Your product is brilliant.
Context is the bottleneck.
You've built the orchestration, the UX, the agent logic. But context walls are breaking sessions, token costs are scaling faster than users, and your inference provider can't keep up. Hypernym compression sits in the call path and fixes all three.
Three platforms. Same wall.
The context problem looks different depending on what you're building. The fix is the same.
“We orchestrate 10 tools. The model can barely hear one.”
Your platform ingests email, Slack, docs, calendars, and task systems into a unified agent experience. The orchestration is excellent — but every source adds tokens, and the model is drowning in context it can't prioritize.
Users create one massive context for everything, or try to split into distinct contexts per task and watch them collide. Chains break silently. Your team has no visibility into where the model lost the thread.
You don't need less ambition. You need denser context.
As your userbase grows, token costs grow with it — potentially exponentially. Your inference provider may not have the capacity you need at peak. And your users won't wait. Compression breaks the tradeoff between quality, cost, and speed simultaneously.
Costs scale with users. They don't have to.
10×
throughput per dollar
Every compressed token is money you don't spend. At platform scale — millions of calls per day — the savings compound into your most important line item.
30×
more context, same window
Smaller payloads mean less pressure on your inference provider. The same infrastructure handles more concurrent users without upgrading your tier or negotiating new rate limits.
2×
faster task completion
Less context for the model to process means faster responses on any model you're already running. Your users get speed without sacrificing quality — no model swap required.
Orthogonal to everything you're already doing. Hypernym compression stacks on top of prompt caching, model routing, RAG tuning, quantization — all of it. It's a multiplier, not a replacement. If you've already optimized your pipeline and hit a wall, this is the next lever to pull.
One call between retrieval and inference.
Everything else stays the same.
You find the right context
We make it fit
It understands more
Available as a hosted API, containerized for on-prem deployment, or as a custom enterprise integration. Your data policies, your infrastructure choices.
Talk to us about deployment →What your users feel. What your metrics show.
Sessions that don't break
Users stop hitting the wall mid-task. The frustrated restart — and the churn that follows — goes away.
Multi-source without bloat
Ingest email, Slack, code, docs, and wikis into one context without drowning the model. Every source compressed to its essential meaning.
Invisible to your users
Hypernym sits in the call path. Your users never see it, configure it, or know it's there. Their experience just works better.
Margins that improve with scale
Most platform costs grow linearly — or worse — with users. Compression flips the curve. Per-user token cost drops while output quality holds. Finance will notice.
Speed your users feel
Compressed context means less for the model to process. Response times drop on every model — no architecture changes, no model swaps.
Ship it, don't build it
Semantic compression is a research problem. You could spend a year and a research team building it. Or you could integrate Hypernym this quarter and ship features instead.
Validated by the teams building the frontier.
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2× faster task completion on SWE-bench with zero quality loss.
30× context reduction. Patent pending.
Your orchestration is the hard part.
Context compression shouldn't be.
Tell us what your platform does, where context breaks, and what your token bill looks like. We'll show you what changes.
Building with coding agents? See Hyper Context for coding agents →