Knowable Labs

Know what’s coming.
Know what to do.

Knowable Labs builds end-to-end forecasting and trading infrastructure. We produce best-in-class forecasts on news, politics, and finance, then execute them as positions on prediction and capital markets, with hard risk guardrails and a full audit trail.

Who we work with

Forecasts are useful anywhere a decision depends on what happens next. Today we work with traders and teams in three areas.

Active prediction market traders

You bring an idea, your own data, or nothing at all. The harness does the research, writes a strategy you can run, backtests it with fees included, and places the positions inside guardrails you set.

Hedge funds and market makers

Use calibrated forecasts to find event-driven and macro opportunities, sharpen conviction, and size positions with greater confidence. For systematic desks, Knowable brings an independent, forward-looking signal into fair value, pricing, and execution.

Insurance

Underwriting and reserving are bets on events that haven’t happened yet. Knowable delivers calibrated probabilities on catastrophes, policy changes, and litigation outcomes, giving pricing and exposure decisions a live, evidence-backed forward view.

A demonstrated edge across leading benchmarks and live markets

Knowable’s forecasts outperform leading models and human forecasters on ForecastBench, while its trading system generates consistent returns in live Kalshi markets.

Live trading on Kalshi
Since inception
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Last 60 days
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Trades
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Settled
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Time-weighted return since inception — → —
Return since inception Trade placed

Fully autonomous: the system picks the markets, sizes the positions, and executes. Every trade is logged.

ForecastBench standings

On ForecastBench’s contamination-free questions, Knowable Labs is among the top market scores on the board.

ForecasterMarket score (higher is better)
Knowable Labs
75.6
Gemini Google DeepMind
75.6
Superforecaster median human gold standard
75.0
Grok xAI
73.0

Standings as of September 21, 2026. Verify on the public leaderboard: we run as “Anonymous 8” (tick “show anonymous teams”).

Knowable forecasts that held

Real calls our system made on live Kalshi markets. For each one: what we said, what the market said, and what actually happened.

Our call vs. the market
We said
Market said

Each number is the chance of “yes” at the moment we made the call.

What happened

The harness

One harness, four steps. Research pulls news, filings, market data, and your own data as of the decision time. Strategize turns the idea into entry rules, sizing, and exits a system can run. Backtest and execute places positions with guardrails in code and every position traced to its evidence. Learn retrains calibration and sizing on every resolved market.

Forecasting infrastructure

Calibrated probabilities on news, politics, and finance continuously updated as new evidence arrives and outcomes resolve. Built to cover thousands of questions at once at a fraction of a frontier-model’s cost.

Question intake

Any resolvable question, from a market feed or your own list.

Evidence gathering

Live news and data pulled per question, cited in every forecast.

Calibration

Forecasts scored against outcomes and corrected continuously.

Coverage

Thousands of open questions tracked at once.

Execution infrastructure

Turns forecasts into positions on prediction and capital markets. Built for scrutiny, not just returns.

Risk guardrails

Position and exposure limits enforced in code.

Explainable audit

Every position traces back to its forecast and evidence.

Full logging

Every decision recorded end to end.

Continuous training

Realized outcomes feed back into the forecaster.

If it resolves, we forecast it

Binary01
Output
One probability, 0–100
Will the UK Prime Minister resign next month?
Numeric02
Output
A full distribution
Where does core CPI print in October?
Timing03
Output
A date, with a window
When does the Fed next cut?
Multi-outcome04
Output
Weights across a set
Which party takes the most seats?

Questions arrive from a market feed or your own list. Thousands tracked at once, re-forecast on every new fact.

Meet the team

They spent decades building ML systems at Google. Now they’re putting that experience toward a harder problem: knowing what happens next.

Yew Jin Lim

Yew Jin Lim

Co-founder · PhD in Machine Learning

Ex-Director of Engineering on Google Search, with 18 years building planet-scale ML and GenAI systems. PhD from the National University of Singapore.

LinkedIn →
Gautham Srinivas

Gautham Srinivas

Co-founder · Product leader in ML quality

Ex-PM leader on Google Search AI. Before Google, an early engineer at Flipkart and one of India’s top 10 competitive programmers through the IOI training camp.

LinkedIn →

Work with us

If you trade markets, plan supply chains, or underwrite risk, get in touch. None of those but want our forecasts on tap? We’d like to hear from you too.