When I was building the first version of Mustartlove, the question I returned to most often wasn't about growth or monetization or product-market fit. It was simpler and more urgent: how do we ensure that every person who comes to this platform feels genuinely safe?
That question shapes everything about how we build. Safety isn't a feature you bolt on after the product is complete. It isn't a compliance checkbox or a PR talking point. It is, or should be, the foundational design principle from which everything else flows. A platform where people don't feel safe cannot fulfill its core purpose, regardless of how elegant the interface or how sophisticated the matching algorithm.
This post is an honest account of how we think about safety at Mustartlove — what we've built, why we've built it, and where we're still working to improve. I'm writing it because I believe the industry needs more transparency about safety practices, and because our users deserve to understand the systems that are working on their behalf every time they open the app.
Safety-by-Design: What It Means and Why It Matters
The concept of "safety-by-design" originated in the hardware engineering world, where it describes systems designed to fail safely — to default to the least harmful state when something goes wrong. Applied to digital platforms, it means something analogous: building systems where the safe outcome is the path of least resistance, where potential harms are anticipated in design rather than addressed after they occur, and where the incentives built into the product architecture push toward user wellbeing rather than against it.
Most social platforms, including most dating apps, are not designed this way. They are designed for engagement first, and safety considerations are applied reactively — as content moderation responses to harm that has already occurred, as features added under regulatory pressure, as PR responses to incidents that become public. This approach generates a predictable pattern: harm occurs, becomes visible, generates pressure, and eventually produces a response. The users who experienced the harm in the meantime are, in the most meaningful sense, casualties of a design philosophy that didn't prioritize their protection from the beginning.
Safety-by-design inverts this. It starts with a threat model — a systematic assessment of the ways a platform could be used to harm users — and builds defenses into the architecture before those threats materialize. It treats safety investment as product investment, not cost center. And it measures success not just by harm rates but by user confidence: do our users feel genuinely safe? Do they engage with the platform in ways that suggest trust? Do they bring their authentic selves rather than defensive, guarded versions of themselves?
Identity Verification: Building a Foundation of Authenticity
The most basic safety question on any dating platform is: are the people here who they say they are? Catfishing, romance scams, and profile misrepresentation are endemic to platforms that don't address this question seriously. The harm they cause ranges from wasted time and emotional disappointment to devastating financial losses and in some cases genuine physical danger.
Mustartlove uses a tiered identity verification approach that balances security with accessibility. Every user on the platform completes phone number verification at registration — a basic but effective barrier against the throwaway accounts that enable many forms of harassment and fraud. From there, verification tiers escalate in both the assurance they provide and the access they unlock.
Photo verification — our second tier — uses a real-time liveness check combined with computer vision matching to confirm that a user's profile photos actually depict them. The process takes about 90 seconds: we ask the user to perform a series of random facial movements while the camera is active, then automatically compare the live capture against their profile photos. Users who complete photo verification receive a visible verification badge, and our internal data shows that verified profiles receive approximately 60% more connection requests than unverified ones — a signal that our users understand the value of verified identity and reward it in their interaction behavior.
Our third verification tier, available for users who want to signal maximum authenticity, uses document verification through a trusted third-party identity verification service. Users who complete this tier have their government-issued ID verified against their live identity, providing the highest available level of confidence that the person behind the profile is who they claim to be. This tier is optional, but we've seen organic adoption driven primarily by users who have had negative experiences with misrepresentation on other platforms and want to signal their own trustworthiness to potential matches.
AI Content Moderation: Speed, Scale, and Human Judgment
Content moderation on dating platforms faces a challenge that's distinct from other social media contexts: much of the potentially harmful content — inappropriate messages, unsolicited explicit images, harassing language — occurs in private one-to-one conversations rather than in public-facing feeds. This makes purely reactive, report-based moderation fundamentally inadequate. By the time a user reports harmful content, they've already been harmed by experiencing it.
Our AI content moderation system operates on a pre-delivery review model for high-risk content categories. Messages that match patterns associated with harassment, explicit content, threats, or financial solicitation are flagged for automated review before delivery and, in cases where automated confidence is below our threshold, held for human review. We process this flagging in under two seconds in the vast majority of cases, meaning that users in normal conversation experience no perceptible delay while our systems are working to protect them.
The technical foundation of our moderation system is a multi-layer classifier trained on a proprietary dataset combining public datasets of harmful content with platform-specific examples that reflect the particular patterns of harm we've observed on Mustartlove. We update the model quarterly with new examples identified through our human review process, which means the system continuously adapts to new tactics used by bad actors.
Photo moderation uses a similar approach. All images uploaded to the platform pass through automated screening for explicit content, with borderline cases reviewed by human moderators before publication. We've invested significantly in ensuring our image classifiers are accurate across diverse skin tones, body types, and demographics — a documented weakness of many commercially available content classifiers that we take seriously given our commitment to equitable safety for all users.
Behavioral Monitoring: Catching Patterns, Not Just Incidents
Some of the most harmful behavior on social platforms doesn't manifest in any single incident that would trigger content moderation. It emerges from patterns: rapid sequential messaging to many users, sending very similar messages to large numbers of people, quickly escalating conversations toward financial topics, requesting off-platform communication very early in conversations. These behavioral patterns characterize spam, scam operations, and certain forms of predatory behavior that content review alone cannot catch.
Our behavioral monitoring system tracks interaction patterns across the platform and flags accounts whose behavior deviates significantly from the baseline distribution we observe among genuine users. Flagged accounts are reviewed by our Trust and Safety team, who investigate and take action ranging from warning the user about platform policies to temporary suspension to permanent ban, depending on the nature of the violation.
