Nate Castillo

Let's Build

Nate Castillo

Let's Build

Forward Deployed Engineer

Compound with AI

Compound with AI

A builder who sells and a seller who builds. I close the gap between AI pilots that impress in the demo and systems that survive production. From architecture, adoption, and the business case that funds both.

A builder who sells and a seller who builds. I close the gap between AI pilots that impress in the demo and systems that survive production. From architecture, adoption, and the business case that funds both.

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Start the Conversation

About

Builder. Operator. Seller.

Builder. Operator. Seller.

Forward Deployed Engineer (FDE) for AI solutions

Forward Deployed Engineer (FDE) for AI solutions

Forward Deployed Engineer (FDE) for AI solutions

Nate Castillo

Los Angeles, CA

FDE

user pic

Background

I've built AI products from zero as a VC-backed founder, scaled solutions into the Fortune 500, and operated the revenue engine in between. That means I've lived on both sides of the gap that kills most enterprise AI: engineering teams that ship capability nobody adopts, and business teams that buy outcomes nobody can deliver.

As an FDE, I embed with your team on the frontier of your industry. I sit with business leadership to run discovery, translate the problem into an architecture, then prototype alongside engineering — rapidly — so the distance from executive whiteboard to working system is measured in weeks, not quarters. I'm bilingual in business and bits: comfortable in the boardroom defending a TCO model, and comfortable in the terminal shipping the thing the model justified.

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Start the Conversation

PROBLEM

AI makes building easier. Reaching production is still brutal.

AI makes building easier. Reaching production is still brutal.

Most enterprise AI initiatives don't die because the model didn't work. They die in pilot purgatory with a demo that impressed everyone, an architecture that couldn't survive real data, security review, or the CFO's spreadsheet. AI has made prototyping cheap, but the gap between a working prototype and a production system with an owner, a budget line, and a measurable return is as wide as ever. Momentum stalls, the champion moves on, and the project quietly dies. That's the gap I'm built to close.

Most enterprise AI initiatives don't die because the model didn't work. They die in pilot purgatory with a demo that impressed everyone, an architecture that couldn't survive real data, security review, or the CFO's spreadsheet. AI has made prototyping cheap, but the gap between a working prototype and a production system with an owner, a budget line, and a measurable return is as wide as ever. Momentum stalls, the champion moves on, and the project quietly dies. That's the gap I'm built to close.

SOLUTION

Forward Deployed Engineering aligned to production outcomes

Forward Deployed Engineering aligned to production outcomes

Close the gap between the pilot that impressed and the system that ships.

Close the gap between the pilot that impressed and the system that ships.

Embedded Discovery

I sit with your business leadership and your engineers in the same week.

Embedded Discovery

I sit with your business leadership and your engineers in the same week.

Rapid Prototype → Production

Models, agents, and integrations built on your stack and your data, hardened for security.

Rapid Prototype → Production

Models, agents, and integrations built on your stack and your data, hardened for security.

The Economic Engine

The system that can prove its own ROI is the system that keeps its budget.

The Economic Engine

The system that can prove its own ROI is the system that keeps its budget.

CAPABILITIES

Translate AI capability into compounding outcomes

Translate AI capability into compounding outcomes

From executive discovery to agents running in production

From executive discovery to agents running in production

Research anything...

Research

Software & App Industry

UX & UI Design Industry

High Converting Customer

AI Opportunity Mapping

Structured discovery across your org: where the workflows are, where the data lives, what's feasible now vs. next. A ranked build roadmap scored by ROI — before a line of code is written.

Research anything...

Research

Software & App Industry

UX & UI Design Industry

High Converting Customer

AI Opportunity Mapping

Structured discovery across your org: where the workflows are, where the data lives, what's feasible now vs. next. A ranked build roadmap scored by ROI — before a line of code is written.

Research anything...

Research

Software & App Industry

UX & UI Design Industry

High Converting Customer

AI Opportunity Mapping

Structured discovery across your org: where the workflows are, where the data lives, what's feasible now vs. next. A ranked build roadmap scored by ROI — before a line of code is written.

Code

1

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3

4

5

class AutomationAgent:
def __init__(self, activation_limit):
self.activation_limit = activation_limit
self.current_mode = "idle"

def evaluate_task(self, workload_value):
if workload_value > self.activation_limit:
self.current_mode = "engaged"
return "Automation agent has been successfully activated!"
else:
return "No activation needed. Agent stays idle."
def get_current_mode(self):
return f"Current operational mode: {self.current_mode}"

Rapid Agent Development

Integrate agents with your tools and automate the workflows your team keeps doing manually.

Code

1

2

3

4

5

class AutomationAgent:
def __init__(self, activation_limit):
self.activation_limit = activation_limit
self.current_mode = "idle"

def evaluate_task(self, workload_value):
if workload_value > self.activation_limit:
self.current_mode = "engaged"
return "Automation agent has been successfully activated!"
else:
return "No activation needed. Agent stays idle."
def get_current_mode(self):
return f"Current operational mode: {self.current_mode}"

Rapid Agent Development

Integrate agents with your tools and automate the workflows your team keeps doing manually.

