Build with Privacy
Powered by ZK Swarm Intelligence
Enterprise-grade privacy infrastructure for ZK Swarm AI systems
Multiple AI agents coordinate tasks while raw inputs remain encrypted and provably private.
Agents produce ZK proofs that computation was performed correctly without revealing inputs.
Chain and nest agents into workflows (ETL, decisioning, retrieval, summarization).
Per-workflow privacy policies, role-based access, and audit proofs.
Run on cloud, on-prem, or hybrid nodes with pluggable compute backends.
Start with a few lines of code and build with complete privacy guarantees.
Five steps from private workflow to verifiable output
A developer describes a swarm (agents, roles, data sources, rules).
The workflow embeds a privacy policy and verification schema — what is allowed to be revealed (if anything).
Each agent receives encrypted inputs, performs computation locally or in a trusted runtime, and emits a zero-knowledge proof that it followed the workflow and policy.
A verifier collects ZK proofs and optional high-level results. If proofs pass, the swarm's output is accepted and shared according to policy.
Every run emits immutable proofs for audits — prove the system acted correctly without seeing raw data.
Here's an example of a monetized AI agent made using Voidinium
# x402 Monetizable Agent - Get paid for AI analysis
import os
from voidinium import Agent
from fastapi import FastAPI
from pydantic import BaseModel, Field
from x402.fastapi.middleware import require_payment
app = FastAPI(
title="Memecoin Analysis Agent",
description="AI-powered analysis monetized with x402",
)
MEMECOIN_PROMPT = """Expert cryptocurrency analyst specializing
in memecoin evaluation and risk assessment."""
def create_memecoin_agent() -> Agent:
return Agent(
agent_name="Memecoin-Analyzer",
system_prompt=MEMECOIN_PROMPT,
model_name="gpt-4o",
max_loops=1,
autosave=True,
dynamic_temperature_enabled=True,
)
memecoin_agent = create_memecoin_agent()
class MemecoinRequest(BaseModel):
symbol: str = Field(..., description="Memecoin ticker")
include_social: bool = Field(default=True)
include_technicals: bool = Field(default=True)
# Apply x402 payment middleware - $0.10 per analysis
app.middleware("http")(
require_payment(
path="/analyze-memecoin",
price="$0.10",
pay_to_address=os.getenv("WALLET_ADDRESS"),
network_id="base-sepolia",
description="AI memecoin analysis with risk assessment",
)
)
@app.post("/analyze-memecoin")
async def analyze_memecoin(request: MemecoinRequest):
query = f"Analyze {request.symbol.upper()}"
analysis = memecoin_agent.run(query)
return {"symbol": request.symbol, "analysis": analysis}Production-ready privacy infrastructure with compliance guarantees
Every run returns cryptographic attestations that computation followed the declared workflow.
Only the exact, allowed information (e.g., aggregate or labelled output) is released — everything else stays encrypted.
Use TEEs, MPC, or pure ZK proofs depending on your required trust/latency/cost balance.
Export proof chains for regulators/auditors — show compliance without exposing user data.
RBAC, usage quotas, and enterprise-grade logging (proof metadata only).
Built for high-throughput blockchain environments — execute ZK workflows at Solana speeds with cryptographic settlement.