Exploitative solver and so much more

Meta big data

Millions of hands from the meta are analyzed to reveal actual pool trends, such as overfolds or overbluffing in specific spots.

AI-Modeled decision trees

AI creates decision trees capturing the meta’s frequencies and ranges, reflecting opponents’ play in every spot.

Max ev without range balancing

The top ev lines are targeted, avoiding range balancing to maximize profits by exploiting meta weaknesses.

Move explanation

GTOkiller acts as an coach, explaining the reason for each move and detailing which meta leak it exploits.

Data-Driven clarity

Explanations use real meta frequencies and ranges for accurate understanding.

Range balancing reduces ev

Using bluffs and bluff-catchers only for balance wastes EV when these moves aren’t profitable against the meta.

Opponents far from GTO

GTO assumes optimal opponents, but real meta players deviate from GTO, making balanced lines inefficient.

Exploit and crush

GTOkiller simplifies strategy, selecting max-ev lines that punish meta leaks for ev++.

Unique meta per site

GTOkiller creates strategies specific to each site, adapting to the unique mix of regulars, fish, or rake defining each meta.

Strategies by villain profile

Aggressive or tight opponents demand different approaches. GTOkiller adjusts max-ev lines based on villain’s profile to exploit their leaks.