Compress the context. Keep the meaning.
Hypernym compresses LLM context at runtime — code, docs, conversations — so models keep the right facts and teams get more throughput per dollar.
.svg){kind=logo}
AI works until it runs out of room.
Every call sends more context than the model needs. The current fix? Bigger windows and bigger bills.
Context fills up
Agents re-read everything. The window fills up. Every run starts from zero.
Tokens are wasted
Same files loaded every loop. Tools compete for the space they're trying to protect.
Quality degrades
Full context means drift, confusion, hallucination. Longer sessions, worse output.
Same results. Half the time.
50k lines→1.5M lines
30× compression means 30× longer before context fills up. Build the whole feature, not half of it.
$3.54→$0.35 / file
Self-validating compression with built-in coverage analysis. No separate fact extraction required.
388s→192s on SWE-bench
Less context to process means faster responses.
Hypernym parses context at the structural level, compresses it through our compression engine, and carries what one session learns into the next. The model sees less but understands more.
One engine. Two products.
To compress something well, you have to understand what matters in it. That makes context measurable and tunable.
Hyper Context
Compressed memory of the codebase at every loop. What one run figured out carries to the next. All-day sessions, no wasted tokens, 2-8× faster.
Learn More →Hypernym for Platforms
Embed compression directly into your product. Your users get longer sessions, lower costs, and better output — and they never know we're there.
Learn More →You don't need a bigger window.You need denser context.
See how Hypernym compresses context for coding agents and AI platforms.