AI Departments-as-a-Service
Foresight Is Live — Start There First

Your Team of 3
Does the Work of 30

We build AI departments — coordinated teams of specialized agents that multiply what your people can do. Secured. Managed. Measured.

Not a chatbot. Not a copilot. An AI workforce layer for real businesses — architected by someone who ran $3.2B operations at Amazon.

Most companies should start with Foresight — add AI teammates to your existing team in 5 minutes. If you already know you need something fully custom or ownership-based, this page shows how we build it.

Start with Foresight See How It Works

Your Best People Are Drowning
in the Wrong Work

If You Run a Small Business

Your team is talented but drowning. Everyone wears 4 hats. The work that matters — strategy, relationships, creative thinking — gets buried under data entry, report generation, email triage, and scheduling. You can't afford to hire 10 more people. Even if you could, you can't find them.

If You Lead a Corporate Team

Your department is headcount-capped but scope keeps growing. Every quarter brings new initiatives with no new resources. Your best people spend 60% of their time on work that doesn't require human judgment. Attrition is climbing because talented people don't want to be data-entry clerks.

Not a Chatbot. A Workforce.

An AI Department is a coordinated system of specialized agents managed by an orchestrator that understands your business. Each agent is purpose-built: marketing agents that draft and analyze. Ops agents that automate and monitor. Finance agents that reconcile and forecast.

Your team doesn't get replaced. They get promoted.

From task-doers to directors — overseeing AI agents that handle the 60% of work that doesn't require human judgment.

The AI Department Architecture

Your team member stays in charge. The AI Orchestrator manages specialized agents across every function. Everything runs inside an enterprise security boundary.

graph TB
    DH["👤 DEPARTMENT HEAD / OWNER
Sets Priorities · Makes Decisions · Provides Judgment"]
    
    DH -->|"Strategic
Direction"| ORC

    ORC["🧠 AI ORCHESTRATOR
Business Context · Task Routing · Performance Tracking"]

    ORC -->|"Tasks &
Context"| MKT["📣 MARKETING
Content · SEO · Social · Analytics"]
    ORC -->|"Tasks &
Context"| OPS["⚙️ OPERATIONS
Automation · Inventory · Vendors · Reports"]
    ORC -->|"Tasks &
Context"| FIN["💰 FINANCE
Invoices · Reconciliation · Forecasting · Auditing"]
    ORC -->|"Tasks &
Context"| CS["🎧 CUSTOMER SUCCESS
Triage · Responses · Sentiment · Churn"]
    ORC -->|"Tasks &
Context"| ENG["🛠️ ENGINEERING
Code Review · Bug Triage · Docs · Testing"]

    MKT --> INT["🔌 YOUR EXISTING TOOLS
CRM · ERP · Email · Slack · Cloud Storage"]
    OPS --> INT
    FIN --> INT
    CS --> INT
    ENG --> INT

    SEC["🛡️ SECURITY: Encryption · RBAC · Audit Trails · Data Residency"]
    DASH["📊 EXECUTIVE DASHBOARD: ROI · Agent Performance · Cost Savings · Security Logs"]

    style DH fill:#1a1a2e,stroke:#AB7522,stroke-width:3px,color:#fff
    style ORC fill:#16213e,stroke:#AB7522,stroke-width:2px,color:#fff
    style MKT fill:#0f3460,stroke:#533483,color:#fff
    style OPS fill:#0f3460,stroke:#533483,color:#fff
    style FIN fill:#0f3460,stroke:#533483,color:#fff
    style CS fill:#0f3460,stroke:#533483,color:#fff
    style ENG fill:#0f3460,stroke:#533483,color:#fff
    style INT fill:#16213e,stroke:#0f3460,color:#fff
    style SEC fill:#900000,stroke:#fff,stroke-width:2px,color:#fff
    style DASH fill:#533483,stroke:#fff,color:#fff
The Hidden Problem

Why AI Gets Worse the Longer
It Runs

Every AI model has a fixed working memory — its context window. Give it a long enough task and something critical always gets pushed out. Early instructions vanish. Decisions contradict each other. The model starts filling gaps with guesses instead of facts.

This isn't a bug you can patch. It's a physics constraint. The question is whether your AI architecture accounts for it — or ignores it until production breaks.

