Because the most expensive AI mistake is building the wrong thing. Discovery exists to make sure we understand your actual operational problem before any development begins. It produces a prioritized list of opportunities, two functional prototypes, and a clear build roadmap — so when you invest in implementation, you're investing in something that has already been validated internally by your own team.

Skipping Discovery and going straight to Build is possible if you already have a well-defined use case, clean data, and a clear scope. If that's your situation, tell us in the first call and we'll recommend the right entry point.

No. The mockups produced in Discovery are Proofs of Concept — functional prototypes built for internal evaluation and decision-making. They run, they respond, and your team can test them. But they are not production systems. They don't have the architecture, integrations, or stability required for operational use at scale. Their purpose is to validate whether a use case is worth building before any development investment is made.

For Discovery: access to the people who own the processes we'll be analyzing, typically two or three sessions with whoever runs the area we're focusing on. No technical setup required.

For Build: your data needs to be structured and accessible. We don't do data wrangling, server configuration, or database setup as part of the Build scope. If that work is needed, we identify it in Discovery and it gets scoped separately or handled by your team before we begin.

Discovery requires three sessions, one diagnostic (90 min), two co-creation workshops (90 min each), and one executive presentation (2 hrs). Total time commitment from your team: approximately 6 hours over 20 business days.

Build requires two training sessions and availability for testing and feedback rounds. The rest is on us.

We don't request access to your customer data. The systems we build process data within the platforms and providers you choose, we configure how they connect and operate, but the data stays within your infrastructure and the providers you approve.

For organizations with compliance requirements, we scope the data architecture accordingly during Discovery and recommend providers with the relevant certifications (SOC2, GDPR-aligned, and others as applicable).

Every Build includes 30 to 60 days of post-launch support, behavioral corrections, prompt adjustments, and integration review within the agreed scope. After that period, operational responsibility transfers to your team, which is why the two training sessions and full documentation are included in every engagement.

If you want ongoing support after that, the Fractional AI service covers exactly that, strategic sessions, system improvements, and internal team development on a retainer or project basis.

We're based in Buenos Aires and work across LATAM, US and Europe. Engagements are conducted remotely. Time zone alignment with Argentina is our only practical constraint for scheduling sessions.

Because we don't know what we'll find until we look. The scope and cost of fixing or continuing an AI project depend entirely on what was built, how it was built, what failed, and what is recoverable. Quoting a fixed price before the diagnostic would either be inaccurate or inflated to cover uncertainty — neither of which serves you.

Stage 1 — the diagnostic — has a fixed price starting from USD 800. It produces a written assessment and concrete options. If you decide to proceed, Stage 2 is quoted based on findings. You're never committed beyond Stage 1 without knowing exactly what it will cost.

Stage 1 — the diagnostic — has a fixed price starting from USD 800. It produces a written assessment and concrete options. If you decide to proceed, Stage 2 is quoted based on findings. You're never committed beyond Stage 1 without knowing exactly what it will cost.on

Yes, with conditions. If you arrive with a clearly defined use case, organized data, a mapped process, and a realistic scope, we can evaluate whether to go directly to Build. We'll confirm this in the first call. If there are unresolved gaps, unclear requirements, messy data, undefined success metrics, we'll recommend starting with Discovery regardless, because those gaps become expensive problems during implementation.

Three things. First, we don't start with a solution, we start with your problem. Every engagement begins with a diagnostic before any recommendation is made. Second, we show before we pitch, Discovery produces working prototypes, not slide decks. Third, we build for independence, every deliverable is designed so your team can own and operate the result without depending on us indefinitely. We measure success by how little you need us after we leave.