Maple
Sunlit desk with an open book, coffee cup, and autumn branch

About Maple

Open-source AI, made easy to use and possible to verify.

AI you can use with confidence.

Maple Privacy Labs builds private AI products for people, teams, and developers. We make capable open-source models easy to use while giving you a way to check how your data is protected.

Our goal is to put more of the best open models within reach. Privacy and proof are part of that experience: your messages are encrypted on your device, processed inside hardware-isolated infrastructure, and never used to train models.

What Maple does

Maple Agent

A personal AI for your real life that remembers what matters. Join the waitlist for early access.

Join the Agent waitlist

Maple Teams

A shared workspace for organizations that need capable AI while keeping company work under their control.

Explore Maple Teams

Maple Proxy

An OpenAI-compatible endpoint that verifies the secure enclave and encrypts requests from tools you already use.

Explore Maple Proxy

Our Inspiration

Autumn woodland painting with trees and a path by William Trost Richards

Autumn Woods

by William Trost Richards

Maple trees form underground networks of root systems, communicating and sharing resources without the knowledge of animals above. This cooperative approach helps them thrive in challenging environments.

Maple AI draws inspiration from this natural model of secure, decentralized intelligence. Each account uses a personal encryption key, giving you control over the data. Whether you are handling sensitive information on behalf of clients or your own personal thoughts, communication stays between you and the AI. No data is used for training models nor shared with third parties, not even us.

People shouldn't have to sacrifice their privacy for high-quality AI. Maple upholds the highest standards of privacy and confidentiality, preserving the fundamental human right to freedom of thought. With confidence in AI, you can work together to thrive in your own challenging environments.

What makes Maple different

01

Access to open models

Maple brings capable open-source models into a straightforward product, so using them does not require building your own infrastructure.

See Maple Research
02

Privacy built into the path

Messages are encrypted before they leave your device and decrypted only inside hardware-isolated infrastructure for inference.

See how Maple protects data
03

A claim you can check

The client and server code are public. Reproducible builds and enclave attestation give you a way to check what is running.

Explore the security proof

Built for real life and real work

Who uses Maple?

For the things you want to think through, build, and keep yours.

For individuals

01

Your own thoughts.

Ask the difficult question. Bring the personal document. Work with capable open models in a space built to keep your conversations private.

Explore Maple Research

For teams

02

Your team’s knowledge.

Give people room to research, write, and analyze with AI while keeping company work under company control.

Explore Maple Teams

For developers

03

Your existing tools.

Bring private inference into the apps and workflows you already use through an OpenAI-compatible endpoint.

Explore Maple Proxy

Founders

Our team has combined over 30 years experience building scalable cloud and local app solutions that take privacy and security seriously. We have successfully built in the fields of data storage, defense, fintech, education, security, therapy, computer vision, and consumer technology.

Mark Suman, Maple co-founder and CEO

Mark Suman

CEO

Former engineer at Apple with a focus on AI and Privacy. Early employee in Product and Engineering at multiple startups, including Director of Mobile at Instructure Canvas. Over 20 years building in tech.

Anthony Ronning, Maple co-founder and CTO

Anthony Ronning

CTO

Infrastructure engineer in many startups over the last decade. Previous experience in defense, security, networking, and bitcoin companies. Built the TEE architecture that makes Maple possible.

Our Team

Michael Atwood

Michael Atwood

Head of Growth

Go-to-market engineer focused on AI automation and growth. Has built AI systems for businesses across commerce, media, law, accounting, and more, from content engines to back-office workflows. Co-founder of Oshi, a bitcoin rewards platform.

How Maple works

  1. STEP 1

    Choose a model

    Use Maple Research, a team workspace, or Maple Proxy to reach the models available to your plan.

  2. STEP 2

    Send a private request

    Maple encrypts your message on your device before it travels to the secure enclave.

  3. STEP 3

    Check the proof

    Review the public code, reproducible builds, and attestation behind the privacy claim.

See how the proof works

Frequently asked questions

Straight answers about the models, the privacy boundary, and how to check it.

What is Maple?

Maple is a private AI platform from Maple Privacy Labs Inc. It gives people, teams, and developers access to capable open-source models through products designed to be easy to use and possible to verify.

Does Maple use my data to train AI models?

No. Maple does not use your conversations to train models. Messages are encrypted on your device and processed inside hardware-isolated infrastructure.

How can I verify Maple’s privacy claims?

Maple publishes its client and server code. Its security proof explains reproducible builds and enclave attestation, which let you check the code and the environment running it. Read the security proof

Where can I see the available plans?

The pricing page lists Maple’s current individual and team plans, as well as API rates. See pricing

Startup programs

Programs helping us bring private AI to more people.

Open models, within reach. Private by design.

Maple Privacy Labs Inc.Austin, Texas