A NAM-powered tone rig. Studio-grade guitar tone — built on Neural Amp Modeler — in your pocket and on your desk.
Powered by NAM
The Neural Amp Modeler engine, made playable
Neural Amp Modeler (NAM) uses machine learning to capture the exact sound and feel of real amps and pedals. ToneRig wraps that engine in an interface built for players — load captures, build a chain, and play. No rig-modeling PhD required.
Thousands of tones, one tap away
ToneRig plugs straight into TONE3000, the largest library of Neural Amp Modeler captures. Sign in with your account, search the collection, and load a tone into your chain without ever leaving the app.
- Browse and search captures right inside ToneRig
- Filter by Amp + Cab, Amp Head, pedals, and more
- Download straight into your signal chain
Everything in one rig
Real NAM captures
Load Neural Amp Modeler files and play through the exact amps and pedals they were captured from — no profiling hardware required.
Pedalboard-style chains
Stack drives, modulation, delay, and reverb in any order. Drag, drop, and dial in your rig the way you'd build it on the floor.
Cab & IR loader
Pair any capture with impulse responses for cabinet and room character. Mix and match until it sits right.
Built for low latency
Engineered to feel immediate under your fingers, so practice, recording, and silent jamming all stay responsive.
Presets that travel
Save full rigs as presets and recall them instantly — from the couch to the stage to the studio.
MIDI control
Program Change and MIDI Learn let a foot controller switch presets, tap tempo, and mute — hands free.
See it in action
A full guitar rig — input, compressor, NAM amp, and effects — running on your iPhone and iPad.
Your whole rig, one screen
Build the signal chain, slot by slot
Drop in an input, compressor, NAM amp, and effects, then tap any slot to open its controls. Load a Neural Amp Modeler capture or browse TONE3000 without leaving the app.
ToneRig is out now
Available on the App Store for iPhone and iPad. Build your rig and start playing.