Traditional Approach

One Long Session · Everything In Memory

Task 1
✓ Correct
Task 2
✓ Mostly correct
Task 3
⚠ Drifting from spec
Task 4
✗ Contradicts Task 1
Task 5
✗ Original context lost
Task 6
✗ Compounding errors
Working memory 97% full — critical context pushed out

The model doesn't know what it's forgotten. It fills gaps with plausible-sounding guesses.

Our Architecture

Ingress Guardrails · Isolated Tasks · Validation-Before-Done · Persistent Memory

Task 1
✓ Validated → committed
Task 2
✓ Validated → committed
Task 3
✓ Validated → committed
Task 4
✓ Validated → committed
Task 5
✓ Validated → committed
Task 6
✓ Validated → committed
Working memory per task Always fresh — reloaded from persistent memory

Each iteration starts clean. What was learned is written down. What matters is loaded back in.

The Six Mechanisms That Make This Work

🚧

Ingress Guardrails

Risky actions and oversized requests are blocked before they reach an agent — at the control-plane layer, not the prompt layer. The system defaults to deny, returns structured reroute guidance, and fails closed. No hoping the model follows instructions.

⚡

Task Isolation

Each agent handles one atomic, well-defined task — not an open-ended session. Isolated runs execute in their own context. Fresh every time. No accumulated drift.

🔒

Validation-Before-Done

Nothing can be marked complete without validation evidence. An agent can't self-certify its own work — the system requires proof of success before closing the loop. Bad output gets caught at the task level, not after 40 tasks of compounding errors.

🧠

Persistent Memory

Agents don't rely on in-session recall. Decisions, context, and lessons are written to structured memory files — reloaded fresh at the start of each task.

🎯

Specialized Routing

The orchestrator routes to the right specialist. No single agent carries the full context of every function — each expert knows its domain deeply.

🧭

Governed Approval Flow

Critical actions enter a durable approval queue visible in the dashboard. Approved items dispatch through the real execution path with scoped tokens and expiry. Every item carries its own transition history — an audit trail per work item, not just a global log.

Security Architecture

The Rule We Never Break

Don't give an AI permission and then tell it not to use it.
Remove the permission.

Most AI deployments rely on prompt-level guardrails: "Don't send emails without approval." "Don't delete records." "Don't access production data." That's a policy document, not a security boundary. Prompt instructions can be overridden by injection attacks, ignored during hallucinations, or simply forgotten when context windows fill up. We architect differently.

How Most People Do It
✗

Email Agent

Given full email access. System prompt says "always draft, never send without approval." A prompt injection or context overflow — and the guardrail vanishes.

✗

Database Agent

Given read-write credentials. System prompt says "only run SELECT queries." One hallucinated DROP TABLE and your weekend is ruined.

✗

Code Agent

Given production deploy keys. System prompt says "only push to staging." Model decides staging IS production. Nobody catches it until Monday.

The security boundary is a sentence in a prompt. That's not a boundary — it's a suggestion.

How We Architect It
✓

Email Agent

Can draft to a staging queue. Cannot send. The API credential literally doesn't have send permission. No prompt injection can escalate what the key doesn't allow.

✓

Database Agent

Runs on a read-only replica. The connection string points to a follower. Write queries return permission errors at the database level. The agent can't even try.

✓

Code Agent

Creates pull requests. Cannot merge or deploy. CI runs tests automatically. A human reviews and merges. Production deploy is a separate credential the agent never touches.

The security boundary is the credential itself. No prompt, no hallucination, no injection can override what the system doesn't allow.

This Isn't New. It's Ignored.

The Principle of Least Privilege has been standard in enterprise security for 20 years. Every IAM policy at AWS, every RBAC system, every zero-trust architecture is built on the same idea: give each actor the minimum permissions required for its job. Nothing more.

What's remarkable isn't the principle — it's that almost nobody applies it to AI agents. They hand the model a God Mode API key and hope the system prompt holds. Then they're shocked when it doesn't.

We don't hope. We constrain.

The Full Security Stack

🔐

End-to-End Encryption

AES-256 at rest, TLS 1.3 in transit. Your data is encrypted everywhere it lives and everywhere it moves.

🛡️

Least-Privilege Credentials

Every agent gets the minimum permissions for its job. Email agents can't send. Database agents can't write. Code agents can't deploy. Enforced at the credential level, not the prompt level.

📋

Immutable Audit Trail

Every agent action logged with timestamp, input, output, and decision rationale. Logs cannot be modified or deleted. Full replay capability.

