Everything you need to know about FDE.
Guides on Forward Deployed Engineering, AI deployment, production systems, and how the embedded model works.
FDE Fundamentals
FDE as a Service: The Agency Model for Forward Deployed Engineering
FDE as a Service provides embedded AI engineering capacity on a per-project basis. No full-time hire, no recruiting competition, no permanent headcount. Here's how the model works, what it costs, and who benefits most.
Forward Deployed Engineer vs. Consultant: What's the Difference?
FDEs and consultants both work with external companies, but the model, accountability, and outputs are fundamentally different. Here's the full comparison across every dimension that matters for AI deployment.
Forward Deployed Engineering Agency: The Model Explained
A Forward Deployed Engineering agency provides embedded AI engineering capacity on a project basis — the FDE model without the full-time hire. Here's how the agency model works, what it costs, and who benefits most.
How to Hire an Embedded AI Engineer (Without Competing with OpenAI)
Hiring a senior embedded AI engineer means competing with OpenAI, Anthropic, and Databricks for the same candidates. Here's how to win that competition — or sidestep it entirely with the FDE agency model.
What Is a Forward Deployed Engineer?
A Forward Deployed Engineer (FDE) is a senior software engineer who embeds inside a client company to own AI system delivery end-to-end. Learn the role, responsibilities, and why demand has surged 800% since 2024.
What Is an Embedded AI Engineer and How Do They Work?
An embedded AI engineer works inside your organization — your Slack, your repo, your sprints — and owns AI system delivery end-to-end. Here's how the model works, what it costs, and when it beats hiring.
What Is FDE-Agent? AI Agent Deployment by Embedded Engineers
FDE-Agent is fdeai.agency's AI agent deployment specialty — embedding senior engineers to build multi-agent systems, tool-use pipelines, and agent orchestration frameworks in production. Here's what it covers, how long it takes, and what it costs.
AI Deployment
Enterprise AI Deployment Challenges: Why It's Hard and What to Do About It
Enterprise AI deployment is harder than it looks. Security reviews, data governance, legacy system integration, and organizational resistance are the real blockers — not the AI itself. Here's how to navigate them.
Enterprise LLM Deployment: Architecture, Compliance, and Scale
Deploying LLMs in enterprise environments requires navigating security reviews, data governance, legacy integration, and compliance requirements that cloud demos don't surface. Here's the complete enterprise LLM deployment guide.
How to Take an AI Pilot to Production
Most AI pilots never reach production. Here's the engineering and organizational roadmap for taking an AI proof-of-concept to a live, maintained production system — and the most common failure points along the way.
What Makes a Production AI System: Requirements, Architecture, and Operations
A production AI system is fundamentally different from a demo or POC. Here are the engineering requirements, architectural decisions, and operational processes that separate AI systems that work in production from those that work only in demos.
Why AI Projects Fail: The Real Reasons (Not the Obvious Ones)
Over 73% of enterprise AI projects never reach production. These are the actual root causes — ownership gaps, undefined success criteria, integration underestimates — and what to do differently.
Services
AI Agent Deployment Services: What to Look For
AI agent deployment services vary enormously in scope, accountability, and output. Here's how to evaluate providers, what production-grade agent deployment actually requires, and the questions to ask before signing.
AI Agent Development Company: What to Look For in 2026
The AI agent development market is crowded with vendors who build demos, not production systems. Here's how to evaluate AI agent development companies, what production-grade delivery looks like, and the red flags to avoid.
AI Engineering Team Augmentation: When and How to Do It
AI engineering team augmentation fills the gap between what your current team can deliver and what your AI roadmap requires. Here's when it makes sense, how to structure it, and how to avoid the common failure modes.
LLM Evaluation Frameworks: How to Test AI Systems Before You Ship
LLM evaluation is the difference between shipping a system you understand and shipping one you're guessing about. Here's how to build an evaluation framework for production AI systems — from test set construction to automated scoring.
ML Infrastructure Consulting: What It Covers and When You Need It
ML infrastructure consulting covers the systems that AI models run on: training pipelines, feature stores, model serving, monitoring, and data pipelines. Here's when you need it, what it costs, and what a good engagement delivers.
On-Premises LLM Deployment: A Complete Technical Guide
On-premises LLM deployment runs open-weight language models on your own hardware, inside your network. Here's the technical architecture, hardware requirements, model selection, and operational considerations for air-gapped and sovereign AI deployments.
RAG Pipeline Development: Architecture, Costs, and Best Practices
Retrieval-Augmented Generation (RAG) is the dominant pattern for enterprise AI. This guide covers RAG pipeline architecture, common failure modes, cost optimization, and what production-grade RAG actually requires.
Comparison
AI Deployment Agency: What to Look For and How to Evaluate
AI deployment agencies vary enormously in what they actually deliver. Here's how to evaluate them, what separates execution-focused agencies from strategy-only firms, and what a good AI deployment engagement looks like end-to-end.
AI Deployment Consulting: Strategy vs. Execution
AI deployment consulting covers both strategic and technical layers of getting AI systems into production. Here's the difference between strategy-focused and execution-focused consulting — and which one your project actually needs.
OpenAI Forward Deployed Engineer: Roles, Pay, and the Deployment Company
OpenAI's Forward Deployed Engineer role and the OpenAI Deployment Company represent the frontier of enterprise AI delivery. Here's how OpenAI's FDE model works, what it pays, and how the Deployment Company changes the market.
Palantir's Forward Deployed Engineer Model: How It Works
Palantir invented the Forward Deployed Engineer model and built a $50B+ company on it. Here's how Palantir's FDE model works, how it's evolved, and what other companies have learned from it.