Free diagnostic · 60 seconds

There are 5 levels of AI adoption.
You're lower than you think.

5 questions, 60 seconds, private. Find your level — and the distance to the next one.

Take the diagnostic →
No RFP · No sales pitch · No commitment

Most companies aren't where they think they are.

You have ChatGPT Enterprise. You bought Copilot licenses. Your marketing team uses Claude.

That's not AI adoption. That's prerequisite.

Your board is going to ask soon — if it hasn't already — what your company's real AI strategy is. "We have licenses" won't be enough.

The gap between Level 2 and Level 4 isn't 50% better. It's 500% better.

Not incremental. Categorical. And it widens every quarter. Before your board asks, it's worth knowing where you actually stand.

The 5 levels, at a glance.

What distinguishes each level isn't the tools — it's what the company actually ships in production.

Level01
Curious
ChatGPT in tabs. No process change.
Ships: nothing in production. Reps using AI on their own.
Level02
Equipped
Reusable prompts and tools shared across the team.
Ships: prompt libraries, enrichment waterfalls, basic scoring models.
Level03
Automated
At least one workflow runs end-to-end without humans.
Ships: agents in production for a specific vertical.
Level04
Leveraged
Central AI infrastructure: evals, deploys, shared context.
Ships: internal agent platform, skills library, automated evals.
Level05
Compounding
The system improves itself.
Ships: agents that surface improvement opportunities, models that retrain on feedback.

The diagnostic. 5 questions. 60 seconds.

Answer honestly — the value is in nobody else seeing your answers. The verdict comes at the end, with a specific recommendation for your company.

QUESTION 1 / 5
Who owns AI in your organization today?
What do you build vs buy in AI?
What's the state of your data for AI?
Where does AI live in your organization?
How autonomous is AI in your processes today?

Your verdict is ready.

Before we show it, where should we send it? You'll also get an executive PDF summary you can share with your board.

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Your verdict
Level 2
Equipped

What would take you to the next level:

    Let's talk about your roadmap →
    The real gap

    Moving up a level isn't 50% better. It's 500% better.

    This is where most companies get it wrong. They think of AI maturity as a linear slope — a few more prompts, a few more licenses, a few more training hours. So they end up "a little better."

    It doesn't work that way.

    Between Level 2 (shared prompts) and Level 4 (central infrastructure) there's a discontinuity. What an Applied AI engineer ships at Level 4 isn't 50% better than what someone with a shared prompt does. It's 500% better — because it operates on structured data, with evals that catch degradation, with multi-source context, with agents that have been in production for months.

    It's the difference between a faster calculator and a team that takes over your entire financial reporting. The calculator helps. The team redefines the operation.

    The typical mistake

    That "AI Lead" you're hiring doesn't exist.

    I know because it's the first idea almost every CEO has when they see the gap. "Let's hire someone who knows AI and have them build this."

    Three problems with that plan:

    First: the labor market. "GTM Engineer" job postings grew from 10 in 2022 to over 2,000 in 2026 — 205% year over year. The few who fit the real profile (who ship models to production, not who make slides) are already employed, mostly at US startups paying dollar salaries. In Chile, that profile is effectively non-existent.

    Second: even if you found them, they'd build what your CTO mandates. Not what AI is actually capable of today. And your CTO — with respect — probably doesn't know what AI is capable of today. Nobody in the company does. That's why you're hiring in the first place.

    Third: one person, even a technical one, doesn't build a Level 4 in 12 months. They build a Level 2.5 in 18 months. You need someone who's already built Level 4 before — operating with team leverage, not as an individual contributor.

    That's why Moonshot exists.

    The regional race

    While Chile rolls out Copilot, São Paulo is already at Level 4.

    It's uncomfortable to say. But the data backs it.

    Brazilian and Mexican mid-market and corporate companies already have dedicated Applied AI teams in production. European multinationals operating in LATAM are consolidating their AI stack from São Paulo and Mexico City — not from Santiago. The reason is talent + capital + shipping culture.

    Chile isn't losing the race. It's starting later, with less talent available, and a more conservative corporate culture around technology. That's recoverable — but only if the decision is made fast, and executed differently.

    "Differently" means: not an 18-month digital transformation with a traditional integrator. It means: a new piece in production every 30 days, monthly subscription, cancelable, no RFP.

    How Moonshot works.

    Two phases. The first is your document even if we don't continue. The second is a new piece every 30 days.

    Phase 1 · Diagnostic
    USD 4,000
    one-time · 2-3 weeks
    Prioritized inventory of AI opportunities in your company, mapped against EBITDA impact, technical viability, and data availability. 12-month roadmap with 3-4 candidate pieces and an implementation sequence.
    It's your document — even if we don't move to Phase 2.

    Who's behind Moonshot.

    Over 10 years shipping production-grade software in 15+ countries across LATAM and the US.

    Operators leveraged by an internal AI Board of 7 specialized agents — visionary, engineer, financial, sales, skeptic, operator, explorer. That's the lever that lets us deliver full-team output without being a traditional agency or a 80-person integrator in the middle.

    No offshore team to coordinate. No slides to generate. No RFP to respond to. No annual contract to renegotiate.

    If a month doesn't ship, that month isn't billed.

    30 minutes. One conversation. Zero commitment.

    If you could have your company's next AI-first software piece in production in 60 days

    — where would you start? That's the conversation.

    WhatsApp · on request