🚫

No Model Training

Your data is never used to train foundation models. Ever. Your business intelligence stays yours.

📍

Data Residency

Choose where your data lives — US cloud, EU cloud, or on-premises. Customer-managed encryption keys available.

✅

SOC 2 Aligned

Controls mapped to SOC 2 Type II. GDPR/CCPA ready. HIPAA BAA available. Monthly security posture reports.

"The biggest risk in AI isn't that it doesn't work. It's that it works — and someone deploys it without thinking about who has access to what. We design the blast radius before we write the first agent prompt."

Human-in-the-Loop: The Governed Control Plane

The security stack above handles data protection and access control. But there's a harder problem: what happens when an AI agent needs to do something dangerous?

Not malicious. Dangerous. Deploy code. Send an email to a client. Modify a production database. Run a migration. These are legitimate actions that your AI workforce will need to perform — and the question is whether your architecture handles them with a prayer or a protocol.

Most AI operations tools handle this with prompt-level instructions: "Don't deploy without asking." That's the equivalent of putting a "Please Don't Steal" sign on an unlocked door. It works until it doesn't — and in AI, "doesn't" means a hallucination, a context window overflow, or a prompt injection attack that overwrites the instruction entirely.

We built a different answer: a governed control plane with three enforcement layers. Ingress guardrails that block risky requests before they reach an agent. A durable approval queue where anything that requires action sits until a human explicitly approves it. And scoped execution tokens that auto-expire after the approved action completes. This isn't a prompt instruction — it's architecture.

The sudo Model for AI Agents

If you've administered a Linux server, you already understand this architecture. A regular user can read files, run processes, and navigate the system. But anything that modifies system state — installing packages, editing configs, restarting services — requires sudo. The user doesn't have root access. They request escalation, authenticate, and the system logs every elevated action to /var/log/auth.log.

// Foresight HITL Architecture
┌─────────────────────────────────────────────────┐
│  AI AGENT (read-only service account)           │
│  Can: query, analyze, draft, recommend          │
│  Cannot: write, deploy, send, modify            │
│                                                 │
│  Agent identifies action needed:                │
│  "Deploy hotfix to production server"           │
│                                                 │
│  ┌───────────────────────────────────────────┐  │
│  │  ESCALATION REQUEST                       │  │
│  │  Action: deploy commit abc123             │  │
│  │  Target: prod-web-01                      │  │
│  │  Reason: fix null pointer in /api/v1/...  │  │
│  │  Risk: medium (production deployment)     │  │
│  │  Requested: 2026-03-05 14:32:07 CST      │  │
│  └───────────────────────────────────────────┘  │
│                    │                            │
│                    ▼                            │
│  ┌───────────────────────────────────────────┐  │
│  │  HUMAN OPERATOR                           │  │
│  │  Reviews request → Issues OTP             │  │
│  │  OTP: a8f3k2 (expires: 14:47:07 CST)     │  │
│  │  Scope: deploy commit abc123 to prod only │  │
│  └───────────────────────────────────────────┘  │
│                    │                            │
│                    ▼                            │
│  ┌───────────────────────────────────────────┐  │
│  │  ELEVATED EXECUTION                       │  │
│  │  Agent executes with OTP credential       │  │
│  │  Action logged to audit trail             │  │
│  │  OTP consumed → access revoked            │  │
│  └───────────────────────────────────────────┘  │
│                                                 │
│  AUDIT LOG ENTRY:                               │
│  [2026-03-05 14:32:41] agent=soc-deploy         │
│  action=deploy scope=prod-web-01                │
│  approver=nathan.rone otp=a8f3k2                │
│  result=success duration=34s                    │
│  auto_revoke=14:47:07                           │
└─────────────────────────────────────────────────┘

Amazon's Operational Break-Glass

This pattern has a name at Amazon: Operational Break-Glass. When an engineer needs elevated access to a production system — to fix an outage, roll back a deployment, or access sensitive data — they don't just SSH in with root. They:

  1. Open a ticket describing what they need to do and why
  2. Get approval from an on-call manager
  3. Receive time-bounded elevated credentials
  4. Execute the action — logged and auditable
  5. Credentials auto-expire

The system fires alerts. The action is recorded. Post-incident review covers every break-glass event. This is how you operate production systems at scale without hoping that people follow the rules. You build the rules into the credential system.

Foresight implements the same pattern for AI agents. The agent is the engineer. The operator is the on-call manager. The OTP is the break-glass credential. The audit log captures everything.

