One browser layer, many workflows.
Twin is the browser execution layer for LLM agents — a token-efficient browser API that turns a goal into deterministic, replayable action. Here's what teams build on it, and the wedge that makes each one cheaper the more it runs.
Built for browser work at volume
Every page follows the same shape — the problem, how Twin solves it with a compile-once skill cache, and the outcome when cost stops scaling with usage.
AI agents
Give your LLM agent a real browser it can drive — and stop paying the model on every single run.
See how it worksRPA replacement
Replace brittle, selector-keyed RPA bots with skills that adapt to the page and get cheaper the more they run.
See how it worksQA & test automation
Author end-to-end tests as goals, run them deterministically, and replay every failure as session video.
See how it worksData extraction at scale
Extract from authenticated, multi-step pages — and stop re-paying the model to read the same site every run.
See how it worksInternal workflow automation
Automate the internal tools and vendor portals that have no API — with audit logging and human approval built in.
See how it worksAccessibility automation
Drive web tasks on a user’s behalf and audit pages for accessibility — over a token-efficient view of the live DOM.
See how it worksWhatever you automate, it gets cheaper the more it runs
Most browser infra re-runs the LLM on every execution, so cost climbs with usage. Twin inverts that — the same primitives power every use case above.
Compile once, match forever
The first run compiles a skill; the semantic dispatch cache fuzzy-matches every re-phrased request to it, so repeats skip the planner LLM.
Learn moreReplay with zero LLM calls
Matched skills replay deterministically — a pass is a pass, results are stable, and the marginal cost per run trends toward zero.
Learn moreAuthorized, governed, observable
Per-tenant keys, default-deny RLS, a credential vault, human-in-the-loop handoff, and an audit log on every call.
Learn moreExplore the platform
Whatever you automate, the cost curve bends down.
Compile a task once, match the next re-phrased request with the semantic dispatch cache, and replay it deterministically with zero LLM calls. Read how it works or why the economics invert.