How are fake profiles detected?

Profile photos and text are assessed together as a single case, rather than one field at a time. Models flag images that are inconsistent with the rest of the profile and language patterns typical of scripted scam accounts. Detections are scored, so clear fakes are removed automatically and uncertain ones reach a moderator with the whole profile in view.

Can Checkstep detect grooming and sextortion?

Yes. These harms appear across a conversation rather than in a single message, so the case sent for assessment includes the surrounding exchange and any images shared in it. Example-driven models can be trained on cases your own team has confirmed, so the patterns your platform actually sees are recognized. Confirmed child sexual abuse material is reviewed by a person and reported to a child-safety tip line, as the law requires.

How does age assurance work, and what does it ask of users?

Checkstep offers age estimation from a profile image, and integrates end-to-end age and identity verification where a harder check is needed. You decide which applies and when, so most users are estimated passively and only those who fail the estimate or cross a risk threshold are asked for anything explicitly.

What happens to a user after they are reported?

The report becomes a case in a moderator queue, prioritized by severity. The moderator sees the reported content and that user's history, then approves it, removes the content, or acts on the account with a suspension or a ban. Every enforcement records the policy that was broken, which is what lets you tell the user why and offer them an appeal.

How quickly can a new harm type be added?

Usually quickly, because most new harms are a policy change rather than a new model. You describe the behavior as a prompt-driven rule, or supply examples of real cases, and it applies to incoming content without waiting for retraining. AI agents working your queues pick up a policy change immediately.

Content Moderation for Dating Apps

Safer matches, real people, trusted conversations

A safe space for relationships to flourish. Keep your dating platform free from abuse, harassment and catfishing.

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Online dating, real world dangers

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Every single day, online dating platforms deal with challenges ranging from harassment and bullying to serious threats like child exploitation and catfishing. These behaviors put individuals at risk of emotional and financial harm while also eroding the trust that lets your community thrive.

Left unresolved, these dangers can quickly escalate into user churn, legal issues and reputational damage. To safeguard both users and brand integrity, dating apps need effective and proactive moderation solutions.

55%

of daters have experienced some form of sextortion, abuse, or other problem

Benefits

The best relationships start with trust and safety

With AI-powered moderation, you can provide a safe and enjoyable platform for your daters, free from harmful and unwanted content and bad actors.

Real-time profile and chat decisions

Sub-50ms decisions on profiles, photos and chat, so bad actors are caught before a match is made.

Catfishing and sextortion detection

Photo matching, behavioral signals and metadata consistency expose fake profiles and coercion patterns, not just banned words.

Age assurance under the OSA

Verify age and identity to meet Online Safety Act duties, without adding friction that costs you sign-ups.

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Your partner for safer online dating

Our AI content moderation solutions reduce harmful behaviour and build the trust that keeps your users engaged and coming back.

Automate moderation to let human moderators focus on what matters most

Send ‘suspicious content’ to our sophisticated AI reasoning model, Advanced ModBot, to take decisions on content. Updates to your policies are immediately learned by the bot. Keep humans in the loop and allow the bot to escalate when it’s not sure.

Streamline case management from flag to resolution

Manage flagged profiles, photos and conversations in one central place with full visibility into violations. Case management gives your team clear context, policy alignment and actionable options - so you can review, escalate, or resolve cases quickly and confidently.

Create custom detection models in minutes, not months

Our technology-forward approach allows you to create custom models to detect what matters to your platform - in a matter of minutes. Gone is the need to wait months for a machine learning team to train a model from scratch.

Take user-level action to ban or suspend repeat offenders from your platform

Moderate at a content or user level to keep your platform safe from bad actors and catfishes. Checkstep tracks a user’s history with your platform, flagging past violations and offences, letting you make a decision on whether to remove just the content - or the user themselves.

Explore platform

“Checkstep's expertise in Trust and Safety is second to none. Their understanding of our needs from day 1 has helped us streamline our operational efficiency.”

123 Multimedia
Digital media holding owning two dating platforms, Tchache and Babel

FAQs

Most frequently asked questions about Checkstep for dating apps

Learn more about our AI content moderation platform

How are fake profiles detected?

Profile photos and text are assessed together as a single case, rather than one field at a time. Models flag images that are inconsistent with the rest of the profile and language patterns typical of scripted scam accounts. Detections are scored, so clear fakes are removed automatically and uncertain ones reach a moderator with the whole profile in view.
Can Checkstep detect grooming and sextortion?

Yes. These harms appear across a conversation rather than in a single message, so the case sent for assessment includes the surrounding exchange and any images shared in it. Example-driven models can be trained on cases your own team has confirmed, so the patterns your platform actually sees are recognized. Confirmed child sexual abuse material is reviewed by a person and reported to a child-safety tip line, as the law requires.
How does age assurance work, and what does it ask of users?

Checkstep offers age estimation from a profile image, and integrates end-to-end age and identity verification where a harder check is needed. You decide which applies and when, so most users are estimated passively and only those who fail the estimate or cross a risk threshold are asked for anything explicitly.
What happens to a user after they are reported?

The report becomes a case in a moderator queue, prioritized by severity. The moderator sees the reported content and that user's history, then approves it, removes the content, or acts on the account with a suspension or a ban. Every enforcement records the policy that was broken, which is what lets you tell the user why and offer them an appeal.
How quickly can a new harm type be added?

Usually quickly, because most new harms are a policy change rather than a new model. You describe the behavior as a prompt-driven rule, or supply examples of real cases, and it applies to incoming content without waiting for retraining. AI agents working your queues pick up a policy change immediately.