Government
On-Prem vs. Cloud AI Deployment
Choosing between on-premises, cloud, and hybrid AI deployment affects security, costs, and capabilities. A practical framework for making this critical infrastructure decision.
Author
Michael Goldman
Sprinklenet Research contributor focused on retrieval quality, model behavior, prompt risk, and audit controls for enterprise AI systems.
Document Intelligence for Enterprise Teams
Eighty percent of enterprise data is unstructured.
Government
Retrieval Pipeline Architecture for Enterprise AI Integration
A production retrieval pipeline needs more than embeddings. Enterprise AI integration requires ingestion, metadata, permissions, evaluation, orchestration, and governance.
Government
AI Compliance and Data Governance
AI compliance extends beyond traditional data governance into model transparency, bias monitoring, and algorithmic accountability.
AI Security in Practice for Multi-Model Systems
A practical look at AI security for multi-model systems, including data exposure, prompt injection, permission-aware retrieval, auditability, and defense in depth.
Government
Enterprise AI Security for Sensitive Data
Enterprise AI introduces novel security challenges from prompt injection to data leakage. Learn how to build AI systems that protect sensitive data while enabling.
Government
Federal AI Policy: What Contractors Need to Know
Federal AI policy affects how contractors scope, document, govern, and deliver systems; strong firms connect requirements to production controls.
Government
AI Subcontractor Support for Prime Contractors
Prime contractors can use specialized small AI firms for AI integration, retrieval systems, LLM orchestration, governance, proposal support, and rapid implementation.
Vector Databases Explained: A Builder’s Guide
A builder’s guide to vector databases, embeddings, metadata, filtering, retrieval quality, and choosing the right architecture for retrieval systems.
Government
Five Lessons From Defense AI Programs That Apply to Every Enterprise
Lessons from DoW AI programs that apply to enterprise teams building secure, governed, mission-ready AI systems.
Retrieval System Development for Enterprise Knowledge
Retrieval system development connects approved knowledge to AI workflows with citations, access control, retrieval quality, evaluation, and governance.
LLM Orchestration for Multi-Model Enterprise AI
LLM orchestration helps enterprise AI systems route tasks across models, tools, retrieval layers, guardrails, and human review paths for reliable production use.
Sprinklenet AI Newsletter
Most weeks I send one short read on enterprise AI: what’s working in real deployments, what’s not, and what I’d do in your seat.
Jamie Thompson, Founder and CEO