4. The Syncora Solution

Syncora exists to unlock the world’s most valuable data for AI, without breaking privacy rules, without exposing raw information, and with incentives that keep supply flowing.

We do this with two tightly connected products:

Synthetic Data Engine (SaaS) - already live, where enterprises and developers can create training-ready synthetic datasets in minutes.

On-Chain Data Hub (Web3) - our marketplace layer, where contributors earn royalties and buyers get verifiable datasets with provenance.

Together, they form the data access layer for decentralized AI.

4.1 Synthetic Data Engine (Self-Serve SaaS)

The engine is our first pillar. It gives any enterprise, researcher, or team a way to turn private data into safe synthetic copies, instantly.

How It Works

Upload: A dataset (medical tables, financial logs, images, JSON, time-series).

Validate: Our agents scan for schema consistency, anomalies, and potential privacy leaks.

Strip PII: Names, IDs, addresses, or anything identifiable are automatically removed.

Synthesize: Our generation models (GANs, transformers, diffusion) create a synthetic version that mirrors the statistical patterns of the original but contains no real records.

Score: The dataset is evaluated for fidelity (does it preserve signal?), coverage (are edge cases represented?), and novelty (no memorization).

Features

Multi-modal: Supports tabular, JSON, time-series, and imaging.

Benchmarked: 72% higher fidelity than Gretel; ~50% lower cost.

Privacy-safe: Originals deleted after synthesis; only synthetic copies remain.

Turnaround: Training-ready data delivered in minutes, not weeks.

Why It Matters

This solves Problem #1 (locked by compliance): Enterprises can finally use their own data, or partner data, without violating HIPAA, GDPR, or other frameworks. They never expose raw inputs.

4.2 On-Chain Data Hub (Contributor Marketplace)

The second pillar is our marketplace. It takes the engine one step further: instead of just a tool, it becomes an ecosystem where contributors and buyers meet, with trust anchored on-chain.

How It Works

Contributors Upload: A hospital, bank, or individual contributor uploads a dataset.

Agents Validate: Automated checks for ownership, quality, and compliance. (AI-generated or copyrighted content is rejected; misuse leads to slashing).

Synthesize: Data is turned into safe synthetic copies. Originals are immediately deleted.

License: Synthetic datasets are listed for buyers. Each carries provenance metadata on Solana, proof of source, validation, and synthesis.

Royalties: When a buyer licenses a dataset, contributors earn 80% of the revenue automatically, paid in $SYNKO. Syncora retains 20% as platform fee.

Features

Provenance Anchored: Every dataset carries an on-chain record of validation and synthesis.

Royalties: Contributors earn not once, but every time their dataset is resold.

Spam Resistance: Listing requires staking tokens; low-quality data gets slashed.

Compliance by Default: Originals deleted; synthetic only distributed.

Why It Matters

This solves Problem #2 (no ownership or royalties): Contributors finally have a system that recognizes and rewards them. It also solves Problem #3 (trust gap) for buyers, who now see verifiable provenance.

4.3 Data Bundling in Syncora

Syncora’s Data Hub is not a marketplace of scattered, individual files. Instead, every contributor upload, no matter how small, is validated, scored, and then aggregated into larger, domain-specific bundles that buyers can actually use for training AI models.

Contributor Uploads

Contributors submit datasets by selecting a domain such as healthcare, finance, or education. Each file is automatically tagged and processed by Syncora’s validation agents, which detect schema type (tabular, imaging, JSON, logs, etc.) and scan for compliance and ownership.

Aggregation & Bundling

Once validated, contributions are pooled with others of the same domain and schema.

Example: A 5 MB set of TB lab reports is grouped with thousands of other TB reports to form a large synthetic TB dataset.

Example: A hospital’s chest X-ray files are aggregated with similar imaging contributions to form a comprehensive chest X-ray dataset.

Through synthesis, these pooled contributions become training-ready synthetic bundles in the range of tens to hundreds of gigabytes, the scale required for meaningful model training.

Royalties & Incentives

Each contributor’s share of the royalties is proportional to their contribution size and quality score.

Higher-scoring datasets (fidelity, coverage, novelty) earn a larger portion of the bundle’s revenue.

Payments are made in $SYNKO, with 20% unlocked immediately and 80% vesting over five months, ensuring sustained engagement.

Why Bundling Matters

For contributors: Even small files (5 MB, 50 MB) matter, because they are pooled into much larger datasets where their contribution is fairly rewarded.

For buyers: They never license “one hospital’s files.” They only see high-quality, large synthetic datasets with provenance, suitable for training.

For Syncora: Automated bundling ensures scale, compliance, and trust without manual curation.

4.4 How Syncora Solves the Three Core Problems

Let’s map it back:

Locked by Compliance → Synthetic Engine

Enterprises can use and share synthetic datasets without violating HIPAA, GDPR, or financial secrecy.

No Provenance or Royalties → On-Chain Data Hub

Contributors earn perpetual royalties, and provenance is immutable.

Public Data Exhausted → Behavioral + Regulated Data Supply

By unlocking private silos, Syncora provides the rare edge cases and regulated data models desperately need.

4.5 Unique Differentiators

Agentic Automation

Syncora is built around autonomous agents that validate, synthesize, and score data with minimal human oversight. This makes it fast, scalable, and resistant to human error.

Bridging SaaS + Web3

Most synthetic platforms are Web2 SaaS (Gretel, MostlyAI).

Most Web3 data projects focus on personal sovereignty (Vana, Dria).

Syncora does both: enterprise-grade synthesis + contributor-reward marketplace.

Regulated Data Focus

Our sweet spot isn’t just social media or browsing history. It’s high-value, high-compliance data: medical, financial, enterprise behavioral logs. This is the hardest to unlock and the most valuable.

4.6 Case Studies in Action

Healthcare: A hospital uploads 10 years of sepsis patient data. Syncora generates a synthetic dataset, deletes the originals, and licenses the synthetic to multiple AI teams. The hospital earns royalties every time, and the AI models train on realistic patterns without touching PHI.

Finance: A bank provides fraud transaction logs. Buyers train fraud detection models on synthetic logs that preserve rare fraud events. Raw logs never leave the bank.

Enterprise: A SaaS company contributes error logs from customer interactions. Synthetic versions highlight rare anomalies. Contributors earn royalties; buyers get unique behavioral data.

4.7 Proof of Quality and Safety

Syncora’s outputs are backed by quantitative and compliance guarantees:

Fidelity Metrics: F1 score of 0.57 vs Gretel’s 0.33 (higher is better).

Privacy Metrics: NNDR 0.0003 vs Gretel’s 0.0100 (lower = safer).

Tests: Train-on-Synthetic/Test-on-Real (TSTR) benchmarks confirm utility.

Privacy Attacks: Membership inference and reconstruction tests run automatically to ensure no leakage.

4.8 Closing This Section

Syncora isn’t a theoretical idea. The engine is live, billing since June 2025. The Data Hub is live on devnet with over 1,200 contributors. Enterprises are already using the engine; contributors are already uploading.

The combination, synthetic engine + on-chain hub, is the missing link to unlock regulated, behavioral data at scale.

This is how Syncora becomes the data access layer for decentralized AI.