAI or Anthropic contract with a private LLM, including cutover sequencing, prompt parity testing, and rollback.
What Breaks in the First 90 Days of a Private LLM Rollout
A field guide to the failures that hit private LLM deployments in weeks 1 to 12, what each one costs to fix, and which contract clauses prevent them.
Where the Money Actually Goes in a Private AI Deployment
A line-item financial breakdown of private, on-prem LLM deployments, from GPUs and power to RAG pipelines, security audits, and staffing.
What to Put in a Private LLM RFP Before You Sign
The clauses, SLAs, and technical requirements to demand in a private LLM RFP, and which vendor answers should disqualify a bid before procurement.
How to Choose Between Fixed-Scope, Managed Appliance, and Co-Build Contracts for a Private LLM
Compare fixed-scope builds, managed appliance subscriptions, and co-build engagements for a private LLM, with price ranges, risk allocation, and exit terms.
Training an Open Weights Private LLM: Cost vs Benefit Analysis
A line-item cost and benefit analysis of training a private open weights LLM, from GPU spend to fine-tuning payoff, for regulated enterprises.
LLM Appliance vs Self-Built GPU Cluster for Private AI
Compare a turnkey LLM appliance against a self-built GPU cluster on cost, staffing, compliance, and time to production, with a clear decision framework.
Passing an EU AI Act Conformity Assessment on a Self-Hosted LLM
A CISO's checklist for passing an EU AI Act conformity assessment on a private, self-hosted LLM, mapped to logging, oversight, and technical documentation controls.
When Will Private, Open Source LLMs Have Their WordPress Moment?
WordPress revolutionized web publishing by making powerful, open source tools accessible to everyone—from bloggers to enterprises. Today, private, open source LLMs are following a similar trajectory. This post explores how the commoditization of model weights, rising demand for AI privacy, modular deployment stacks, and falling hardware costs are setting the stage for a “WordPress moment” in AI. From Raspberry Pi-scale devices to enterprise-grade LLM stacks, we’re approaching a future where every company—not just big tech—can deploy and control its own intelligent systems.
The Hidden Risks of Public AI APIs—and How Private LLMs Solve Them
Public AI APIs like OpenAI and Anthropic offer convenience and powerful capabilities, but they come with hidden risks—data privacy concerns, vendor lock-in, compliance challenges, and unpredictable costs. This post explores why enterprises should be cautious when relying on public APIs and outlines how private LLM deployments offer a secure, customizable, and compliant alternative. By hosting models in your own infrastructure, you gain full control over your data, reduce regulatory exposure, and avoid the limitations of third-party providers.
Is It Really a Knockout Blow for LLMs? Or Just a Glancing Hit?
LLMs flounder when they face tasks that step outside the patterns they've seen in training.
HIPAA, GDPR, & Private LLMs: Meeting AI Compliance Standards
Ensure AI compliance with HIPAA, GDPR, and global privacy laws by building private LLMs with secure data handling, consent, and governance controls.
LLMs Behind Closed Doors: Building Secure, In-House AI Models
Build secure, in-house LLMs to protect sensitive data, ensure compliance, reduce latency, and gain full control over your AI infrastructure and operations.