Launching a crypto-first operator on an AI-native data foundation from day one

MINT is a new generation crypto-first operator that brings casino, sportsbook and prediction-market style products together under a single Web3-native experience. Founded by serial entrepreneur Zak Manhire, the brand sits at the intersection of iGaming and crypto, where users expect speed, transparency and the kind of continuous engagement they already know from trading.

The whole product is built around one idea: progression is the product. Three verticals running in parallel, a retention-first economy, and a lean team that needs to read player behaviour in real time puts unusual pressure on the data layer. MINT chose Gamblitude as its data and analytics foundation before a single player placed a bet.

Launching a new iGaming brand has always been a race against time. Launching a crypto-first operator with three distinct product verticals makes that race considerably harder.

Most new operators accept a familiar pattern. The platform goes live, revenue starts flowing, and the data stack gets built on top of the chaos months later. Analysts stitch together PAM exports, provider feeds and payment logs in spreadsheets. KPI definitions drift between teams. Ad-hoc questions pile up faster than anyone can answer them. By the time a proper BI layer is in place, the operator has already accumulated six to twelve months of reporting debt.

There is a financial reason this pattern is so common. Building comparable analytics in-house is a serious investment: a data engineering team, a warehouse decision, a BI layer, integration work across the platform, provider feeds and payment rails, plus the time to define metrics from scratch in a domain as specific as iGaming. For a new operator, that money competes directly with brand, licensing, content deals and player acquisition. Most brands take the shortcut, accept months of limited visibility, and plan to come back to data later — and the first attempt usually falls short.

On top of that baseline problem, three factors made the standard playbook unworkable for MINT:

The real challenge was not only launching fast, but launching with full intelligence, from the first deposit, without tying up the budget that needed to go into brand and growth.

MINT made an early decision to treat the data layer as core infrastructure rather than something to bolt on later. Instead of building in reverse, the team deployed Gamblitude in parallel to the platform itself. While MINT is still in soft launch ahead of full token integration, the operator already has an AI-native analytics environment running against live operational data.

The implementation focused on three principles:

In practice, the rollout included:

Time from kick-off to the first production dashboards was measured in weeks. By the time MINT onboarded its first wave of players, the operator already had end-to-end visibility across every product, every asset and every segment.

“We are building MINT to entertain players at the highest level. To do that, we need to understand player behaviour clearly enough to design an environment that is intelligent, measurable and continuously responsive. Gamblitude gave us that layer from the first day. We did not have to wait for our data stack to catch up with our ambition.”

Zak ManhireCEO, MINT

“MINT is a glimpse into where the industry is heading. Three products, crypto rails, a retention-first economy, a team that wants to make decisions in real time. That is exactly the environment we built Gamblitude for. Starting an operator on an AI-native data foundation removes an entire class of problems before they have a chance to form.”

Wojtek SznapkaCEO, GAMBLITUDE

Building a comparable analytics stack in-house is a multi-quarter engineering project. A credible team for an operator of MINT’s ambition would include a head of data, data engineers, analytics engineers, a small BI team, plus infrastructure and licensing costs. Realistic running cost sits in the six-figure range per year before anyone answers the first business question. Time to something usable is measured in quarters.

Starting on Gamblitude removed that line item from MINT’s early budget. The operator got an iGaming-specific data warehouse, semantic layer, AI Agent, predictive models and real-time monitoring for a fraction of what a comparable in-house build would cost, and in a fraction of the time. Capital that would have gone into a data team and a warehouse project was available for brand, content and player acquisition, which is where a new operator actually needs to move fast.

Quality matters here as much as cost. Gamblitude ships with metrics, attributes, segmentation patterns and ML models already built for iGaming. A generalist data team starting from scratch would spend months catching up to that baseline, and would still be maintaining it afterwards. MINT skipped the entire catch-up phase.

The impact of starting on Gamblitude was visible during MINT’s soft launch and early live testing. Because the platform was live before players arrived, the team never entered the usual discovery phase of trying to understand its own business.

“What struck us early on was how much ground we could cover with a small team. Every function had the same view of the business, the same definitions, the same real-time access. That changed the speed of decisions we made.”

Zak ManhireCEO, MINT

With no legacy reporting habits to unlearn, adoption was unusually fast. From the first week, people across product, operations and finance were using the platform as their primary way of interacting with business data. The AI Agent became the default entry point. Instead of opening a dashboard to look for a number, team members typed a question in natural language and received the answer, the chart and the explanation in one response.

MINT runs as a lean team with a flat operating model. Launching on Gamblitude allowed that team to behave like a much larger organisation without the overhead that usually comes with scale. Product decisions were grounded in behavioural data from week one, including signals specific to prediction-market style products and crash-style games. Compliance and risk ran on live visibility rather than retrospective reports, which matters in a crypto environment where velocity of funds and multi-account behaviour need continuous attention. Growth and affiliate workflows operated on predictive scoring from the first cohort rather than waiting months to accumulate enough history for meaningful models.

The net effect is that MINT never paid the tax that most new operators accept as inevitable. No legacy reporting layer, no drifting KPI definitions, no shadow spreadsheets waiting to be consolidated, and no team of analysts being hired to answer questions the platform can already answer itself.

The observation that stood out during the early months of operation came from prediction-market style products. No one in iGaming has a mature template for how these players behave, because the category is still taking shape across the industry. Using Gamblitude, the MINT team identified behavioural patterns that do not match either casino or sportsbook baselines. Prediction-market participants show different deposit patterns, a longer time horizon on individual positions, and an LTV profile that rewards different retention mechanics.

“We expected the platform to handle the basics well. What we did not expect was how quickly it would help us see things we did not know to look for. Prediction-market style products and crypto behaviour generate patterns that are still being learned across the industry. Having a foundation that surfaces them natively is a real advantage.”

Zak ManhireCEO, MINT

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