Free: 1M context tokens (one-time). Pro: $15/month for 100M tokens/month, $0.18 per extra 1M tokens. Scale: $49/month for 350M tokens/month, $0.15 per extra 1M tokens. 30% off Pro or Scale for the first year for early access teams.
Not given
Key features
Removes duplicate context and off-topic results
Works with any LLM provider
API and SDK integrations
Quality guard checks if kept context is sufficient
Local cleanup of tool output and JSON fields
Fallback to original context on service failure
Per-agent budgets and token limits
Savings report showing cost avoided
Constant-state attention: models serve any context length from the same memory per request
HCGM: models run from disk storage instead of memory
M-Rail: context processed by a model can be reused instantly in future requests
CPUDA/sGPU: run unmodified CUDA programs on CPUs with bit-identical results
Organ grafting: transfer capabilities between models without training
Personal instruction set (ISA) compiled on device from phone signals
Population ISA: compile laws from many people without personal records
Digital twins of customers, fleets, cities or markets
Results identical across all machines and architectures
Computation cost follows novelty not data size
What makes it different
Reduces input tokens to LLMs by 56–71% on average
Optimizes in milliseconds without slowing requests
No model or provider changes required
Every removal logged with reason and recoverable
Constant memory cost regardless of context length or data size
Exact reuse: every computed result is kept and reused instantly
Portable execution with bit-identical results across all hardware
Models run from storage without retraining or fitting in memory
Privacy-preserving: raw data never leaves the device
Contextwaise
Wirkung