FutureAI Raises $5.8m in Seed Funding and Partners with Google for AI Model for Generative Interfaces

San Francisco, CA – FutureAI will usher in a new era of development with the launch of Leo 1, our new poly-agentic AI architecture for building generative applications and user interfaces. Leo 1 utilizes future-looking data, enabling developers to create bespoke user experiences designed to strengthen engagement and increase conversion rates.

Our $5.8m Seed Round and AI Partnership with Google Cloud to Unleash New Generative AI Era for Developers to Leverage Private User Data

As part of the Leo 1 launch, we are announcing $5.8m in seed funding led by PivotNorth Capital, Village Global and Jay McGraw’s JPM Capital.

“Our AI model Leo 1 is a massive shift in AI that enables developers to create opt-in applications that deliver a personalized experience to every user with a single click,” said Lee Hnetinka, FutureAI’s founder and chief executive officer. “Up until now, application development and user interfaces have hinged on clicks and data science, leveraging historical data. Applications have often leveraged other user’s behavior and machine learning techniques like collaborative filtering and content-based filtering to build recommender algorithms that drive the determination for generating the interface. These algorithms drive a prediction of what is likely to perform well and these predictions are passed along to a marketer who is in charge of leveraging data to make a final decision of what is shown to the user and build the collections for the user interface. In some instances this is done manually, and in other instances it is done algorithmically, but in both instances this can only be done using historical data, and do not leverage future-looking data. This is all changing now with our new generative AI model which combines a user’s data and a developer’s data to generate an application generatively and personally.”

“Generative AI can help developers create highly personalized experiences in applications and bring users more relevant and helpful content,” said Dr. Ali Arsanjani, Director, AI/ML Partner Engineering at Google Cloud. “FutureAI’s decision to build on Google Cloud means its teams will have reliable access to our AI optimized infrastructure, compute, and AI tooling as they go about training and scaling AI models, and we’re pleased that Google Cloud’s technology will support FutureAI’s mission.”

Creating an Architecture to Ensure Privacy and Security for Each User to opt-in and Connect External User Data Starting with their Gmail and Plaid Transaction Data to Receive a Highly Personalized Experience.

Leo 1 is designed with an all new poly-agentic AI architecture that encompasses three agents for leveraging external user data in a private and secure opt-in manner, with our users being able to opt-in to first being able to connect their Gmail and Plaid data to create a highly personalized experience. By enabling developers to build their application generatively, users are delivered a more personal experience increasing the metrics developers look to optimize such as conversion, transaction frequency and size and customer life-time value.

The first agent is an LLM Sift to remove any private tokens that aren't used for generating the application and interface for a user. The data we remove is any data that falls into categories such as financial, health, and a handful of other categories that we deem private to users and do not use to generate a user’s interface. This data is identified and removed before we ingest a user’s data. We use a combination of text moderation scoring, data loss prevention and an LLM sifting technique to ensure these tokens are not ingested and used for generating the application and user’s interface. All data that users share is user consented and users have full control over the data they share with FutureAI.

Our second agent is the contextualization of the user’s tokens and clustering. This agent uses a combination of RAG and LLM contextualization to cluster the user’s tokens and ready them for pairing with a developer’s tokens.

The third agent in our architecture is the pairing method to pair a user’s tokens with a developer’s tokens to generate their user interface for the application. This agent is our largest LLM out of all three agents and is our AI model, Leo 1. Leo 1 has been trained for generating user interfaces with personalization at its core.

The combination of all three of these agents is what forms our poly-agentic architecture and allows Leo 1 to generate an application’s user interface leveraging opt-in access for users’ Gmail and Plaid data while keeping user data private and secure in our own GPU clusters and ecosystem. To ensure all user and developer data is kept private and secure, we host our entire architecture ourselves, ensuring no user or developer data is passed back to any third party, or used for training any LLM.

Beginning the Migration from a Graphical User Interface to an Automated Intelligent User Interface (AII) Architecture

To build an application with a generative interface we use tokens instead of clicks, and these tokens are users’ Gmail information and Plaid transaction data that gives us visibility into what is relevant to a user. It allows us to take into consideration another dimension of a user that a single developer isn’t able to access, and this enables a generative interface which is more personalized to the user.

