Discovering algorithms, not predicting tokens.
Hyperpilot's SW.Syn^ engine is built on a new class of foundation model — one that searches the space of algorithms directly. While Req.Gen^ and Test.Auth^ run on LLM technology, with engineer review, SW.Syn^ is the discovery-model breakthrough that unlocks the next step-change in algorithm discovery.
This is not an LLM.
LLMs predict the next token. Our model searches the space of algorithms directly — over data structures, control laws, estimators and policies — against a formal specification. The output isn't probably right. It's demonstrably correct against its tests.
The shape of the search space determines what software you can ever find.
Hyperpilot's search space is unbounded. An LLM's is bounded by the public code it was trained on — and industry-grade control software isn't public. And even if it were, it's not enough to find solutions to new problems — and to totally new hardware. Cold fusion, anyone?
- Searches algorithms directly — not words or tokens
- Unbounded — no public-data ceiling
- Domain- and stack-agnostic
- Searches over tokens — re-mixes what it has seen
- Bounded by the public-code corpus
- Fine-tuning requires your sensitive, precious IP
A new architecture for a new problem.
- Model class
- Algorithm-discovery foundation model
- Architecture
- Non-LLM · search over algorithmic constructs
- Training signal
- Test-pass · minimality · cost
- Output
- Deterministic software · test report · deployable code
Each call is a recursive search & score. The model proposes a candidate algorithm in response to test results.
In critical systems, it's simple: 100% passed tests means 100% correct software. So the model only provides software once every test is passed.
Want the deep version?
We share the white paper under NDA with investors, partners, and beta customers.