Code

1

2

3

4

5

class AutomationAgent:
def __init__(self, activation_limit):
self.activation_limit = activation_limit
self.current_mode = "idle"

def evaluate_task(self, workload_value):
if workload_value > self.activation_limit:
self.current_mode = "engaged"
return "Automation agent has been successfully activated!"
else:
return "No activation needed. Agent stays idle."
def get_current_mode(self):
return f"Current operational mode: {self.current_mode}"

Rapid Agent Development

Integrate agents with your tools and automate the workflows your team keeps doing manually.

Production AI & Economics

FinOps for AI workloads. Know your cost per token, per query, per outcome — and design the system so those numbers compound in your favor.

Production AI & Economics

FinOps for AI workloads. Know your cost per token, per query, per outcome — and design the system so those numbers compound in your favor.

Production AI & Economics

FinOps for AI workloads. Know your cost per token, per query, per outcome — and design the system so those numbers compound in your favor.

PROCESS

Three steps to an AI system that compounds

Three steps to an AI system that compounds

Locked in process. Outcome oriented execution. No vague decks or bloated retainers.

Locked in process. Outcome oriented execution. No vague decks or bloated retainers.

STEP 1

STEP 2

STEP 3

03

Production & Compound

The compound stage. We harden the system for production — security, observability, cost instrumentation — and hand your team the keys with the runbooks to operate it. Engagements are structured around measurable outcome milestones, not billables. We win together.

STEP 1

STEP 2

STEP 3

03

Production & Compound

The compound stage. We harden the system for production — security, observability, cost instrumentation — and hand your team the keys with the runbooks to operate it. Engagements are structured around measurable outcome milestones, not billables. We win together.

PROOF OF WORK

Results that compound

Results that compound

Different stages, different problems. Same outcome: AI capability turned into production systems and measurable value.

Different stages, different problems. Same outcome: AI capability turned into production systems and measurable value.

TCO & Tokenomics Analyzer

Built an interactive TCO analyzer that models AI workload economics end-to-end — infrastructure, inference, and token spend. Used it to design a cost architecture combining open-source models, model tiering, and operational data hygiene, driving token costs toward zero. The output wasn't a spreadsheet; it was a decision engine finance could interrogate.

0

%

TCO Reduction

GTM-Audit-Agent

Designed and shipped an agent that compresses manual go-to-market analysis from hours to minutes per report. It audits a company against 8 GTM patterns and surfaces the highest-leverage AI agent use cases — giving RevOps and product teams a ranked build roadmap instead of a consulting engagement.

0

%

Time Savings per report

Production Inference Stack, 89% Cheaper

Architected and deployed an open-source inference platform — Langflow and n8n agent workflows routed through LiteLLM, orchestrated on Kubernetes — replacing closed-model API spend with a self-hosted engine. Not a cost estimate; a running system with the receipts.

0

%

Reduction in cost per 1M tokens

TCO & Tokenomics Analyzer

Built an interactive TCO analyzer that models AI workload economics end-to-end — infrastructure, inference, and token spend. Used it to design a cost architecture combining open-source models, model tiering, and operational data hygiene, driving token costs toward zero. The output wasn't a spreadsheet; it was a decision engine finance could interrogate.

0

%

TCO Reduction

GTM-Audit-Agent

Designed and shipped an agent that compresses manual go-to-market analysis from hours to minutes per report. It audits a company against 8 GTM patterns and surfaces the highest-leverage AI agent use cases — giving RevOps and product teams a ranked build roadmap instead of a consulting engagement.

0

%

Time Savings per report

Production Inference Stack, 89% Cheaper

Architected and deployed an open-source inference platform — Langflow and n8n agent workflows routed through LiteLLM, orchestrated on Kubernetes — replacing closed-model API spend with a self-hosted engine. Not a cost estimate; a running system with the receipts.

0

%

Reduction in cost per 1M tokens

TCO & Tokenomics Analyzer

Built an interactive TCO analyzer that models AI workload economics end-to-end — infrastructure, inference, and token spend. Used it to design a cost architecture combining open-source models, model tiering, and operational data hygiene, driving token costs toward zero. The output wasn't a spreadsheet; it was a decision engine finance could interrogate.

0

%

TCO Reduction

GTM-Audit-Agent

Designed and shipped an agent that compresses manual go-to-market analysis from hours to minutes per report. It audits a company against 8 GTM patterns and surfaces the highest-leverage AI agent use cases — giving RevOps and product teams a ranked build roadmap instead of a consulting engagement.

0

%

Time Savings per report

Production Inference Stack, 89% Cheaper

Architected and deployed an open-source inference platform — Langflow and n8n agent workflows routed through LiteLLM, orchestrated on Kubernetes — replacing closed-model API spend with a self-hosted engine. Not a cost estimate; a running system with the receipts.

0

%

reduction in cost per 1M tokens

Let's Build!

Are you ready to start compounding with AI?

Start the Conversation

© 2026 Nate Castillo

Let's Build!

Are you ready to start compounding with AI?

Start the Conversation

© 2026 Nate Castillo

Let's Build!

Are you ready to start compounding with AI?

Start the Conversation

© 2026 Nate Castillo