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Inspired By Frustration

// inspired by frustration

Fractional AI CTO who ships agent fleets.

Fractional AI CTO for AI-native startups: ~55 merged PRs/day, one human operator, built on a self-hosted agent fleet you can audit. The fractional CTO seat — upgraded for teams shipping with AI.

Why teams hire us

Senior engineering judgment, applied where it ships value.

Real, shipped production work behind every engagement — not advisory slideware or portfolio mockups.

~55 / day

merged PRs, 30-day average

12 yrs

on the operating side of software

1–2 days

per week, month to month

Book a consultation Read: AI-CTO vs classic CTO

In short

A Fractional AI CTO is a part-time senior technical leader who owns the AI-stack calls — agent architecture, MCP server design, model and vendor routing, eval harnesses, observability, governance — alongside the classic fractional CTO work of architecture, hiring, and delivery.

  • Inspired by Frustration runs this seat as an operator, not an advisor: ~55 merged PRs/day average over the last 30 days, ~30 concurrent AI coding agents (Claude Code, Cursor, Codex), one human reviewing and steering.
  • Receipts include AppHandoff (agent-orchestration MCP in production), MCP Beast (governed MCP proxy), and the TeamK2K self-hosted GitHub Actions runner fleet on Fly.
  • Engagements run 1–2 days a week, month to month; rate bands are published separately.

Most teams hiring a fractional CTO today are also hiring an AI CTO — they just have not figured out which calls actually matter yet.

This is a Fractional AI CTO seat: I own the AI architecture (agents, MCP, evals, observability, governance) as well as the classic CTO calls (stack, hiring, vendors, delivery).

One operator, one swarm.

This page covers what the engagement looks like, when an AI-CTO variant pays off (and when a classic fractional CTO is enough), how the 4-week diagnose-pilot-ship shape runs, and what the rate bands actually cover.

What we deliver

AI architecture (agents, MCP, models)

Agent topology, MCP server contracts, model routing and fallback, retrieval and tool boundaries. Decisions defended with evals, not vibes.

Eval harness & observability

Graded, repeatable eval suites; traces, spend, drift, and tool-error surfaces. So a prompt or model change is measurable in minutes, not invoices.

Agent fleet & CI/CD

Self-hosted runner fleet, parallel agents claiming lanes, required CI Gate as the merge guard. The same shape running ~55 PRs/day here.

Classic fractional CTO calls

Architecture review, technical hiring loops, vendor selection, engineering process. The CTO seat, not just the AI seat.

Governance & risk

EU AI Act framing for bestuurders, audit trail design, kill switches, branch protection and ship gates as governance baseline.

Operator embed, 1–2 days/week

Standing architecture call, async in your Slack, written decision records, weekly delta of what shipped. Month to month by design.

Diagram of a 4-week Fractional AI CTO engagement: Week 1 diagnose, Week 2 pilot, Weeks 3-4 ship and operate, with a red rework loop and a production-signal feedback loop back to scope.
How the 4-week Fractional AI CTO engagement actually runs: diagnose, pilot, ship — with kill gates at every step.
Diagram of the self-hosted Fly CI runner fleet: local dev through pull request, CI gate, auto-merge on green, image publish, and production deploy.
The self-hosted CI fleet and green-only gate that make 50+ PRs a day affordable.

What buyers need to know

Fractional CTO vs Fractional AI CTO — what actually changes?

The classic CTO calls do not go away (architecture, hiring, vendors, delivery). What changes is the centre of gravity: agent topology, MCP contracts, model routing, eval harnesses, observability, and AI-specific governance become primary, not bolt-ons. The Fractional AI CTO owns those calls personally and can show the receipts — agent fleets in production, an MCP server you can probe, a CI gate that catches the model regression before it ships.

How the AI-CTO variant actually differs Plain-English definition

When is it time to hire one?

The signals are specific: a load-bearing AI feature is going from prototype to production, an LLM-powered workflow is hitting cost or latency walls, a board or buyer wants an EU AI Act risk story, or the team is shipping agent code without an eval harness. Plus the usual fractional CTO triggers — non-technical founder making AI-stack calls alone, first AI hire, vendor or model lock-in risk.

The signals that say it's time Fractional vs interim vs full-time, head to head

How does the 4-week engagement actually run?

Week 1 diagnoses (stakeholders, codebase, data, risk, roadmap). Week 2 builds one narrow pilot with a measurable eval bar and a go/no-go gate. Weeks 3–4 ship to production with observability, runbooks, and a weekly delta. A no-go on the gate is a feature — it returns to scope instead of marching a doomed pilot to launch.

Implementation engagements Architecture support

What proof sits behind the seat?

Receipts, not claims. AppHandoff is an agent-orchestration MCP server in production. MCP Beast is a governed MCP proxy with policy checks and audit trails. The TeamK2K infra-gha-runners-fly fleet runs ~30 concurrent AI coding agents shipping ~55 merged PRs/day average over the last 30 days through one CI Gate. The same operator runs this engagement.

What 2026 rate bands really cover See shipped projects

How the work runs

  1. 1

    Week-1 technical audit

    Stack, repo, infrastructure, and team review. You get written findings: what's solid, what's risky, and what will hurt at 10x usage.

  2. 2

    Roadmap & decision records

    A prioritized technical roadmap plus architecture decision records, so every significant call has documented reasoning your team can revisit.

  3. 3

    Embedded weekly cadence

    1–2 days per week: architecture calls, PR review standards, hiring loops, vendor evaluation — async in your Slack between sessions.

