Gaming
Game commerce is high-frequency and friction-sensitive, with small payments and impatient players. Payinference decides each payment in the authorization hot path, targeting under 50 ms at p95 with typical decisions around 1 ms, keeping friction reserved for payments your thresholds actually flag, and learning from every reported outcome.
Friction kills conversion; fraud eats margins
High-frequency small payments can't afford heavy checks on every attempt, but skipping checks entirely invites abuse. Without a per-payment decision, games either over-apply friction and lose players or under-apply it and absorb the losses, and neither choice leaves a record of why.
Decisions at game speed
Solutions that fit
The same decision layer, pointed at the problems this industry actually has.
Risk decisions
Turn risk signals into one executable instruction. Step-up, hold or block is scored on safe payment context against your own thresholds, with reason codes on every decision.
Payment optimization
Choose the best action for every payment before it is sent. Routing, failover and fallback are decided per payment from live health, cost and policy signals.
Retry intelligence
Decide whether a failed payment is worth retrying and how. Same route, failover or stop is decided from retry policy and current provider health, not a fixed schedule.
Frequently asked questions
Common questions from gaming teams evaluating Payinference.
The decision path is built for the authorization hot path. It computes from cached policy, cached provider health and cached pricing, with no PSP calls and no LLM calls, targeting under 50 ms at p95 with typical decisions around 1 ms.
One call to POST /v1/decision. Reporting outcomes to /v1/outcomes is always free, and the first 1,000 decisions each month are included at no charge. Shadow and enforce decisions run the same decision path and are billed the same.
In shadow mode Payinference returns decisions and records what it would have done while your existing logic keeps executing, so you can evaluate decision quality against real traffic with zero risk. In enforce mode your stack executes the returned instruction.