Transactions and receipts
An online store generates an unpredictable receipt reference and links it to the order in its protected transaction system. The random output can be verified separately from the payment record.
VERIFIABLE RANDOMNESS
Random outcomes. Verifiable by design.
Built on Sui, with Solana as a fallback network, PlantMe’s Verifiable Randomness API uses on-chain VRF to generate random results with cryptographic proofs your application can verify.
Unpredictable before generation. Verifiable afterwards.
Cryptographically secure randomness keeps results unpredictable before they are generated. Verifying the proof detects altered or fabricated outputs, giving people evidence of fairness instead of relying on an operator’s promise.
From request to verification
Request
Your application requests a random result.
Generate
A verifiable random function produces a result and a cryptographic proof.
Verify
Verify the proof before using the result in your application.
Real-world scenarios
Examples of how applications can use verifiable randomness across eight fields.
Voting systems
An election application generates random ballot references. Voter eligibility, ballot secrecy and accurate counting are handled by the election system, not the random number.
Opinion polling
A polling team selects respondents from an eligible panel using a documented random sampling method. The result and proof help make the selection reproducible.
Public policy and citizen feedback
A city selects participants for a citizen feedback panel from an opt-in pool. It fixes eligibility and selection rules first, then uses verifiable randomness for the draw.
Document verification
A training provider assigns a random certificate reference and binds it to an issuer-signed record. The issuer’s signature establishes authenticity; randomness supplies the reference.
Supply chain management
A warehouse randomly selects batches for quality checks. Auditors can verify the random selection against a fixed batch list, while the inventory system maintains traceability.
Lotteries and raffles
An event organizer closes entries before requesting randomness, then selects winners using published rules. Participants can check the proof and how the result maps to eligible entries.
Medical records and prescriptions
A pharmacy system generates an unpredictable prescription reference inside its protected workflow. Patient details stay in the clinical system; a randomness proof does not establish prescription validity.
These are illustrative integration scenarios, not customer deployments. Your application manages record integrity, identifier uniqueness, eligibility and privacy; the API supplies verifiable randomness.
Where fairness needs evidence
Draws and raffles
Example
A community giveaway selects three winners from a fixed list of eligible entries.
Application flow
Close entries and fix the selection rules before requesting randomness. Verify the proof, then use the result with an unbiased selection method to choose winners without duplicates.
What can be checked
Record the eligible-entry list, selection rules, random result and proof so participants can check how the winners were chosen.
Games and rewards
Example
A game assigns a reward from a published table of items and probabilities.
Application flow
Fix the reward table before the request. Verify the returned proof, map the random result to the published probabilities and record the awarded item.
What can be checked
Players can check the proof and the mapping to the reward table. Published odds, request handling and preventing selective retries remain application responsibilities.
Random sampling
Example
An audit team selects a subset of records for inspection, or a researcher selects participants from an eligible group.
Application flow
Fix the eligible population and sample size before requesting randomness. Verify the proof and use a documented sampling method to select the sample.
What can be checked
Keep the population definition, method, result and proof for reproducibility. Random selection alone does not make an incomplete or biased population representative.
A valid proof verifies the random output. Fair entry rules and how results are used remain part of your application.
Build with verifiable randomness
Talk to the PlantMe team about your use case and integration.
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