Why This Matters for SOC 2

SOC 2 Type II auditors evaluate whether your controls operate effectively over time. The single hardest control to demonstrate for AI systems is authorization — proving that a human authorized each significant action.

With most AI tools, the best you can offer is: "We told the AI not to do things without asking." That's a policy control, not a technical control. Auditors know the difference.

With our governed control plane, every elevated action has:

  • → A human authorization event with a named approver
  • → A scoped execution token proving the approval was for this specific action
  • → A durable per-item transition history — not just a log entry, but a full state machine from request through validation
  • → Validation evidence required before the item can be marked complete
  • → Auto-expiry on the execution token — no lingering elevated access

That's a technical control. That's what passes audits.

The Attack Surface Most People Miss

The conversation about AI security usually focuses on data: "Is my data encrypted? Does the model train on my data? Where is my data stored?" Those are real concerns — and we handle all of them.

But the attack surface that actually keeps security teams up at night is action authority. An AI agent with write access to your production database doesn't need to leak your data to cause damage. It just needs to run the wrong query. And if your security model is "we told it not to," you're one hallucination away from an incident with no audit trail.

HITL approval gates eliminate this category of risk. The agent cannot run the query without an OTP. The OTP requires a human. The human requires context. The context is logged. If the query runs, someone approved it. If no one approved it, the query doesn't run. There is no third state.

This runs in production. Today. Nathan's own AI team — the agents operating his infrastructure — run under this exact control plane. Ingress guardrails, durable approval queues, scoped execution tokens, validation-before-done, per-item audit trails. Same architecture across demo and production surfaces. Same governed behavior everywhere. We didn't design this in a meeting — we built it because we needed it at 2am when an agent wanted to push a hotfix.

What's Live

✓ Ingress guardrails: fail-closed blocking of risky and oversized requests before agent execution

✓ Durable approval queue: structured request flows with dashboard visibility across all surfaces

✓ Scoped execution tokens: approve → execute flow with auto-expiry and real dispatch path

✓ Validation-before-done: items cannot close without evidence. Per-item transition history and audit trail

What's Coming

→ Role-based approver tiers (RBAC): different approval chains for different risk levels and environments

→ Webhook integrations: ServiceNow, Jira, PagerDuty — approval requests route through your existing enterprise workflows

→ Compliance export layer: external reporting and audit export for regulatory requirements

→ Approval delegation: designate backup approvers for after-hours escalations with configurable chains

Question 1 of 2 — Infrastructure

Where Does Your
AI Actually Run?

From a laptop on your desk to redundant iron across multiple data centers — every AI deployment lives somewhere. The spectrum is wider than most people realize, and where you land changes everything about cost, control, and capability.

← Lower Cost · More DIY More Scale · More Managed →
Laptop Cloud Private HPC

How We Evaluate the Right Infrastructure For You

Four questions that drive the decision:

1

Data Sensitivity

Is your data regulated? PHI, PII, financial records, legal? The answer immediately narrows the field — some compliance frameworks require you to own the hardware.

2

Task Volume & Cost Curve

Low-volume, variable? Cloud. High-volume, predictable steady-state? Owned compute amortizes. We run the math before you spend a dollar.

3

Team Capacity

Do you have an infrastructure team? On-prem and colo require people to manage them. Cloud and API minimize operational overhead dramatically.

4

Timeline

Need production in 30 days? API or cloud, full stop. Have 6 months and a compliance mandate? We design the right owned-infrastructure stack from scratch.

This is covered in week one of your AI Readiness Audit. We don't push a default — we audit your situation and give you the honest answer, even if it means less work for us.

Question 2 of 2 — AI Models

Which Models Actually
Run Your AI?

Infrastructure tells you where the compute lives. Models tell you what's doing the thinking. These are two separate decisions — and any infrastructure tier can support any model approach.

The right model strategy usually isn't one of these three in isolation. It's a deliberate blend based on what each task actually requires.

🌐

Pure API

Frontier models — data goes out, answers come back

Call OpenAI, Anthropic, xAI, or Google. No hardware, instant access to the most capable models in the world.

✓ Frontier capability, zero infra, always latest version, pay-per-use

✗ Data leaves your environment, per-token costs compound, model behavior can change

Best for: any task where data sensitivity allows it

Most Common
⚡

Hybrid

Right model for the right task — local + cloud

Sensitive or high-volume tasks run on local models. Complex, creative, or judgment-heavy tasks go to frontier APIs. The orchestrator routes intelligently.

