> For the complete documentation index, see [llms.txt](https://docs.scannit.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.scannit.io/overview/illustrative-user-journeys.md).

# Illustrative User Journeys

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.

<table><thead><tr><th width="120.26171875">Phase</th><th>Description</th></tr></thead><tbody><tr><td><strong>1. Identify the gap</strong></td><td>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.</td></tr><tr><td><strong>2. Design the quest</strong></td><td>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.</td></tr><tr><td><strong>3. Activate the network</strong></td><td>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.</td></tr><tr><td><strong>4. Receive the dataset</strong></td><td>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.</td></tr><tr><td><strong>5. Measure the impact</strong></td><td>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.</td></tr></tbody></table>

### Contributor Journey – Earning Through the Same Quest

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

<table data-header-hidden><thead><tr><th width="125.23046875">Phase</th><th>Description</th></tr></thead><tbody><tr><td><strong>1. Discover the quest</strong></td><td>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.</td></tr><tr><td><strong>2. Qualify</strong></td><td>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.”</td></tr><tr><td><strong>3. Capture the clip</strong></td><td>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.</td></tr><tr><td><strong>4. Collect the reward</strong></td><td>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.</td></tr><tr><td><strong>5. See the impact</strong></td><td>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.</td></tr></tbody></table>

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.

<table><thead><tr><th width="143.24609375">Pillar</th><th>How it works in Scannit</th><th>Benefit to the contributor</th></tr></thead><tbody><tr><td>Action-based consent</td><td>Data enters the system only when a user completes the task. No background scraping, no hidden trackers.</td><td>Participation stays intentional; digital life outside accepted quests remains untouched.</td></tr><tr><td>Up-front pricing</td><td>Each quest card displays a reward in points and after TGE directly in $SCAN. </td><td>Contributors learn the real market worth of their photos, receipts, or voice clips, passively educating themselves on data value over time.</td></tr><tr><td>Granular toggles, not blanket settings</td><td>Users can accept some quest types (e.g., receipts) and skip others (e.g., voice). Preferences are stored and surfaced when new quests launch.</td><td>Data sharing becomes a per-asset, effort-vs-payout decision rather than an all-or-nothing privacy checkbox.</td></tr><tr><td>Quality = higher earnings</td><td>A confidence score and optional credential badges (e.g., “Dexterity verified,” “Nutritionist certified”) unlock premium quests with better payouts.</td><td>Skill and reliability translate directly into income, mirroring real-world labor markets.</td></tr><tr><td>Voice in network rules</td><td>Token-weighted voting lets contributors approve fee tiers, quest-quality standards, and treasury grants, delegatable for those who prefer to stay hands-off.</td><td>Economic upside is paired with governance influence, aligning long-term interests.</td></tr></tbody></table>

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.