Project description

Lisa Intel is building a governed AI research and discovery platform that enables autonomous, cross-domain scientific innovation while maintaining safety, auditability, and control.

Recent MIT research shows that scientific foundation models converge across domains, enabling transferable representations between chemistry, materials science, biology, and beyond.

📄 MIT article: https://www.alphaxiv.org/abs/2512.03750

Our project focuses on operationalizing this insight into a real system:

What we are building

A modular AI research platform coordinating multiple domain-specific models

Autonomous cross-domain discovery loops (hypothesis → validation → iteration)

A built-in governance layer for safety, auditability, and policy alignment

Structured outputs suitable for validated research artifacts and IP generation

Unlike existing tools, Lisa Intel focuses not on a single model or domain, but on the control plane that governs how advanced AI research systems operate safely at scale.

https://lisaintel.com

Project summary

This project develops a governed, autonomous AI research platform that turns cross-domain foundation model capabilities into safe, auditable scientific discovery. The goal is to create infrastructure that enables innovation without sacrificing control or safety.

What are this project's goals? How will you achieve them?

Goals

Build a reference implementation of a governed AI research system

Demonstrate safe, autonomous cross-domain discovery workflows

Establish a foundation for future standardization and policy alignment

How

Design and implement a modular orchestration architecture

Integrate governance mechanisms (audit logs, control policies, fail-safes)

Validate the system through simulated and pilot research tasks

How will this funding be used?

Funding will support:

Core system architecture and orchestration development

Governance and safety mechanism design

Validation through simulated research pipelines

Legal and technical work related to patent hardening and documentation

With $75k (minimum):

Deliver a functional architecture + governance prototype

With $200k (full funding):

Build a robust reference system

Run cross-domain validation experiments

Prepare the platform for external pilots and collaborations

Who is on your team? What's your track record on similar projects?

Pedro Bentancour Garin – Founder & Lead Architect

Background spanning:

AI governance and safety systems

Multidisciplinary research coordination

Prior work on large-scale, system-level technology concepts

Alexis Podolny - Strategic Advisor

Previously at startup "TheyDo", raised €54M in two years.

The project is supported by ongoing dialogue with researchers, policy actors, and AI governance initiatives in the US and EU.

What are the most likely causes and outcomes if this project fails?

Likely causes

Insufficient funding to fully validate system-level assumptions

Slower-than-expected integration across domains

Outcomes

Even partial success yields valuable governance frameworks

Architecture and insights remain reusable for future AI safety and research infrastructure efforts

Downside risk is limited; upside impact is high.

How much money have you raised in the last 12 months, and from where?

$15000 raised to date from family and friends

$50000 from loans

Project currently founder-funded and pre-seed