How Semantix Works

Turning AI outputs into verifiable proof through cryptographic validation

The Process

Semantix transforms any model output into a cryptographic proof of trustworthiness, without requiring users to rerun or manually verify anything. Our Active Validation System (AVS) works behind the scenes to ensure every AI decision is backed by verifiable confidence scores.

1

Query Initiated

A prompt (e.g., "summarize this contract") is submitted to a selected AI model, similar to how users interact with ChatGPT.

  • User submits natural language query
  • System routes to appropriate AI model
  • Query enters Semantix validation pipeline
🎯

Standard AI Query

Just like any AI interaction, but with trust verification built in

2

AVS Activation

Instead of returning the result immediately, the query is routed through Semantix's Active Validation System (AVS), a decentralized layer that triggers validation workflows.

  • Query intercepted by AVS middleware
  • Validation workflow triggered automatically
  • Confidence threshold determined
⚙️

Active Validation System

Decentralized validation that runs automatically behind the scenes

3

Multi-Model Validation

AVS runs the same query through a network of trusted models and validation nodes. Responses are compared for consistency, accuracy, and reproducibility using a probabilistically adjusted F1 score.

  • Query runs through multiple reference models
  • Consistency analysis across outputs
  • F1 score calculated with stability weighting
🤝

Consensus Validation

Multiple models agree = confidence. Disagreement = investigation needed.

4

Re-Verification Loops

For high-stakes use cases, AVS can re-run validations across multiple nodes to optimize the confidence score before finalizing. Simple tasks get one round, critical decisions get multiple.

  • Adaptive verification based on stakes
  • Multiple rounds for critical decisions
  • Confidence optimization loops
🔄

Adaptive Re-checking

Higher stakes = more verification rounds. Trust becomes tunable.

5

STARK Proof Generation

The final score and decision are bundled into a STARK proof, cryptographically verifiable by any app, chain, or agent. This creates an unforgeable receipt for every answer.

  • Cryptographic proof generation
  • On-chain verifiable without re-execution
  • Portable across chains and protocols
🔒

Cryptographic Proof

Unforgeable mathematical proof that travels across any blockchain

6

Verified Result Delivered

The user or agent receives a validated output, with a confidence score backed by math, not trust. The result comes with a portable proof that can be used anywhere.

  • AI output with confidence score
  • Mathematical proof of trustworthiness
  • Reusable across applications

Trusted Output

AI result + confidence score + cryptographic proof = verifiable trust

Key Innovations

🎯 F1 Score Innovation

Most AI scoring protocols rely on votes, usage, or static benchmarks. Semantix takes a fundamentally different approach: it runs each model output through trusted reference models and re-executes prompts multiple times.

This filters out models that sound smart but fail under pressure. Because the score is backed by math, not opinion, it can be used across any agent or protocol.

⚙️ AVS Innovation

Most open models run once, return a result, and move on. No rechecking, no consistency tracking, no assurance that the same prompt yields the same answer tomorrow.

AVS fixes this with programmable re-verification. It's like upgrading from "trust this one result" to "prove this five different ways." Trust becomes tunable.

Real-World Applications

🤖

Autonomous Trading Bots

Bots validate every trade via AVS before execution, scaling position size based on confidence scores

🏛️

DAO Governance

Execute treasury decisions only when AI recommendations meet cryptographically proven confidence thresholds

🌐

Cross-Chain Coordination

Agents read data from one chain, process on another, and act on a third with portable trust scores

🏥

Healthcare AI

Medical recommendations backed by multi-model validation and cryptographic proof of accuracy

⚖️

Legal Analysis

Legal agents generate court rulings with verifiable case law references and confidence metrics

🛡️

AI Insurance

Insurance policies that only trigger when model decisions meet verified confidence thresholds

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