TAG Media and Gamblitude have launched affie.ai, a new decision-making platform built specifically for affiliate managers in iGaming. The product combines over 150 years of cumulative affiliate expertise with a dedicated analytics layer designed for real operational use.
The platform will be officially presented at SBC Summit Malta (28–30 April), where selected operators will have the opportunity to become one of the first five launch partners.
A Structural Problem in Affiliate Management
Affiliate management remains one of the most commercially critical areas for operators, yet it is still largely driven by experience rather than structured systems.
Managers are expected to balance CPA efficiency, revenue share deals, partner quality and long-term value, often without consistent frameworks or tools. While operators have access to large volumes of data, interpretation is fragmented and highly dependent on individual expertise.
This creates a gap:
- Experienced managers rely on intuition and pattern recognition
- Less experienced teams struggle with decision consistency
- Even senior professionals lose time navigating complex data environments
The result is slower decisions, higher risk of mispriced deals and missed optimisation opportunities.
From Data to Decisions, Not Dashboards
affie.ai positions itself differently from traditional affiliate tools. It is not a tracking platform or reporting layer. It is a guided decision-making engine.
The system is built on:
- Real affiliate programme data
- Frameworks developed across 50+ programme launches
- A dedicated iGaming data layer powered by Gamblitude
This matters because most tools stop at showing numbers. affie.ai goes one step further and interprets them.
Instead of asking:
“What is happening?”
The platform answers:
“What should I do next?”
Embedded Analytics, Not Additional Work
Gamblitude provides the analytical backbone through its cloud-native infrastructure, designed specifically for iGaming scale and complexity.
This means:
- Data is unified and consistent across the organisation
- KPIs are aligned through a single semantic layer
- Insights can be generated in seconds instead of days
affie.ai sits on top of this layer and translates raw data into actionable recommendations around:
- Deal structures
- Partner performance
- Programme optimisation
- Commercial risk
The goal is not more analysis. The goal is faster and better decisions.
Reducing Dependency on Individual Expertise
One of the key challenges in affiliate teams is knowledge concentration. Performance often depends on a small number of experienced managers.
affie.ai addresses this by:
- Standardising best practices
- Embedding expertise into workflows
- Making insights accessible across the team
This reduces onboarding time, improves consistency and helps retain institutional knowledge.
Supporting Relationships, Not Replacing Them
Affiliate management remains a relationship-driven business. The value of affie.ai is not automation for its own sake, but removing uncertainty from decisions.
By providing clear, data-backed guidance:
- Negotiations become more structured
- Decisions are easier to justify
- Managers can focus on partner relationships instead of internal debates
This aligns with a broader shift in iGaming, where AI is not replacing human roles but augmenting them with decision-grade intelligence.
Limited Early Access
To support the rollout, affie.ai is offering a 6-week trial for 5 operators, with no fees and minimal onboarding requirements.
Operators can connect their affiliate data and start using the platform immediately, testing its impact on decision speed and programme performance.
Why This Matters
Affiliate remains one of the largest acquisition channels in iGaming, but also one of the least standardised.
affie.ai introduces a new category:
decision intelligence for affiliate management
In a market where margins are shaped by small optimisation gains, the ability to make faster and more consistent decisions can translate directly into measurable revenue impact.
And that is where this product positions itself.