We're careful to design behavioral monitoring in ways that don't create false positives that penalize legitimate users. Genuine users who reach out to many people, who use similar openers with multiple matches, or who prefer early video calls should not be penalized for behaviors that happen to superficially resemble those of bad actors. Our system is calibrated to identify the combination of signals that actually indicates harmful intent, not isolated behaviors that could have benign explanations.
Reporting Systems: Closing the Loop
No automated system catches everything. User reports remain an essential input to platform safety, and how a platform handles those reports is a significant indicator of how seriously it takes safety. On too many platforms, the reporting flow is a dead end — users can submit reports, but they receive no meaningful feedback and see no visible consequences. This erodes trust in reporting systems and ultimately reduces the safety benefit they're supposed to provide.
Mustartlove's reporting system is designed to close the loop with reporters. When a user files a report, they receive an automated acknowledgment within minutes confirming we've received it. If the report requires human review, we commit to completing that review within 24 hours for priority categories (harassment, threats, and explicit content) and 72 hours for other categories. Following review, the reporting user receives a notification indicating that we've taken action — though we don't share the specific action taken to protect the privacy of the reported user.
We publish quarterly safety reports that aggregate key metrics: report volumes by category, response times, action rates, and the percentage of reports that resulted in account suspension or ban. This transparency creates accountability and helps users calibrate their trust in our systems based on demonstrated performance rather than promises.
Privacy Controls: Your Data, Your Rules
Safety on a dating platform isn't limited to protection from other users. It also encompasses protection from the platform itself — from misuse of personal data, from visibility to unwanted audiences, from tracking and surveillance that users haven't consented to. This dimension of safety is often underemphasized in industry discussions that focus on interpersonal harm, but it matters enormously to our users, particularly those in marginalized communities for whom data exposure can have serious real-world consequences.
Mustartlove's privacy architecture gives users granular control over their visibility and data. Location is always approximate, never precise — we show general area, never specific address or neighborhood. Users control who can see their profile: everyone on the platform, only people in their interest groups, or only confirmed matches. Blocking is immediate and absolute: when you block someone, they cannot see your profile, cannot search for you, and see no indication that you exist on the platform.
We do not sell user data to third parties. We do not use location data for advertising purposes. We do not retain conversation data beyond 90 days unless users have explicitly saved conversations. Our data practices are described in plain language in our privacy policy — not buried in technical language designed to obscure rather than illuminate.
Inclusive Safety: Designing for Every User Group
A safety system designed with only the "average" user in mind will fail the users who face the most risk. LGBTQ+ users, particularly transgender and non-binary users, face safety challenges on dating platforms that differ significantly from those faced by straight cisgender users. Users with disabilities may have different vulnerability patterns. Users from different cultural backgrounds may face specific forms of discrimination and harassment. Women and non-binary users face patterns of harassment that are quantitatively and qualitatively different from those faced by men.
Designing for inclusive safety means researching these distinct threat landscapes and building specific features to address them. Our LGBTQ+ safety features include granular controls over identity disclosure — users can choose exactly which aspects of their identity are visible to whom, enabling them to be fully out within trusted communities while maintaining privacy in less safe contexts. We have built specific classifiers for the forms of transphobic and homophobic language that appear in harassment directed at LGBTQ+ users. And we have an explicit anti-discrimination policy that applies to all users regardless of the platform's matching features.
For all users, we've worked to design flows that don't re-traumatize people who have experienced harm. Reporting interfaces shouldn't feel like interrogations. Privacy settings shouldn't require legal expertise to understand. Safety features should be accessible from any point in the interface, not buried in settings menus. These are the principles of trauma-informed design applied to platform safety.
Trauma-Informed Design: Safety That Doesn't Re-Harm
Trauma-informed design is a framework developed in healthcare and social services contexts that has increasing relevance to digital product design. Its core insight is that people who have experienced trauma — including the kind of harassment, manipulation, or violation that many dating app users have experienced — have physiological and psychological responses to triggers that can make standard product interactions feel unsafe or retraumatizing.
For us, this means several concrete things. Our onboarding asks users about their safety preferences in a supportive rather than procedural tone — acknowledging that people come to dating platforms from different experiences and that those experiences deserve respect. Our reporting flows are designed to be low-friction and empathetic: we ask users what they need, not just what happened. Our moderation decisions are communicated in ways that feel protective and affirming rather than bureaucratic.
We've brought in advisors with expertise in trauma-informed care to review our product decisions, and we've made this perspective a standing part of our product review process. Every time we build a new feature that involves user interactions, safety implications are reviewed through a trauma-informed lens before we ship.
Our Ongoing Commitment
I want to be honest about what we don't yet have right. Our moderation system makes mistakes — both false positives that affect legitimate users and false negatives that let harmful content through. Our response times for lower-priority reports don't always meet the standards we aspire to. Our verification tiers, while meaningful, don't eliminate all forms of misrepresentation. Building a genuinely safe platform is not a problem you solve once. It is an ongoing commitment that requires continuous investment, learning, and improvement.
What I can commit to is that we will keep investing in safety with the same seriousness we invest in features that drive growth. That we will keep publishing our safety metrics so users can hold us accountable. That we will keep listening to users who have been harmed by gaps in our systems, and that we will let their experiences drive our improvement priorities. And that we will never sacrifice user safety for engagement or revenue metrics.
That commitment is the foundation that makes everything else we're building at Mustartlove possible. A platform where people feel genuinely safe is a platform where they can be genuinely themselves — and that authenticity is what makes real connection possible.