Input. Output.
Nothing.

A privacy-first multi-model AI inference network. Query open-weight models in parallel, synthesize outputs, and branch reasoning paths. Nothing about your request is ever written to disk.

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Private Inference

Zero retention completion

Broadcast & Diffuse

Synthesize across models

Fork & Merge

Branch reasoning paths

Redact & Verify

Cryptographic receipts

io_receipt_v1Just now

Cryptographic Proof

Hardware enclave verified

Retained Prompt: false

Model: gemini-imageVerify

Broadcast7 active

Multi-Model Active

Querying 7 specialist models in parallel

Gemini, GPT, Claude, DeepSeek, Qwen, Llama, Dolphin

Synthesizing...View

ForkBranch created

Reasoning Split

Independent branch: experiment-1

Base architecture defined.

Testing Redis cachingMerge

RedactedPII stripped

Privacy Guard

Sensitive data locally removed

User email is [REDACTED]

Identifiers: 0Log

Network

7

Models Active

8

Relays

Sessions

14

Ephemeral

3

Persistent

Security

21

Keys Stripped

12

PII Redacted

IO Synthesis

352:49

Total Inferences

70

Active Branches

63%

Node Utilization

Network overview

Built for developers and modern agent workflows, IO connects multi-model query, synthesis, and privacy in one platform, with cryptographic receipts that help you verify execution and operate trustlessly

IO Chat

Multi-model private AI. Broadcast prompts, diffuse answers, and fork reasoning paths in one place

OAI

ANT

GEM

I

Synthesized

4

Live

TTL: SessionJust now

Architecture Review

4 models combined

Completion (4/4)100%

IO MCP

The Orchestration Layer. 21 tools spanning core inference, privacy, and verifiable execution

io_broadcast

io_timer_set

io_redact

21

Context Protocol active at 100% capacity with 21 orchestration tools.

IO Code

Multi-path coding agent. Fork reasoning paths and merge optimal solutions locally

Branches2

experiment-1Fork

v0.2.0

Redis Caching

Proposed changes to data layer

mainMerge

Ready

Apply Solution

All linters passed successfully

The Next-Gen Inference Protocol

Designed from the ground up for strict privacy, multi-model orchestration, and verifiable execution.

Zero Retention

Prompts, responses, and user data are never saved to disk. Inference happens entirely in memory and is wiped immediately upon completion.

Hardware Attestation

Moving towards TEE (Trusted Execution Environment) enclaves by v1.0.0 for robust, hardware-backed cryptographic proof of privacy.

Economic Engine

Built-in buyback-and-burn mechanism funded by network revenue, driving sustainable value accumulation for the ecosystem.

IO Intelligence Layer

Privacy-first AI becomes operational intelligence

How IO keeps model work moving while preventing retention, leakage, and unverifiable AI output.

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Operational Privacy

Run inference with nothing left behind

IO keeps the request path memory-only: no account, no analytics, no database, no client storage, and no prompt retained after the response is produced.

Prompts, responses, IP, device fingerprint, and history are never stored
Every session carries a visible TTL and wipes when the timer ends
Redaction strips wallets, keys, emails, and identifiers before inference

Zero-retention AI that can be inspected instead of trusted on policy.

Private Runtime

Operational Privacy

preview

retained_promptnow

false

Bound to io_receipt_v1

policyactive

io-boundary

No persistent store in path

Memory-only Pipelinettl: session

1

input received

2

local redaction

3

model inference

4

response streamed

5

memory wiped

Storage

none

TTL

visible

Redaction

local

Model Intelligence

Ask several models, then synthesize the answer

Broadcast sends one prompt to specialist models in parallel. Diffuse merges the strongest parts into one answer with attribution, so users see disagreement instead of a single opaque response.

Solo routes to the right specialist model automatically
Broadcast returns side-by-side answers with individual receipts
Diffuse produces one synthesized response with per-model attribution

Better answers through model diversity, not vendor lock-in.

Model Lane

Model Intelligence

preview

modenow

diffuse

Bound to io_receipt_v1

orchestrationactive

broadcast + diffuse

Specialist models answer in parallel

Model Lane4/4 complete

Claude

attribution ready

DeepSeek

attribution ready

Qwen

attribution ready

Llama

attribution ready

Models

4

Answers

side-by-side

Synthesis

attributed

Proof Intelligence

Turn privacy claims into verifiable receipts

Each response ships with io_receipt_v1: a signed record of the model, active policy, worker hash, redaction state, and retention claim. Receipts can chain into audit trails.

Receipts verify offline against the published Ed25519 key
Proof certificates export as human-readable PDF with QR verification
Merkle receipt chains make long agent runs tamper-evident

Portable proof for agents, teams, and third-party verification.

Proof Console

Proof Intelligence

preview

receiptnow

verified

Bound to io_receipt_v1

verificationactive

offline proof

Signed record, exportable proof

Proof Certificatesha256:9f1e...c2a4

model: dolphin-mistral-24b

policy: io-boundary-0.3

retained_prompt: false

signature: Ed25519

Signature

Ed25519

Export

PDF + QR

Chain

Merkle

Start building with IO

Join the developers building the next generation of privacy-preserving AI applications. Access our SDKs, MCP server, and open-weights network today.

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