Forward-deployed AI engineering for enterprises that need models in production, not slides in a deck.
It is a Tuesday inside a top-ten North American bank. A Fligoo engineer is on the call, but not as a vendor. She has a desk on the floor, an SSO badge, and a Slack handle that ends in @bank.com. The model she has been training for nine weeks - a churn predictor that nobody has been able to put into production for three previous quarters - is now serving traffic. Nobody is taking a victory lap. Someone is asking what to ship next.
That scene, multiplied across banking, wealth, insurance, retail, telecom, and consumer goods, is what Fligoo sells. The company calls it forward-deployed AI engineering, which is consultant-speak for: our people work inside your company until the thing actually works. The rest of the industry calls it a refreshing change of pace.
The dirty secret of enterprise AI is that the hard part was never the model. The hard part is the eleven months that come before the model - the warehouse schema that nobody owns, the column nobody can explain, the legal review that nobody scheduled, the metric that nobody agreed on. By the time a data scientist has clean rows, the budget has moved to a different initiative.
Fligoo built its entire company around that eleven-month problem. The thinking: if the bottleneck is the messy middle, send senior engineers into the messy middle. Sit with the data team. Sit with the compliance team. Sit with the line of business that has the P&L. Then write code.
Fligoo was founded around 2013 by four Argentine engineers - Marcos Martinez, Lucas Olmedo, Jose Gonzalez Ruzo, and Juan Cruz Garzon - who suspected that the AI services market was about to bifurcate. On one side: armies of generalist consultants selling decks. On the other: small, senior, embedded teams shipping software. They bet on the second model and opened a San Francisco headquarters to be near the customers who could afford it.
It was, in the kindest possible reading, a fashionable bet. In 2013, "AI services" mostly meant a deck about Hadoop. In 2026, it means engineers who can ship a recommendation system, train a tabular transformer, wire up a vector database, and explain to the board why the orchestration layer matters. Fligoo got there a decade early, which is the only place worth getting early to.
Marcos Martinez (CEO), Lucas Olmedo, Jose Gonzalez Ruzo, Juan Cruz Garzon - long-time collaborators out of Cordoba's quietly excellent engineering scene.
Forward-deployed engineering beats off-the-shelf consulting in any market where the data is messy and the stakes are real. Which is to say: every market worth being in.
If you only read the slides, Fligoo looks like a services firm. If you read the engineering, it is a platform company that uses services as a distribution channel. The platform is called SharpAI. It bundles four things that enterprises always need and almost never get in the same place.
PracticeAI is the wealth management intelligence layer that powers the Broadridge partnership. AUTONOMY runs autonomous agents that actually execute workflows. AI Orchestrator handles omnichannel outreach. DataMoveX is the pipeline plumbing that keeps everything fed. Sitting beneath all of it: Supply Chain 360, the forecasting product for global logistics.
The unifying logic is uncomfortably simple. Predict something. Act on the prediction. Move the data. Repeat.
Fligoo's customer disclosures read like a Bond villain's brag: top-10 NA bank, top-3 global retailer, top-10 global insurer, top LatAm bank, top global beverages firm. No logos. No case studies. Plenty of references, the kind that only travel by phone call. The chart below is what the company is willing to say in public, ranked by the polite specificity of the boast.
Read the Fligoo website and one word repeats: outcomes. Sales lift. Retention. Operational efficiency. The company is allergic to deliverables that cannot be tied to a dollar amount, which is the kind of allergy that tends to keep customers renewing. A churn model that does not reduce churn is not, by Fligoo's reckoning, a churn model. It is a bill.
That allergy explains the org chart. The engineers are senior because junior engineers cannot push back on a stakeholder. The platform is opinionated because optionality is how AI projects bloat. The customer list is private because - and this is the part that earns the trust - the customers prefer it that way.
Every Fortune 500 board now has an AI mandate. Almost none of them have an AI org. The gap between mandate and org is where Fligoo lives, and that gap is getting larger, not smaller. Agents will not close it. Frontier models will not close it. What closes it is people who have shipped before, sitting next to people who have not.
Back to the Tuesday call. The churn predictor is live. The Fligoo engineer has already opened a Jira ticket for the next thing - a margin model for the credit card book. The bank's data team is reading the design doc, asking real questions. Nobody is congratulating anybody. This is what production looks like. It is mostly boring. That is the entire point.