If you could choose the metal of your first chain, what would it be?

Gold, copper, aluminum, steel, zinc...?

Please write your choice below 👇
https://x.com/Orecast_RWA/status/1950163731100447027

We have currently fed over 1,500 structured data points into our training process, covering mine production, warehouse incoming cargoes, refinery logs, and historical price fluctuations.

The AI model is learning how metals behave in the real world before being tokenized.

Some signals are stabilizing.

Forecast accuracy is improving.

The validation logic is becoming clearer.

We are making rapid progress.

We're committed to securing the tokenization of real-world metals, which means building protections at every level.

Encrypted Transfers

Warehouse Verification

Third-Party Audits

Multi-Signature Custody

Before any asset is put on-chain, we verify the following key aspects:

Is the price data accurate?

Is the inventory real and accounted for?

Has the transaction actually been executed?

We verify the data, not just the blocks.

Because true metal tokenization requires proof of authenticity.

Before any metal gets extracted, its value is often trapped in reports and land.

We’re building a system to unlock that value.

Verified sites
Proven reserves
Transparent pricing
On-chain contracts

Orecast operates across 12 modules, each closely related to how metals circulate in the real world:

1. Inbound: Recording warehouse and inspection data

2. Reserves: Verifying inventory quantities

3. Inventory: Tracking remaining inventory

4. Pricing: AI-powered updates every 60 seconds

5. Custody: Confirming asset ownership

6. Tokenization: Minting 1:1 collateral units

7. Collateral: Registering locked assets

8. Trading: Matching and clearing orders

9. Auditing: Building a traceable historical record

10. Connectivity: Connecting to DeFi protocols

11. Risk: Identifying unusual signals

12. Reporting: Compliance output

Every unit of metal starts its journey long before tokenization in dusty mines, busy roads, industrial zones, and storage vaults.

What matters isn’t just the metal.
It’s the data that proves where it came from, how it moved, and what it’s worth.

That’s the layer we’re building.

A verifiable link between matter and markets.

We intend to implement a task-based reward system where all inputs follow a clear path:

→ Task claim
→ Work verification
→ Sign and submit
→ Ranking by quality

Rewards are derived not from noise but from proven value.

Orecast's AI captures real-time pricing signals, cross-validates them with inventory updates, and routes them to the execution engine within 60 seconds.

Inventory location, custody, and valuation are synchronized across APIs, UIs, and on-chain calls.

We are validating real-time inventory synchronization between simulated warehouse endpoints.

Update interval: Stable at 58-62 seconds
Data mismatch rate: Less than 0.12% over 681 test cycles

During a controlled stress test, we simulated 50+ coordinated data injection attempts targeting our price and inventory streams.

1. Detection time: 2.4s
2. Containment: 100% of invalid entries blocked
3. Data integrity: 99.98% consistency maintained across modules

Commodity markets can flip in milliseconds, leaving static time series models lagging.

An adaptive AI engine cross-references live price feeds, logistics telemetry, and verified reserve data—recalibrating every 0.8s with <0.25% deviation.

Commodity prices move on multiple clocks.

Our system reads all of them

▪️︎ Minutes: capture high-frequency shocks in supply & demand

▪️︎ Hours: map the daily flow of trades

▪️︎ Weeks: identify structural turning points in inventory & pricing

By linking these horizons, we turn raw volatility into signals that drive real-world execution.

In a recent load test, we introduced malformed trade orders into the execution queue to test fault isolation.

Identification: 1.7s
Isolation success rate: 99.94%
Impacted trades: 0