Hypernym · Precision intelligence
More accurate. Easier to trust.
Hypernym is an AI research lab and products company focused on precision intelligence.
Our products increase the quality of all parts of the AI stack to reduce costs, improve contextual outcomes, and enhance system performance.
Hypernym focuses on inference-time optimizations, semantic compression, and hallucination suppression — infrastructure that tightens human-to-machine-to-human communication.
Inference-time upgrades for any open model. Quality grows as context lengthens — no fine-tuning, no hardware changes.
Every result carries mechanically verified proof of precision, accuracy, and provenance. Inspect it, don't trust it.
Deploy on your infrastructure or ours. Your data and your alpha stay yours — no one else trains on them.
Working with
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Platforms
Platforms that upgrade and verify your infrastructure.
Each platform drops into any model, ships standalone, and compounds with the rest.
A drop-in inference architecture for compatible transformers — no weights modified, no training, no fine-tuning. It improves Lost-in-the-Middle retention, decay rates, and decode performance.
A data lake optimization infrastructure and enterprise tooling suite.
Benchmarks · Public & reproducible
Modulum: quality that grows with context.
Measured on public long-context tasks — with vs without Hypernym, paired and reproducible on the same open weights.
The system
Each product has its own contract, its own evidence, and its own place.
Semantic compression prepares source material. Modulum treats eligible attention paths at inference time. Hypercore turns configured sources and workflows into inspectable research.
Long-context retention, treated at inference time.
Modulum is an inference-time treatment for eligible attention paths. It does not require retraining or a new model checkpoint. Because eligibility and benefit depend on the model, serving engine, and task, every candidate deployment starts with a matched baseline.
- No retraining or new checkpoint
- Model- and engine-specific validation
- Matched treatment and control
Reduce the source. Keep the measurement.
Semantic compression is a separate workflow for reducing long text and code before they reach the model. It reports the achieved reduction alongside coherence and source-coverage signals, so a target is never presented as a guarantee.
- Measured output ratio
- Coherence and source coverage
- Separate from Modulum
Research that keeps its source chain.
Hypercore loads domain-specific databases, entities, prompts, and workflows into a common research pipeline. It combines structured retrieval, entity resolution, multi-step research, and source tracing so people and agents can inspect how an answer was assembled.
- Structured retrieval across configured databases
- Entity resolution before research begins
- Multi-step agentic research
- Citation checks against the session trace
- Reusable, versioned workflows
Optimizations at the inference engine level
Better solutions from the models and GPUs you already run.
Memory and recall, long-context maintenance, hallucination suppression, and attention management — for users and customers of LLMs.