When ingesting user data we ingest all modalities of data, text, images, and email attachments and use these to build a rich persona of a user. The specificity of data ranges from specific colors of a sneaker that a user likes, to the ingredients we see them ordering frequently, or conversations they are having with a partner or personal trainer. We use these tokens to migrate from a predictive method to a generative method for generating a user’s interface which leverages semantic reasoning. For example, Leo 1 reads the ingredients and menus for a restaurant, it looks at the product photos and descriptions, and uses these tokens to generate the interface for each and every person.

With this migration of semantic level generation of an interface, we allow the transition from a graphical user interface which engineers build themselves, to relying on Leo 1 to build the interface for them, and for their application autonomously.

“Self-Driving” For Your Application’s Interface

Generative interfaces for an application requires a tremendous amount of trust by users and developers. For users it requires trust to be built around user consented sharing of data. Each time a user shares data with FutureAI, we ask for the user’s consent and ensure they know what they are sharing. This consent is the basis for user control of their data.

Developers require trust in an AI to generate their interface without telling them why. When we generate the user interface for the developer’s application, what we are generating is the interface with their data based on the user’s data, without sharing the user’s data with them. For instance, for an application that allows a user to book restaurants, we are generating the restaurants relevant to the specific user based on the menus Leo 1 has read. For a brand that sells sneakers, we are generating an interface based on a user’s tokens that provides the data about the tennis shoes and hiking boots important to the user based on conversations between Leo 1 and their data. For a publication, we generate the user’s interface based on their tokens providing us with their most relevant articles.

All of this involves an immense amount of trust on both sides – the users with FutureAI and developers with FutureAI. This is why we think of FutureAI as “self-driving for your application’s interface,” because we all trust an AI to drive for us, and we do it because we believe users will trust FutureAI, and developers will trust FutureAI to generate their interface for them.

Developing Generatively Means Developing Better, Faster, Cheaper, and Personalized

FutureAI has been the culmination of two years in the making, and that’s because it has taken a combination of the right technology, the right team and timing to introduce a breakthrough in how the interface is generated for an application,” added Hnetinka. “To quantify our investment we look at what developers and users stand to gain with generative applications and interfaces.”

On the developer side, the cost basis before FutureAI for generating their interface involves a data science team, data analysis tool and marketer, which combined costs about ~$560k annually. Such teams are tasked with determining the optimal content and products for a user’s interface, and the outcome is built predictively. It takes time for these teams to build the interface and the reinforcement learning for A/B testing, and the interface isn’t as real-time as an organization would want it to be. To compare, developers can build their application generatively with Leo 1 for a fraction of the cost. Today we are making Leo 1 available for $19 per Input MTok and $75 per Output MTok, enabling developers to adopt our new poly-agentic architecture and integrate it into their application with minimal upfront investment. We aim to generate each application’s interface each time in ~5,000ms and today, Leo 1 is available via our API and on Google Cloud Marketplace.

Migrating to a generative application and interface architecture for users results in a personalized experience– an interface made just for them. This is the holy grail of development and is now possible with Leo 1. Delivering a personalized interface and application creates an increase in metrics across the board, such as conversion rates, transaction sizes, frequency of visit, and customer life-time value. The increase of these metrics provides an organization with a look at FutureAI holistically across engineering teams, data teams, marketing teams and finance teams giving us the ability to provide an increased adoption and install rate of FutureAI within every single developer ecosystem.

Generating Masterpieces

We named our AI model after Leonardo da Vinci because he pioneered and refined the techniques and methods for how art was generated, leading to the creation of the Mona Lisa. We take inspiration from da Vinci’s innovations in Leo 1, aiming to deliver to developers the tools to generate a masterpiece each time they generate an interface for their user.

If you are interested in getting access to Leo 1, tell us what you will build with it to request developer access.

If you’re interested in building the next version of Leo 1, We’re Hiring.