TailState Intelligence

Our reasoning

The most important intelligence lives beyond the average.

In anomalies, edge cases, transitions and rare but meaningful states. That is where TailState works.

Beyond the obvious

The obvious is already visible, known, or automated. We look beyond it, for asymmetric insight that hasn’t been arbitraged away.

Built for the tails

Most systems fail at the tails. We build for them: models designed for the rare states where fortunes and failures are decided.

From anomaly to insight

We turn uncertainty, anomalies and system shifts into usable intelligence: evidence where there used to be intuition.

How we work

Two pillars. One direction.

Our R&D pillar explores what is possible. Our product pillar builds what is useful.

Pillar I · R&D Lab

Researching the edge

Explores what is possible.

Our talented AI researchers test frontier ideas before anyone asks them to be profitable. This is the source of everything we ship.

New model architectures, datasets and frameworks
Frontier work on non-linear and uncertain systems
The strongest ideas graduate into products

Pillar II · Product Studio

Building the future

Builds what is useful.

Where bright engineers turn the strongest research into commercial systems people are happy to use: fixing real problems, not chasing novelty.

Software, pipelines and decision-support systems
Commercial first: real problems, real workflows
Enterprise tools, APIs and analytics for B2B customers

Current focus

Two projects in progress.

Both ideas born in the R&D lab. Both are meant for the states where standard systems fail.

Product 01 · Risk intelligence

A foundation model for risk.

We are training, from scratch, a multimodal foundation model that learns the joint behaviour of language, events and time series, working on diffusion and transformer architectures over precisely tagged data.

Ask it a question about the future in plain language. It returns the full conditional distribution of the time series: not a point estimate, not a guess.

→Ask “what if?”Pose an event or hypothesis in plain language: a supply disruption, a policy shift, a breakthrough result.
→See the whole distribution and decide with evidenceExpected shift, spread and tail probabilities, all conditional on the scenario.
→Potential use casesAnywhere a time series meets events: financial risk management, healthcare, biotech, energy, and strategic planning.

Scenario engine · conditional distribution of a time series Interactive

Expected shift

…

P(move < −5%)

…

Tail risk vs baseline

…

Illustrative output only. TailState provides research and analytics, not advice or recommendations.

Product 02 · Next-generation agents

Human on the chips.

The best agents out there, are humans (biological agents). Human on the Chips replicates that: a dynamic system of algorithms wrapped around a generative-AI subconscious; an agent that changes how it works based on its personal experiences.

Every AI agent out there today is a static system of algorithms wrapped around an LLM engine, what we call the subconscious. Capable, but fixed: it performs the same on day one thousand as on day one.

→Starts like a juniorIt begins by making mistakes: working, taking feedback, correcting course like a new employee.
→Grows into a seniorThrough time and feedback it compounds experience into judgment, not just context.
→The bottleneck: memoryThat learning curve demands a genuinely new memory-management system. That is the core of our current R&D.

Agent architecture · static vs dynamic

Typical agent

LLM engine

static algorithms · fixed workflow

Human on the Chips

Generative subconscious

dynamic algorithms · adaptive memory

In research & development. Capability shown is the design goal, not a shipped benchmark.

Who we work with

Built for those who own risk.

We work B2B, wherever decisions depend on tail events: anywhere a time series meets the real world.

Healthcare & biotech

Financial institutions & asset managers

Energy & infrastructure

Insurers & corporate risk teams

Technology & data companies

Research & consulting organisations

Every one of these teams already owns risk they cannot fully see around corners on.

About us

Built by people who priced the tails for a living.

TailState was founded in London in April 2026 by quantitative researchers and risk professionals with track records across global markets, from systematic macro risk to LLM-driven trading strategies.

Alireza Kargarzadeh

CEO & Co-Founder

MSc Mathematics & Finance (Distinction), Imperial College London

Quantitative finance professional across portfolio management, market risk and applied machine learning: long/short global equity portfolio management at Fulcrum Asset Management, market-risk quant at Bank of America Merrill Lynch, and research on systematic, LLM-driven trading strategies.

Fulcrum

Arman Khaledian

Co-Founder

PhD Financial Mathematics, Imperial College London

Quantitative researcher and risk professional across systematic trading, market risk and machine learning: systematic-macro risk at Millennium, machine-learning quant at UBS, equity quant at Bank of America, and Visiting Lecturer at Imperial College Business School.

Intelligence beyond the average.