AI Departments-as-a-Service

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 demo. A production AI workforce — architected by someone who ran $3.2B operations at Amazon.

See How It Works Start With an Audit

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.

Nathan's Architecture

Isolated Tasks · Validation Gates · 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 Five Mechanisms That Make This Work

⚡

Task Isolation

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

🔒

Validation Gates

Nothing advances until it passes explicit success criteria. 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.

🧭

Human Steering Checkpoints

Critical decisions surface to the human. You're not out of the loop — you're just not doing the repetitive work. Your judgment calls the shots; the agents execute.

📈

The Result

Consistent quality across hundreds of tasks. Not "works great in demos." Works in production — overnight, unattended, at scale. That's the difference.

One Person. 94 Commits a Day.

Elvis Sun runs a one-person development team powered by AI agent swarms. An orchestrator manages the business context while specialized agents handle coding, testing, and deployment. That's one developer with an AI department. Imagine what your team could do.

94

commits per day

7

PRs in 30 min

$190

/mo compute cost

Source: Elvis Sun (@elvissun), Feb 2026 — 10K+ likes, 4M+ views

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
1

Laptop / Personal Machine

MacBook, Windows laptop, consumer hardware

The floor. Works for prototyping, personal productivity tools, and pure API workflows. Not a production AI environment.

✓ Zero cost to start, instant setup

✗ Single point of failure, no scale, limited RAM for local models

Best for: proof of concept, personal automation, pure API workflows

2

Mac Mini / Consumer Desktop

The "Mac Mini craze" — Apple Silicon, 32–96GB RAM

The entry point for serious local AI. Apple Silicon's unified memory architecture makes it unusually capable per dollar. A $1,500 Mac Mini can run 32B parameter models.

✓ Low cost, low power, no data leaves the room

✗ Still limited RAM ceiling, not redundant, you're the IT department

Best for: small teams, sensitive local data, cost-conscious operators

3

Mac Studio / High-End Workstation

192GB+ unified memory, discrete GPU workstations

Serious local horsepower. A Mac Studio Ultra (192GB) can run 70B+ models comfortably. GPU workstations (NVIDIA RTX 4090, dual-GPU) push further.

✓ Frontier-adjacent capability on local hardware, data stays put

✗ $3K–$15K upfront, single machine, no failover

Best for: teams needing 70B+ models without cloud costs or data egress

4

In-Office Rack Server

Dedicated server hardware, on-site

A proper server (Dell, Supermicro, custom GPU node) in your office. Team-wide access, always on, more memory and compute than a workstation.

✓ Shared team resource, enterprise RAM (512GB+), stays on-prem

✗ Power, cooling, physical security become your problem; no redundancy

Best for: growing teams with sensitive data and consistent workloads

5

Colocation — Single Rack

Your hardware, shared data center facility

Move your server to a real data center. You own the iron, they provide power, cooling, physical security, and enterprise-grade connectivity. Data sovereignty intact.

✓ Enterprise uptime + connectivity, still own the hardware, no cloud markup

✗ Hardware refresh still on you, setup lead time, not elastic

Best for: compliance-heavy industries needing hardware ownership

6

Dedicated Colo Cage

Your hardware, your locked cage, multiple racks

Step up from a shared rack to your own locked cage. More hardware, physical separation from other tenants, custom networking. Real enterprise posture.

✓ Physical isolation, scale up within your cage, stronger compliance story

✗ Significant upfront hardware investment, still a single facility

Best for: financial services, legal, healthcare orgs owning their compute

7

Redundant Multi-DC Colocation

Your iron across multiple data centers, HA architecture

The top of the owned-hardware stack. Your equipment across two or more geographically separate facilities with failover. If one DC goes dark, you keep running.

✓ True high availability, total hardware ownership, maximum data control

✗ Very high capex ($500K+), requires dedicated infrastructure team

Best for: large enterprises, regulated sectors with strict data residency

Best for most
8

Cloud (AWS / Azure / GCP)

Managed elastic compute, GPU instances on demand

You provision compute, not hardware. Spin up a GPU cluster in minutes. Scale to zero when idle. Access to H100s without buying them. For AI workloads, this beats colo for most companies.

✓ Elastic GPU access, no hardware refresh, global reach, enterprise SLAs

✗ Ongoing costs compound, vendor dependency, data leaves your premises

Best for: variable workloads, teams without infra staff, fast-scaling orgs

9

Hybrid Infrastructure

Owned hardware + cloud burst

Sensitive workloads stay on your hardware. Elastic or public-safe tasks burst to cloud. The best of both worlds when architected deliberately — expensive when it isn't.

✓ Data sovereignty + cloud scale, cost-optimized at volume

✗ Most complex to architect and operate, requires both infra and cloud expertise

Best for: enterprises with mixed data sensitivity and high-volume steady-state

10

Private Cloud / HPC Cluster

Hyperscaler-grade — government, finance, research

The ceiling. Thousands of GPU nodes, private network fabric, air-gapped options. What the NSA, major banks, and national labs run. Built for organizations where failure is not an option.

