01 · Selection
Relevance aware selection
Compaction reads your prompt and selects only the parts of your context that actually apply.
Why send your client notes to a coding agent? Smarter selection means smaller, sharper payloads.
Saves $ · Quietly optimized
Cut your AI bill 72%. Without losing quality.
Every AI call sends only the context that's relevant to the prompt, not your whole graph. Same depth, fraction of the spend. Quietly optimized in the background, every single call.
Most users see 70 to 80 percent lower token spend in the first week
Daily savings, last 30 days.
Percentage reduction in tokens sent per AI call. Compaction learns over time.
Today
72%
30 day avg
73%
Best day
81%
30d ago
21d
14d
7d
Today
How it works
Smaller payloads. Sharper output.
Three reasons compaction makes every AI call cheaper without making it worse.
02 · Learning
Continuous learning
Compaction learns which parts of your context correlate with quality output for each agent.
The longer you use an agent, the better compaction gets at predicting what it actually needs.
03 · Quality
No quality regression
Every compaction model is benchmarked against full context baselines. No quality loss, ever.
If compaction made answers worse, it wouldn't be saving you money, it'd be costing you results.
Why this matters
Faster responses. Lower bills. No quality tradeoff.
Token bills are the silent killer of AI workflows. As your context graph grows, naive prompts get exponentially more expensive. Compaction is the optimization that makes the platform durable. Most users see their AI spend drop 70 to 80 percent in the first week.
- 72% average token reduction per call
- No measurable quality regression in benchmarks
- Continuous learning per agent over time
- Fully transparent. See what was sent on every call
Ready to be known?
Built once. Used everywhere. Worth it the first time you don't have to re-explain yourself.
15 minutes to a useful first profile