✓ Data governed per task, cost-optimized, frontier where it matters

✗ More complex to architect, requires clear data classification

Best for: most businesses once they've thought it through

🔒

Pure On-Premises

Open-source models — your hardware, your data, always

Models like Qwen, Llama, Mistral, DeepSeek run entirely on your infrastructure. Nothing leaves. The gap vs frontier APIs is narrowing fast.

✓ Absolute data sovereignty, no per-token costs, fine-tunable

✗ Still lags frontier on complex reasoning, hardware required

Best for: regulated industries, high-volume steady-state

These Two Decisions Are Independent — But They Interact

Any infrastructure tier can technically support any model approach. The combination you choose sets your cost floor, your capability ceiling, and your data risk profile all at once.

We model all three dimensions — infrastructure, model approach, and task requirements — before recommending anything. The audit is where this gets figured out right.

Your Path

Start Where You Are

Four starting points based on where you are today. Each one is designed to deliver value on its own — and naturally opens the door to the next level when you're ready.

We're transparent about exactly how we do this. Show it to your CTO. Try it yourself if you want. The methodology isn't the secret — the execution is.

Default path: Add AI teammates to your existing team with Foresight starting at $149/mo for Solo, $495/mo for FS1, or $1,295/mo for FS2. This page is for when you need the bigger path: custom architecture, ownership, deeper integration, or enterprise-level complexity.

"I'm curious but skeptical"

AI Readiness Audit

$10,000

One-time · 2 weeks · You keep the playbook regardless

What You Walk Away With

  • →A 15-page operating playbook — not a slide deck. Specific workflows mapped, scored, and prioritized by ROI.
  • →Automation scoring matrix: every process rated on effort, impact, risk, and readiness.
  • →Security gap analysis: where your data flows today, where it shouldn't, and what changes.
  • →Infrastructure recommendation: which model approach and compute tier fits your situation.
  • →ROI model: projected time savings, cost reductions, and payback timeline for each automation target.

You keep the playbook whether we work together or not.

How We Build It — Step by Step

Day 1-3

Stakeholder interviews. 30-min calls with team leads. We use a structured interview protocol — not open-ended "tell me about your day" sessions. We're mapping inputs, outputs, decision points, and handoff friction for every core workflow.

Day 4-6

Process documentation & scoring. We build a workflow map (who does what, how long, how often) and score each process on 4 axes: effort to automate, business impact, risk if it fails, and current tool readiness. This produces a ranked list.

Day 7-8

Security & infra assessment. Where does your data live? What compliance frameworks apply? What tools are in use? We map data flows and identify the right model approach + infrastructure tier using the 4-question framework above.

Day 9-10

ROI modeling & playbook assembly. We calculate projected savings for each automation target (time × cost × volume), build the phased implementation plan, and deliver the playbook in a 60-min walkthrough.

For your tech partner: The interview protocol, scoring matrix, and ROI model are standardized frameworks we've refined across multiple deployments. Your CTO can validate the methodology, challenge the assumptions, and reproduce the analysis. The value isn't the spreadsheet — it's knowing which workflows will actually work with AI and which will burn money. That takes operational judgment, not just technical skills.

"I'm convinced — show me results"

AI Quickstart

$15,000

One-time · 3-4 weeks · First wins in 14 days

What You Walk Away With

  • →3-5 production AI agents running real work in your actual systems. Not a demo — live workflows processing real data.
  • →Security boundaries configured per agent: each one has minimum-permission credentials scoped to its job.
  • →Monitoring dashboard showing agent activity, success rates, and time saved.
  • →30 days of tuning: we watch the agents work, catch edge cases, refine prompts, and optimize routing.
  • →Team training: your people know how to monitor, escalate, and steer the agents. No vendor lock-in.

How We Build It — Step by Step

Week 1

Architecture design. We select the top 3-5 workflows from the audit (or do a rapid assessment if you're skipping the audit). For each: define inputs, outputs, success criteria, failure modes, and security boundaries. Choose model approach per agent.

Week 2-3

Build & integrate. Deploy orchestrator + agents. Configure least-privilege credentials for each tool connection (CRM, email, databases, Slack, etc.). Connect to your existing systems via APIs. Set up persistent memory and validation gates.

