Delivering Layered AI + Human Support for Hypergrowth Platforms

Overview

Industry: Gaming / Rewards / Consumer Technology
Platform Type: Consumer Reward App (consistently ranked among the most downloaded apps in its category)
User Base: 100M+ users; 7m+ downloads; predominantly Gen Z, mobile-first, high-velocity support expectations
Engagement Scope: Tier 1 & 2 Customer Support, Financial Risk Investigations, AI Strategy
Channels Supported: Chat, Email, SMS, WhatsApp, Telegram
Operating Environment: High-volume, fraud-sensitive, payout-driven ecosystem
Core Challenge: Scaling trust, support, and risk operations alongside explosive growth

Among the platforms Hugo supports in this category, this case study focuses on one engagement.

The Client’s platform lets users earn money through gaming, surveys, and app engagement, with instant payouts via PayPal, crypto, and gift cards. It ranks among the most-downloaded consumer rewards apps globally, with a user base skewing heavily Gen Z.

The Challenge: Scaling Support + Risk + Trust Simultaneously

Support Gaps: Response times had stretched to 72 hours – eroding trust in a platform built on instant gratification. Users expected near-instant resolution across chat, WhatsApp, Telegram, and social.

Risk Complexity: As the platform scaled, so did the sophistication and volume of fraud, abuse, and payout disputes. Tier 2 support increasingly involved account verification, fraud review, and edge-case adjudication requiring contextual judgment and pattern recognition, not just scripted responses.

AI Limitations: The Client was already on Intercom, which has a built-in AI deflection tool (Fin), but it wasn’t enough. They’d invested further in additional AI-powered deflection, but those systems still struggled with rewards-specific edge cases: payout disputes, verification workflows, and offer-wall escalations. The Client believed more tickets could be deflected and that deflection would be significantly cheaper than human resolution. But the gap between what their AI layers could handle and what still required human intervention kept growing.

The Client needed a partner at the intersection of support, risk, and AI strategy – one who could work alongside their engineering team, not just execute tickets

Why Hugo

Deep AI operations background: Direct experience operating AI systems at scale, including annotation workflows, model feedback loops, AI QA, and operational tuning. This allowed the team to contribute not just to support execution, but to the design and optimization of the Client’s broader AI strategy.

Risk and T&S capability: Hugo already operated trust and safety, fraud review, and risk-adjacent workflows for other Clients and understood that on platforms handling real money, support and risk are the same conversation.

Comfortable building what doesn’t exist yet: The Client’s challenge didn’t have an off-the-shelf answer. Hugo’s track record of engineering operational and technical solutions in uncharted territory was a key factor in the selection.

Operational flexibility: Beyond scaling teams quickly, Hugo offered the kind of operational elasticity the Client needed with flex-up capacity, surge protection, and the ability to absorb volume spikes without degrading quality.

The Solution: Building Upon Multiple Layers of AI + Humans Where It Matters

Hugo worked alongside the Client’s product engineering team to build upon and extend the AI layers already in place, adding capability where gaps existed, collaborating on net-new solutions where no off-the-shelf tool could do the job, and attaching a human layer purpose-built for the complex, high-judgment work that AI shouldn’t handle.

Layer 1: Layer 1: Basic Transactional Resolution (Out-of-the-box AI)- Intercom Fin

The Client was already running Intercom’s built-in AI, Fin, as their first line of deflection. It handled straightforward, high-volume requests; account FAQs, basic payout status checks, and simple transactional queries. Fin was built for the least common denominator and can only go so far. Hugo helped the Client optimize this layer for cost efficiency and routing accuracy, ensuring it resolved what it could and cleanly escalated what it couldn’t.

Layer 2: Third-Party AI Agent (not from Hugo)

A more capable third-party AI solution sat atop Fin, using traditional API integrations to pull data from the Client’s CRM and knowledge systems. This layer handled ticket types requiring more contextual understanding, but still escalated a significant volume. Hugo worked alongside the Client to optimize routing into and out of this layer, identifying resolution gaps and reducing unnecessary escalation into human queues.

Layer 3: Advanced Agentic AI for Complex Ticket Resolution

Many of the Client’s internal systems didn’t offer APIs, or the Client didn’t want to extend access for security reasons. Hugo’s engineering team partnered with the Client’s to develop a computer vision-based agentic AI that operates across these systems through the UI, the same way a human agent would. Zero backend integration and capable of resolving up to 80% of escalated tickets.

Layer 4: Tiered Support Built for Gen Z

Hugo’s human support layer only handles what AI can’t: non-repeatable edge cases where no standard operating procedure exists, and resolution requires complex judgment, contextual sensitivity, or de-escalation.

The team was structured around two segmentation principles: route by ticket type, so that straightforward requests stay automated and complex disputes go to agents trained for them. And route by user value, so that the Client’s highest-value users always reach a human regardless of ticket complexity.

With a predominantly Gen Z, mobile-native user base expecting speed, personality, and directness, Hugo agents were specifically selected for their digital fluency, conversational speed, and the ability to resolve with precision in short-form, high-velocity channels.

The Outcome

“We were looking for a partnership to help us, and Hugo delivered. I’ve worked with many partners before, but the Hugo team has not just brought professionalism, but personality. Hugo has become a natural extension of our company.”

— Head of Support and Risk Mitigation

Similar Work Hugo Does

• Trust & safety and risk operations for consumer platforms
• AI deployment, tuning, and layered deflection optimization
• Agentic AI development alongside client engineering teams
• UI-based automation for CX and operational workflows
• Judgment-heavy support operations for high-growth platforms
• Premium and escalation-focused support environments
• Rapid operational scaling across complex workflows

Facing a similar challenge? Talk to Hugo