Data Overload Meets AI: Inside Gamblitude’s Bid to Rewire iGaming Analytics

When former STS chief technology officer Wojtek Sznapka and ex-chief sportsbook officer Piotr Cerlak resigned within four weeks of each other this spring, most assumed they would resurface at a rival tier-one operator. Instead, they quietly registered Gamblitude Sp. z o.o., a start-up that wants to turn the betting industry’s fast-growing mountain of data into something analysts, traders and product managers can interrogate in plain English — or let an AI agent do it for them.

From CTO & CSO to Founders

Sznapka spent seven years engineering STS’s shift to a cloud-native stack and bedding-in the 2020 Betsys acquisition; Cerlak oversaw odds-compiler automation that cut in-play latency below a second. Both cite the same frustration: “weeks-long queues for basic data questions,” Sznapka wrote in a recent LinkedIn post, announcing Gamblitude’s goal of “no queues, no code, no friction.” Cerlak echoed the theme: “Much of the effort still goes into finding numbers, not learning from them.”

The pair now split ownership of a Warsaw-based private company incorporated on 23 May with modest start-up capital. An external seed round is “under discussion,” but for now the venture is self-funded.

What the Product Actually Does

Gamblitude pitches itself as a warehouse-native platform that sits directly on Snowflake, BigQuery, Redshift or Postgres — no duplicate copy of the data, no separate CDP. Three layers do the work:

Layer	What Happens Under the Hood	Typical User
AI Query	A bespoke LLM trained on a decade of betting schemas converts English prompts into SQL and enforces the operator’s existing IAM policies.	Business analysts & execs
Insight Boards	A Kafka stream ingests live bet-slips, CRM triggers and payment events, surfaces anomalies, then runs what-if simulations on margin, risk or bonus ROI.	Trading, product & risk teams
Predictive Studio	Pre-tuned churn, LTV and fraud models launch from a notebook-style environment; results can trigger Optimove or Salesforce via webhook.	Data scientists & CRM teams

Private-beta access opens in July for three unnamed European sportsbooks; a full public reveal is slated for SBC Summit Barcelona in September.

Why It Matters — Beyond the Hype

Data volumes are ballooning. A well-tuned Kafka pipeline can push 8–10 TB of records per day at large operators, according to recent capacity-planning posts from Apache committers.

Legacy BI is creaking. Seventy-one per cent of firms now report scalability gaps in their current analytics tooling, while 78 per cent struggle to hire the people to bridge them, says the State of BI 2025 survey.

Regulators want near-real-time oversight. UKGC changes rolling out between August 2024 and February 2025 will require operators to surface player-spend and risk data as it happens, not days later.

Together, those threads create a clear commercial opening for any supplier able to collapse “data-to-decision” lag from hours to minutes.

Competitive Reality Check

Company	Focus	Missing Piece Gamblitude Hopes to Fill
Future Anthem — Amplifier AI	Real-time personalisation & bonusing	No open-ended BI querying for finance or risk.
Optimove	CRM marketing orchestration	Relies on its own customer-data platform; limited visibility into raw bet data.
Genius Sports / Sportradar dashboards	Odds/risk visualisation	Primarily prescriptive; little self-service exploration.

Whether operators want a single horizontal layer — or prefer best-of-breed vertical tools — remains the open question.

Challenges and Unknowns

Integration friction. Warehouse-native designs assume clean data pipelines, yet many mid-tier brands still juggle on-prem MySQL shards, FTP drops and legacy CRM extracts. Stitching these into a real-time Snowflake or BigQuery feed can take months of data-model triage and schema mapping.

Cost-overrun risk. Usage-based warehouses charge by compute-seconds or terabytes scanned; a poorly written ad-hoc query against 200 TB of logs can rack up thousands of euros in minutes. BigQuery, for instance, lists $5/TB processed for on-demand queries, a pricing model many operators find hard to forecast.

Data-sovereignty puzzle. EU jurisdictions such as Germany and the Netherlands now expect gambling data to reside in-country or in accredited EU regions. Operators embracing multi-cloud may need geo-fenced replicas and legal opinions before they can even pilot a new AI layer.

Hallucination & governance risk. An LLM that invents a column or mis-joins a table could produce misleading player-loss figures. Regulated industries are experimenting with multi-model “consensus” checks and automated SQL validation to curb hallucinations, but standards are still emerging.

Black-box anxiety. Even when outputs are accurate, trading and compliance heads want deterministic SQL for audits. Gamblitude will have to expose query lineage and row-level security in a form regulators recognise.

Runway pressure. The company is bootstrapped for now. If conversion from beta to paid license slips, founders face the classic start-up squeeze: raise on imperfect metrics, or trim roadmap ambitions.

The Broader Trend — Where This Goes Next

Conversational agents eclipse dashboards. Enterprise appetite for natural-language interfaces is no longer speculative: Accenture’s 2024 study found 74 % of organisations already seeing returns on gen-AI, and Gartner now expects 80 % of companies to embed AI chat or agents in core workflows by year-end; the AI-agent market itself is forecast to top $103 billion by 2032. For betting operators, the practical upshot is that a Saturday-night product lead will expect to type (or speak) a margin question and get a governed answer immediately, without scrolling through nested dashboards.

Warehouse-native arms race. Mainstream BI vendors — Sigma, Looker, Tableau and ThoughtSpot — are releasing vertical “accelerators” for sports and entertainment analytics to defend share against search-driven newcomers. Their marketing stresses the same theme Gamblitude is built on: keep data in Snowflake or BigQuery, execute the logic in place. That convergence suggests operators will soon evaluate “warehouse-first” as a hygiene factor, not a differentiator.

Regulation forces real-time transparency. The EU’s Digital Services Act, Accessibility Act and forthcoming AI Act converge on one requirement: auditable, explainable data flows. Gambling platforms must track algorithmic decisions, moderate user-generated content and publish transparency reports — obligations that are easier to satisfy when every fact lives in a single governed warehouse rather than scattershot extracts.

Capital and consolidation. IDC pegs enterprise AI spend at $307 billion next year, rising to $632 billion by 2028. With that war-chest, expect CRM stacks, data-feed giants and cloud hyperscalers to acquire warehouse-native agent technology rather than build it from scratch. If Gamblitude lands even a handful of tier-one clients, a sale to an incumbent hungry for agentic IP looks more probable than not.

Gamblitude arrives at a moment when data growth, regulatory pressure and talent scarcity are converging. The founders’ STS pedigree buys credibility, but success will hinge on proving that a warehouse-native AI layer can slot into messy operator estates and satisfy auditors. If they pull it off, the start-up could help shift the industry conversation from “build another dashboard” to “ask the warehouse anything.”