✓ Maximum performance, full isolation, air-gap capable, custom everything

✗ $1M+ investment, requires a dedicated infrastructure engineering team

Best for: government, defense, large financial institutions, research labs

☁️ Cloud vs. Colocation — The Honest Answer

For the vast majority of businesses, cloud wins. On-demand GPU access (H100, A100) without buying $250K+ of hardware, elastic scaling, zero refresh cycles — cloud is structurally superior for AI workloads at most volumes. Colocation makes sense when you have strict regulatory requirements to own the hardware, extremely high steady-state volume where owned compute amortizes better, or specific data residency laws that prohibit managed cloud. If neither of those describes you, cloud is the answer.

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. Here's how they differ.

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

Call frontier models — data goes out, answers come back

You send a request to OpenAI, Anthropic, xAI, Google, or similar. The model runs on their infrastructure. You get the response. No hardware, no setup, instant access to the most capable models in the world.

Pros

  • ✓Frontier capability — GPT-5, Claude, Grok, Gemini
  • ✓Zero infrastructure to manage
  • ✓Always running the latest model version
  • ✓Pay only for what you use
  • ✓Fastest path from zero to production

Cons

  • ✗Your data leaves your environment — by definition
  • ✗Per-token costs compound at high volume
  • ✗Model behavior can change without notice
  • ✗Rate limits and provider dependency

Natural fit: Laptop through Cloud infrastructure tiers. Any task where data sensitivity allows it.

Most Common
⚡

Hybrid

Right model for the right task — local and cloud working together

Sensitive or high-volume tasks run on local models. Complex, creative, or judgment-heavy tasks go to frontier APIs. The orchestrator routes intelligently — most businesses end up here once they've thought it through.

Pros

  • ✓Data sensitivity governed per task, not per system
  • ✓Cost-optimized — cheap tasks stay local, complex ones go out
  • ✓Access to frontier models where it matters most
  • ✓Reduces API spend significantly at scale

Cons

  • ✗More complex to architect and govern correctly
  • ✗Requires clear data classification policies
  • ✗Local models require hardware and maintenance

Natural fit: Mac Studio, in-office rack, colo, or cloud infrastructure — wherever you have compute to run local models.

🔒

Pure On-Premises Models

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

Models like Qwen 2.5, Llama 3.3, Mistral, DeepSeek R1/V3, and Gemma 3 run entirely on your infrastructure. Nothing leaves. Ever. The capability gap versus frontier APIs is narrowing fast — the best open-source models today are remarkable.

Pros

  • ✓Absolute data sovereignty — zero data egress
  • ✓No per-token costs — compute cost only
  • ✓Model behavior is fixed — no surprise changes
  • ✓Fine-tunable on your own domain data

Cons

  • ✗Still lags frontier models on complex reasoning
  • ✗Hardware must be capable enough to run the model
  • ✗Model updates require your own evaluation + deployment
  • ✗Operational overhead to maintain the model stack

Natural fit: In-office rack, colo, cloud GPU instances, or hybrid infrastructure. Requires meaningful compute.

These Two Decisions Are Independent — But They Interact

Any infrastructure tier can technically support any model approach. But there are natural fits: a laptop realistically only supports Pure API or small local models. A multi-DC colo with GPU racks unlocks everything — including fine-tuned proprietary models that never touch the internet. 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.

Assessment to AI Department
in 90 Days

1

Audit — Weeks 1-2

We map your workflows, identify automation opportunities, score each by ROI, and build your AI operations playbook. You keep the playbook whether we work together or not.

2

Quickstart — Weeks 3-6

First agents deployed to your highest-impact workflows. Immediate wins. Your team sees results before the end of month one.

3

AI Department — Weeks 7-12

Full orchestrator + specialized agent team deployed, secured, and integrated with your existing tools. Executive dashboard live.

4

Optimize — Ongoing

Monthly performance reviews. New agents added as needs evolve. ROI tracked and reported. Your AI department grows with you.

Built for the Paranoid

Because in enterprise AI, paranoid is correct. Most AI deployments are held together with API keys and hope. Ours aren't.

🔐

End-to-End Encryption

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

🛡️

Role-Based Access

Principle of least privilege. Each agent only accesses what it needs. Critical actions require human approval.

📋

Immutable Audit Trail

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

🚫

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 start with security architecture before we write a single agent prompt."

Priced Against What You'd Pay a Human

A marketing manager, ops coordinator, finance analyst, and CS rep = $260K/yr minimum. An AI Department handles 80% of their repetitive work.

Explore

AI Readiness Audit

$2,500

2-week deep dive. Workflow mapping, automation scoring, security gap analysis, ROI model, 15-page playbook.

100% credited toward any engagement signed within 60 days

Deploy

AI Quickstart

$5K–$15K

3-5 production agents. Security config, monitoring dashboard, 30-day tuning. Live in 30 days.

One-time · Scoped by complexity

Flagship
Transform

AI Department

$5K–$15K/mo

Full orchestrator + 5-15 specialized agents. Security architecture. Executive dashboard. Monthly optimization.

6-month minimum · Month-to-month after

Strategic

Fractional AI Officer

$15K–$25K/mo

Nathan as your AI executive. Board-level strategy + full AI Department deployment + ongoing management.

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

AI Operations Setup on Aithon → AI Managed Services on Aithon →

Built by an Operator,
Not a Consultant

Nathan One

20 years building and turning around companies. Six telecom 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.