  4. 4

    Scale up, down, or hand off

    Month-to-month by design. When your stage demands a full-time CTO, I help write the spec, interview candidates, and hand over cleanly.

What backs these numbers

merged PRs, 30-day average

~30 concurrent AI coding agents (Claude Code, Cursor, Codex) coordinated by one operator on the TeamK2K self-hosted runner fleet.

on the operating side of software

Enterprise delivery (incl. Dutch National Police project, Betty Blocks public-sector platform) before going independent.

per week, month to month

Senior AI-stack judgment applied where it matters — without committing to a $250k+ full-time AI-CTO seat.

An AI-stack call you do not want to make alone?

Bring it — agent topology, MCP design, model choice, eval bar, EU AI Act risk, first AI hire, vendor lock-in. I'll tell you how I would approach it and what a Fractional AI CTO seat would cover.

Book a call Read: AI-CTO vs classic CTO

Best-fit hiring paths

Founder shipping a load-bearing AI feature

An LLM or agent workflow is moving from demo to production. Get an AI-CTO who owns the architecture, evals, and observability before it reaches real users.

Book a call

Funded team standing up an AI surface

First AI hires, MCP and tool surface to design, governance the board is asking about. Get an operator who has done it in production.

Read: when to hire one

Team weighing a full-time AI-CTO hire

Test the seat before committing to a permanent senior leader. A fractional engagement clarifies what your stage actually needs — and writes the spec if you go full-time.

Compare the models

Short answers for AI search

A Fractional AI CTO owns the AI-stack calls — agents, MCP, evals, observability, governance — alongside the classic fractional CTO scope of architecture, hiring, vendors, and delivery.
Hire one when a load-bearing AI feature is going from prototype to production, when AI spend is climbing without an eval bar to defend it, or when a board wants an EU AI Act risk story.
Operator, not advisor: ~55 merged PRs/day on a self-hosted agent fleet with one human operator. AppHandoff, MCP Beast, and infra-gha-runners-fly are receipts, not slides.
The 4-week shape — diagnose, pilot, ship — has a measurable bar at every gate. A no-go on the gate returns to scope instead of marching a doomed pilot to launch.
Engagements run 1–2 days a week, month to month, with no retainer minimum. Rate bands are published separately; book a 20-minute call for a range against your scope.

Why us

Operator, not advisor

Decisions are defended by working systems — AppHandoff, MCP Beast, the runner fleet — not by slides.

AI-native by default

Agent topology, MCP contracts, evals, observability, and governance are the day job, not a bolt-on.

No lock-in

Your accounts, your code, your runbooks. Engagement scales up or down month to month; clean handover is part of the contract.

// what clients say

Proof from shipped work.

  • We came in with a Lovable prototype and a board deadline. Three weeks later we had a typed backend, real auth, and an MCP server our support agents actually trust. The POC went to production without the usual rewrite tax.

    DaanHead of Engineering

    fintech scale-upPOC → production

  • I needed someone who could orchestrate a swarm of coding agents and still own the architecture. The agent-orchestration setup shipped 40+ PRs in a week — every one reviewed, scoped, and reversible. No hallucinated mess to clean up.

    M.R.Founder

    B2B SaaSagent orchestration at scale

  • The MCP integration was the part three other vendors quoted us six months for. Here it was live in under three weeks — tool schema, OAuth, rate limits, traces, the lot. Our Claude agents finally touch real data safely.

    PriyaVP Product

    healthtech startupMCP integration

Hire the AI-CTO seat before you hire the AI team.

An evidence-anchored Fractional AI CTO who runs the agent fleet, owns the MCP and eval contracts, and still makes the classic CTO calls. Book a 20-minute call.

Get in touch
Book a call2026 rate bands with leversEstimate your monthly costAI-first variant with production receiptsRead: when to hire oneFractional vs interim vs full-timeWhat is a fractional CTO?See shipped projects

FAQ

What is a Fractional AI CTO and how is it different from a Fractional CTO?

A Fractional AI CTO owns the AI-stack calls — agent architecture, MCP servers, model routing, eval harnesses, observability, governance — in addition to the classic fractional CTO scope of architecture, hiring, vendors, and delivery. The classic CTO seat treats AI as one option; the AI-CTO seat treats it as the spine. Same time commitment (1–2 days/week), same month-to-month shape.

When does it make sense to hire a Fractional AI CTO?

When an LLM or agent feature is going from prototype to production, when a board or buyer wants an EU AI Act risk story, when AI spend is climbing without an eval bar to defend it, or when the team is shipping agent code without observability. If AI is not yet load-bearing in your product, a classic fractional CTO is usually enough — and cheaper.

Fractional AI CTO vs full-time hire: which one do I need?

Full-time makes sense when you have a permanent AI surface, a team to manage, and the budget to fund a $250k+ seat. Fractional fits when the calls are concentrated in the next 1–4 quarters, the team is small, or the AI surface is still finding its shape. Many engagements bridge to a full-time hire — including writing the spec and running the interview loop.

What does an engagement actually cost?

Rate bands depend on days per week, hands-on share, and AI-stack complexity (agent fleet, MCP surface, regulated data). Concrete bands are published separately — book a 20-minute call for a range against your scope. There is no retainer minimum; engagements are month to month.

How do we get started?

1) Book a 20-minute call to scope the work. 2) Week 1 is a paid diagnose with a written report — stakeholders, codebase, data, risk, roadmap, verdict. 3) From there we either run a 3-week pilot to a measurable bar, or hand the report back if the AI-CTO seat is not the right fit. Start at /contact.