Use cases — Authentiq

01

Marketplaces & gig platforms

One real person behind every seller, driver or tasker account.

The problem: Banned sellers come back under new names, and one person runs a fleet of accounts to game ratings, promotions and payouts.
Silent, every visit: Logins, listings and messages are scored in the background. Scripted sign-ups and repeat devices are stopped without a prompt.
Ask for proof when: A new seller account goes live, or payout details change.
What real users see: One face check when they open their account. Routine logins stay untouched.

02

Real people in the conversation, not bot rings and sockpuppets.

The problem: Bot rings, sockpuppet accounts and AI agents post, vote and message at a scale no moderation team can keep up with.
Silent, every visit: Automation and AI-agent checks run on every session, so most scripted accounts never get to post.
Ask for proof when: A new account starts posting at volume, or wants to message people it doesn’t know.
What real users see: Most members never see a check. Active members prove it once and keep posting.

03

Bonuses for new humans, not for one person with ten accounts.

The problem: Sign-up bonuses, referral credits and promo codes get farmed by the same people over and over, across fresh accounts and devices.
Silent, every visit: Repeat devices, shared networks and bursts of sign-ups are caught in the background.
Ask for proof when: A bonus is claimed, or a referral pays out for the first time.
What real users see: One check before their first reward. Everything after that just pays out.

04

Real, unique people behind every completed survey.

The problem: Survey farms, AI agents and one person answering as many respondents push bad data into studies that look complete.
Silent, every visit: Every survey entry is checked for automation and against the same person’s earlier entries.
Ask for proof when: Someone enters a high-value or hard-to-fill study, or earns their first incentive.
What real users see: Verified once, then straight into studies without repeat checks.

05

Fair queues and fair play: one human, one seat.

The problem: Scalper bots flood drops and queues, and players run extra accounts to farm rewards or dodge bans.
Silent, every visit: Automation and bursts of traffic are filtered out of queues before they take a place.
Ask for proof when: A high-demand drop opens, or a player enters ranked play or a tournament.
What real users see: One check before their first big drop. Queues stay fast after that.

06

Profiles backed by a real person who showed up, live.

The problem: Fake profiles, romance scams and catfishing wear down trust faster than reports can remove them.
Silent, every visit: Every session is checked for automation, and for devices and networks that don’t fit the profile.
Ask for proof when: A profile goes live, or asks to unlock messaging.
What real users see: One check when their profile goes live. After that, nothing until something changes.

07

Training data from many real people, not one person counted many times.

The problem: One person works a stack of annotator accounts, verified accounts get rented out, and AI agents fill in tasks meant for people. The model learns one person’s habits as everyone’s.
Silent, every visit: Every work session is checked for automation and AI agents, for one device behind many accounts, and for an account that suddenly works from a new device and network.
Ask for proof when: An annotator joins a project, or their account shows up on a new device.
What real users see: One check when they join. After that, they just label.