Nate Castillo

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Nate Castillo

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AI Infrastructure | Token Economist

AI Value Engineering

From prototype to production, with every token generating a return.

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About

Builder. Operator. Seller.

Builder. Operator. Seller.

Nate Castillo

Los Angeles, CA

Forward Deployed Value Engineering

user pic

Background

Founder and operator at the frontier of enterprise AI. 13 years running GTM in AI infrastructure and cloud (HPE, CloudBolt, Aruba), plus two startups founded in enterprise AI and fintech. I run discovery with leadership, turn the problem into an architecture, and prototype with engineering so whiteboard to working system takes weeks, not quarters. Bilingual in business and bits.

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PROBLEM

AI made pilots easy. Production is still difficult.

Most enterprise AI initiatives don’t die because the model didn’t work. They die in pilot purgatory: a demo that impressed everyone, built on an architecture that couldn’t survive security review, procurement, 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: a demo that impressed everyone, built on an architecture that couldn’t survive security review, procurement, 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

AI economics and governance aligned from the start

AI economics and governance aligned from the start

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 business outcomes

Translate AI capability into business outcomes

From executive discovery to agents working in the background

From executive discovery to agents working in the background

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

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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 value

Three steps to an AI system that compounds value

Locked in process. Outcome oriented execution. Relentless curiosity.

Locked in process. Outcome oriented execution. Relentless curiosity.

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.

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

Under promise. Over deliver.

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!

Building on the frontier of AI?

Let's cook

© 2026 Nate Castillo

Let's Build!

Building on the frontier of AI?

Let's cook

© 2026 Nate Castillo

Let's Build!

Building on the frontier of AI?

Let's cook

© 2026 Nate Castillo