The Solution - Scannit Network

Scannit delivers a modular ecosystem that aligns the interests of individual contributors and enterprise data consumers. The platform combines intuitive micro‑interactions with rigorous validation, creating a transparent pipeline from data capture to utilization.

Core Components of the Scannit Network:

Questboard (Data Collection Layer)

• A gamified interface where users complete simple tasks (e.g., selfies, voice recordings, gestures, receipt scans) that generate valuable real-world data to power AI and robotics systems.

Data Buyers & Developers

• Companies and researchers seeking high-quality, user-consented data, with guaranteed provenance and ethical sourcing for training AI, market analysis, or product development.

Quest creation & data buyer portal

• Phase 1. Scannit’s internal data operations team designs and publishes quests based on enterprise demand.

• Phase 2 (roadmap). A self‑service web console will allow companies, researchers, and other stakeholders to launch quests or submit labeling and validation jobs. The portal enforces tiered quality gates, provides real‑time KPI dashboards, and records consent and usage rights on‑chain.

Built-In Quality Control

Scannit embeds multilayer safeguards to ensure trust and precision before any reward is issued or dataset released.

• Anomaly detection. Machine‑learning models flag suspicious patterns, including synthetic submissions and bot activity.

• Fraud prevention. Each contribution undergoes authenticity checks, from purchase‑receipt verification to biometric consistency reviews.

• KYC‑lite validation. Optional account linking (for example, an Open Banking connection) increases contributor credibility without exposing excess personal information.

• Peer consensus review. For selected quests, the system runs a two-step check: our AI pass produces an initial label, then a rotating pool of contributors reviews a random slice. Human reviewers confirm or overrule the AI; majority agreement locks the record, while mismatches route it back to the uploader for correction.

• Expertise credentialing. Optional verification of professional or academic credentials enables assignment of specialized, higher‑value quests while preserving personal privacy.

Confidence score

Every contributor has a dynamic confidence score.

• Positive signals; verified quest completions and clean peer reviews, push the score up.

• Negative signals; failed validations or ML-flagged anomalies, pull it down.

Example: A user submits ten receipt scans; all pass validation and peer spot-checks, nudging their score high enough to unlock premium quests that pay f. ex. 30 % more. Later, one mislabeled upload is flagged; the score dips, limiting access to these tasks until the user’s next verified submissions restore it.

The exact weighting formula remains undisclosed, but the principle is clear: consistent quality expands earning opportunities, while errors temporarily narrow them.

The Roadmap to AI-Driven Autonomy

Scannit will roll out functionality in carefully staged increments, ensuring stability and quality at each step.

Timeline	Milestone	Description
Q3 2025	Public app launch	Quest‑based rewards system is live for iOS and Android. Initial quest catalog curated by Scannit’s internal team.
Q4  2025	Testnet Release	Testnet release with $tSCAN: validating quests and network infrastructure at scale in a near-production environment.
Q1 2026	Self‑service quest portal (beta)	Selected enterprise partners gain the ability to design and deploy quests through a web console with built‑in quality gates.
Q3 2026	Decentralized storage integration (alpha)	Initial support for user‑owned data hosting via Solid Pods and Filecoin, maintaining existing consent controls.
Q4 2026	Automation and analytics backend	Rule‑based data packaging, delivery automation, and opt‑in AI‑driven optimization. Focus remains on back‑end efficiency rather than user‑facing agents.

This phased approach prioritizes immediate user value with the quest‑based model, while laying the groundwork for enterprise scalability, advanced contributor matching, and decentralized governance.

Growth starts with two reinforcing actions: onboarding contributors who supply high-quality, permissioned data, and onboarding enterprises that purchase it. Each completed quest increases the data pool; each satisfied buyer funds new quests, raising contributor earnings. $SCAN rewards ignite this loop and, over time, self-sustain demand for the token.