The world's most
accurate AI predictions
Mantic is a world-class technical team on a mission to solve the next AI grand challenge: predicting global events with superhuman accuracy, and deploying this capability to power radically improved decision-making in business and government.
Example predictions
From an ongoing tournament against human forecasters
- BusinessRevolut Banking License
- Global AffairsBRICS Expansion
- EconomicsUK Jobs Growth
- ConflictIran Leader Appearance
- PoliticsTaiwan Recall Vote
what mantic does
A new kind of foresight
Despite decades of progress in computer modelling, the best predictions in the messy world of human affairs still come from top human forecasters, who use reasoning and judgement.
Mantic is building machines that can predict like an expert superforecaster but with digital speed and scale.
Mantic has an edge on topics where a purely data-driven approach is infeasible or insufficient.
We’ve developed our system to make medium term predictions (1 week - 1 year out) about geopolitics, business, policy, technology, and culture.
Round | Prize Money |
---|---|
Q4 2024 | 4th / 40 |
Q1 2025 | 1st / 34 |
Q2 2025 | 4th / 54 |
In the $120,000 Metaculus AI Benchmark Tournament, we’ve consistently ranked in the top 10% of entrants, winning the top prize money in Q1 2025.
Our latest system establishes a new state of the art, benchmarked on 348 questions from Q2 2025:
A generalist model, ready for specialization:
Forecasting is a skill. Scientific research shows that superforecasters, using open source information, can outperform trained intelligence analysts and other domain experts.
Inspired by this finding, we’ve built our prediction engine to tackle questions from any geography or industry, without requiring private data. It dynamically decides what information to find and how to model the problem, like a top human forecaster would.
Working together with clients, we can add further layers of specialisation and assurance. We can validate our prediction engine on the client’s area of focus, and we can onboard new sources of information to ensure the predictions are well-informed.
Our Offerings
We’re ready to serve predictions to clients however is most useful:
Tabular
Predictions
A regular stream of predictions in a structured format
- Probability of terror attack in each country globally
- Probability of CEO departure at each Fortune 500 company
- Insurance underwriting
- Supply chain risks
Deep
Forecast
On-demand reports
The user specifies a topic, the system researches the relevant issues, formulates forecasting questions, then makes many predictions and writes a report to explain them.
- Commercial due diligence
- Strategy consulting
Custom
Dashboards
Track bespoke predictions over time
The user identifies which predictions they want to track, and the system notifies them when the probability goes up or down and why.
- Trading
- Government affairs
- Corporate risk and strategy
Every prediction comes with a rationale: historical analysis, recent developments, and the key arguments.
About us
Mantic is a startup founded in London in 2024, on a mission to solve forecasting and radically improve decision-making.
We raised £3 million in our pre-seed funding round, led by Episode 1, with participation from the US trading firm DRW and a range of angel investors including leading researchers at Google DeepMind and Anthropic.


We are supported by Google Cloud and AWS, delivering the compute we need to train class-defining models.
We aim to match, then exceed, the accuracy of human superforecasters. We will drive adoption of this capability across the private and public sector, unlocking better-informed decision-making.
Our Team

Ben Day
Before Mantic Ben was Head of Research at Foresight Data Machines, developing AI for optimizing steel production. Ben has a PhD in machine learning from the University of Cambridge, where he researched meta-learning and graph neural networks. Alongside this, he was a Research Consultant at Relation Therapeutics on AI for drug development.

Toby Shevlane
Before Mantic Toby spent 2.5 years at Google DeepMind, where he worked as a Senior Research Scientist. There, he co-led a team within the Gemini effort that designed experiments to test Gemini’s dual-use capabilities. Before that, Toby’s PhD research at the University of Oxford was about the governance of LLM release decisions and potential misuse risks from AI.

Matthew Aitchison
Previously, Research Engineer at Google DeepMind. PhD from ANU in reinforcement learning.

Scott Jeen
Previously, PhD at University of Cambridge on reinforcement learning.

Vlad Bogolin
Previously, researcher at University of Oxford; Senior Engineer at MariaDB and FlowX.AI. PhD in AI from the Romanian Academy.

Max Clark
Previously, software engineer at Citadel. Highest dissertation mark at University of Cambridge Computer Science undergrad for paper on AlphaGo.
We're hiring
Join us to solve the next AI grand challenge.
We’re assembling a world class team. If you’re energized by hard technical problems with potentially massive impact, we want to hear from you.