Illustrative User Journeys

How a single data request moves from boardroom concept to model improvement and contributor reward.

To ground the architecture in real-world activity, the following parallel narratives trace the same “Left-Hand Interaction” quest from the moment an enterprise defines the need to the moment a contributor earns $SCAN and sees tangible impact.

Enterprise Journey – “Left-Hand Interaction” Quest

A robotics company turns a hard bias problem into a one-week data sprint.

Phase	Description
1. Identify the gap	Lumina Robotics confirms that its vision model misclassifies scenes featuring left-handed grasps. Internal analysis shows the public web offers too few left-hand examples to correct the bias.
2. Design the quest	In Scannit’s portal the team launches “Show us your left hand,” requesting three-second phone videos of common objects lifted with the left hand. Clip specs, reward of 20 $SCAN, and total budget are set in a guided form.
3. Activate the network	The quest card appears in contributor feeds worldwide. Thousands of participants capture diverse clips in kitchens, cafés, and offices, rapidly filling the volume target with real-world variety.
4. Receive the dataset	When the quota and quality thresholds are reached, Scannit delivers MP4 files, JSON labels, and a full provenance log through an API endpoint. Compliance documentation is bundled automatically.
5. Measure the impact	Retraining with the new footage lifts left-hand grasp recognition by double digits. Marketing highlights the upgrade in the next release, and the data team earmarks Scannit for future bias-reduction tasks.

Contributor Journey – Earning Through the Same Quest

A single user turns an everyday gesture into rewards and recognition.

1. Discover the quest	Scrolling the Questboard, the contributor spots “Show us your left hand,” marked with a premium payout and a short example clip. The task is clear and the reward is visible in both $SCAN and fiat terms.
2. Qualify	A one-time micro-test asks the user to hold a pen in the left hand for two seconds. Passing the test unlocks the main quest and tags the profile as “Dexterity verified.”
3. Capture the clip	In the kitchen the user records a three-second video lifting a coffee mug with the left hand. Real-time feedback confirms framing and lighting before accepting the upload.
4. Collect the reward	Minutes later the submission passes validation and 20 $SCAN appear in the in-app wallet. A notification invites the contributor to repeat the quest with different objects for additional earnings.
5. See the impact	The dashboard later shows that Lumina Robotics purchased the dataset and that the clips helped cut grasp errors. A “Bias-buster contributor” badge unlocks access to upcoming high-value quests.

These mirrored journeys demonstrate Scannit’s core promise: precise enterprise data needs are met through transparent, well-paid micro-interactions, aligning incentives for both sides of the marketplace.

User Choice & Data Economics

From passive data exhaust to a marketplace where every contribution is a conscious, priced decision.

Scannit does not ask contributors to trust a black-box “privacy layer.” Instead, the platform makes every data exchange an explicit, priced opt-in. Contributors see what a quest requests, what it pays, and decide. Nothing more, nothing less.

Pillar	How it works in Scannit	Benefit to the contributor
Action-based consent	Data enters the system only when a user completes the task. No background scraping, no hidden trackers.	Participation stays intentional; digital life outside accepted quests remains untouched.
Up-front pricing	Each quest card displays a reward in points and after TGE directly in $SCAN.	Contributors learn the real market worth of their photos, receipts, or voice clips, passively educating themselves on data value over time.
Granular toggles, not blanket settings	Users can accept some quest types (e.g., receipts) and skip others (e.g., voice). Preferences are stored and surfaced when new quests launch.	Data sharing becomes a per-asset, effort-vs-payout decision rather than an all-or-nothing privacy checkbox.
Quality = higher earnings	A confidence score and optional credential badges (e.g., “Dexterity verified,” “Nutritionist certified”) unlock premium quests with better payouts.	Skill and reliability translate directly into income, mirroring real-world labor markets.
Voice in network rules	Token-weighted voting lets contributors approve fee tiers, quest-quality standards, and treasury grants, delegatable for those who prefer to stay hands-off.	Economic upside is paired with governance influence, aligning long-term interests.

By centering choice, price transparency, and progressive earning potential, Scannit replaces the opaque, passive data harvesting of Web 2.0 with a clear-sighted marketplace where individuals trade information on terms they set themselves.