A Top Dating Platform Turned 40,000 Tickets Into Support AI

One of the world's largest dating platforms, home to one of the industry's most respected data science teams, faced a high-volume support cost center and an open question: could LLMs automate first-line support without the brand damage of an AI saying something unsafe on a dating app? The bar was double-sided: answers had to be accurate, and the system had to stay composed when users asked it anything at all. The platform also wanted to understand whether AI belonged in the dating experience itself.

In a 12-week first phase, Lazer built an AI support chatbot POC trained on more than 40,000 real support tickets, deployed in Lazer's own environment for controlled internal testing. Evaluation was designed in from the start: accuracy tracking plus explicit brand-risk metrics measuring how the model behaved under adversarial or random questioning. A customizable default prompt gave the platform direct control over the agent's voice and boundaries, a decision that particularly impressed senior stakeholders.

Phase 2 prototyped "dating copilot" concepts: conversation assistance, opener generation, and profile-browsing interaction patterns, validating the direction the platform's internal team needed to take to bring AI into the core product. On the strength of both phases, the engagement expanded into an innovation sprint with additional Lazer engineers embedded directly with the platform's data science team as the LLM experts in the room, with full code handoff to the internal trust and safety team.

Lazer scoped the work as a 12-week first phase, starting from the platform's core question: could an LLM handle first-line support without saying something unsafe on a dating app. Rather than optimize for accuracy alone, the team designed evaluation in from the outset, defining brand-risk metrics that measured how the agent behaved under adversarial or random questioning alongside standard accuracy tracking. They assembled a retrieval corpus from more than 40,000 historical support tickets and stood the chatbot up inside Lazer's own hosted environment, so testing could run internally under controlled conditions before any customer exposure. A customizable default prompt was added to give the platform direct control over the agent's voice and boundaries. Working through gradual data access in an uncertain environment shaped much of the engineering. A second phase then prototyped "dating copilot" concepts, exploring conversation assistance, opener generation, and profile-browsing patterns. As the phases progressed, additional Lazer engineers embedded with the platform's data science team, with full code handed off to the internal trust and safety team.

Consumer platforms with high-volume support operations and high brand sensitivity, where an AI failure is a screenshot away from a PR problem.