Week 3-4

Testing & launch. Shadow mode first: agents run alongside humans, outputs compared. Then graduated autonomy: low-risk tasks go live first, with human review gates on high-impact decisions.

Week 5-6

Tuning & handoff. Monitor edge cases, refine prompts, adjust routing. Train your team on the monitoring dashboard. Document everything. You own the system — no lock-in.

For your tech partner: The orchestrator layer uses established agent frameworks with persistent memory (structured file systems, not in-context). Each agent gets an isolated execution environment, scoped API keys, and explicit success criteria checked at the validation gate before any output is committed. Tool integrations use standard OAuth/API key flows — no proprietary middleware. Your team gets full access to the configuration, prompts, and monitoring. You can fork it and run it yourself after handoff.

Flagship

"This is working — I want more"

AI Managed Services

$12K–$18K/mo

6-month minimum · Month-to-month after

Department Base

$12,000/mo

  • →Orchestrator + up to 5 specialized agents
  • →Monthly optimization cycle
  • →Executive dashboard with ROI tracking
  • →4-hour SLA on issues
  • →Monthly performance report

Best for: teams of 5-15 ready to offload repetitive work across 2-3 functions.

Department Pro

$18,000/mo

  • →Orchestrator + up to 15 specialized agents
  • →Weekly optimization cycle
  • →Strategic AI roadmap for your business
  • →2-hour SLA + dedicated Slack channel
  • →Weekly performance report + quarterly business review

Best for: teams of 10-50 running AI across 4+ business functions. The full AI department experience.

What Ongoing Management Actually Looks Like

Weekly (Pro) / Monthly (Base)

Agent performance review: success rates, edge case analysis, prompt refinement. New automation targets identified from usage patterns. Cost optimization — right-sizing model tiers per task.

As Needed

New agent deployment as your needs evolve. Tool integration updates when you adopt new software. Security credential rotation. Model upgrades when better options become available.

Quarterly (Pro)

Business review with leadership: ROI realized vs projected, strategic recommendations, industry benchmarking, roadmap for next quarter's AI expansion.

For your tech partner: You retain full access to all agent configurations, prompts, memory files, and monitoring. The orchestrator and agents run on infrastructure you control (or cloud accounts you own). We manage — we don't hostage. If you cancel, you keep everything. The value of the managed service is continuous optimization, not access control: we watch agent performance across hundreds of task executions, identify drift patterns your team won't catch, and deploy improvements weekly. It's the difference between building a car and having a pit crew.

"Make AI our unfair advantage"

Fractional AI Officer

$20,000/mo

By invitation · A CAIO costs $300K+. This doesn't.

What You Get

  • →Everything in Department Pro — full AI department deployed and managed.
  • →Nathan as your AI executive. Board-level strategy, not just operations.
  • →Competitive intelligence: what AI capabilities your competitors are building and how to stay ahead.
  • →Vendor evaluation: when you're pitched AI tools, I tell you what's real and what's smoke.
  • →Custom agent development for strategic initiatives — not just automating existing processes, but creating new capabilities.

Why This Exists

Most companies that need a Chief AI Officer can't justify the $300K+ salary, don't know what to look for, and will end up hiring someone who's great at AI theory but has never operated anything at scale.

I've run P&Ls, led turnarounds, built products at Amazon, and currently operate production AI systems 24/7. The Fractional AI Officer role isn't about advising from the sidelines — it's about operating in the trenches with your team.

Limited to 3 clients at any time. If it's not a fit, I'll tell you before you spend a dollar.

For your tech partner: This isn't a replacement for your CTO or VP of Engineering. It's a complement. I handle AI strategy and operations so your technical leadership can focus on your core product. I'll work directly with your engineering team on integration points, respect your architecture decisions, and defer to your team on your product domain. My domain is AI operations, and I stay in my lane.

Built by an Operator,
Not a Consultant

Nathan One

20 years building and turning around companies. Six tech turnarounds. #1 sales rep at a startup-to-IPO. VP running full P&Ls. Head of Partnerships at Amazon, growing the org from $1B to $3.2B.

Built AI systems at Amazon scale — LLaMA Task Engine, partner scoring, sentiment analysis. Now I help companies build what I've already built: AI departments that work in production, not just in demos.

If it doesn't perform, I don't get paid.

Every Company Will Have an AI Department.
Will Yours Be Built Right?

Five questions. I'll respond within 48 hours if it's a fit.

Not a chatbot. Not a funnel. I read every one.