Verifiable AI forScientificResearch
Explore, execute, and verify complex research with AI built for rigorous work.
Bottleneck
For as long as humans have existed, we’ve always tried to understand the world around us. What we’ve learned so far is remarkable, but with progress comes increased complexity.
The growing burden of knowledge is slowing scientific progress, because human cognition can’t scale with the proliferation of information available.
Shift
The scientific method is also evolving. AI is moving from a tool to a research partner, creating a fundamental shift in how (and where) science is performed.
Academic systems are built on human-led discovery, whereas frontier AI is concentrated primarily within labs with commercial priorities. For AI to meaningfully participate in the scientific process, how it’s built (and who it’s for) matters deeply.
Stewardship
As AI becomes foundational to discovery, it must protect knowledge as a shared asset, not a proprietary advantage.
FirstPrinciples is building AI solely for fundamental research, transparent by design, and committed to knowledge as a public good.
AI systems built for rigorous research
Theo
The AI workspace for scientific research
Plan and execute complex research, work across multiple AI models and scientific tools, revisit assumptions without starting over, and trace how results were produced.
Explore Theo
From Open Question to Verified Result
Explore
Investigate difficult questions, synthesize scientific knowledge, and develop structured research plans.
Execute
Work across models, code, symbolic systems, scientific software, and domain-specific tools in a single research workflow.
Verify
Test assumptions, compare approaches, validate outputs, and preserve the evidence and provenance behind every result.
Our Core Pillars
Scientific advancement
We enable new modes of discovery by building AI systems that contribute to rigorous, original scientific research.
Shared understanding
We aim to deepen humanity’s understanding of the universe and contribute to shared knowledge that supports long-term progress.
Responsible AI infrastructure
We work closely with the global scientific community to build AI infrastructure that can contribute meaningfully to scientific progress.
Institutional innovation
We are an independent, global company building AI systems designed for scientific discovery and grounded in the standards of rigorous research.
Built for Trusted Research
FirstPrinciples builds AI systems designed for complex technical work - combining domain-specialized models, scientific tools, structured research workflows, and verification.
Grounded and traceable
Keep research connected to its underlying assumptions, evidence, methods, and provenance - not just the final AI-generated answer.
Science-native
Work with AI systems designed around scientific reasoning and the tools, workflows, and standards researchers already use.
Model-independent
Use specialized Theo models alongside leading frontier models, comparing approaches rather than depending on a single system.
Built for organizations
Bring AI into research environments with the control, security, and deployment flexibility required for sensitive scientific work.
10+
Fine-tuned specialized models
8B-1T+
Parameter models
Research is how we build better products
Explore our research
Conjecturing
AI system 'Theo Conjecture' solves 35-year-old math conjecture, finds a term no one predicted
An automated discovery system built at FirstPrinciples by Randy Davila has proven a 1989 prediction by Paul Erdős and William Staton linking prime numbers to the Riemann zeta function, and uncovered a second mathematical term that had gone unnoticed for decades.
AI in science
Lab Notes: CiteVerify - Fake citation detection and evidence verification for AI research
Scientific writing has always relied on the implicit contract that claims must remain traceable to evidence. Large language models complicate that relationship. Modern systems can generate reports that appear rigorous and extensively sourced while quietly introducing fabricated citations. CiteVerify is FirstPrinciples’ answer to the rise of fake citations.
Conjecturing
Automated conjecturing: How machines are exploring mathematical structure
Before a theorem can be proved, someone has to decide what is worth proving. For decades, the formation of a conjecture remained largely outside the reach of machines. In Automated Conjecturing with TxGraffiti, Randy Davila explores how that boundary has shifted.
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AI in science
Between tools and theory: Reflections from the Machine Learning and the Physical Sciences workshop, NeurIPS 2025
A reflection on ML4PS 2025, where researchers in physics and machine learning grappled with the role AI should play in scientific discovery, and what it would take to move from process acceleration toward deeper scientific insight.
Verifiable AI for your hardest research problems
See how FirstPrinciples and Theo can support your research team.