Self-Hosted Runtime + Canadian R&D

Strip layers between
intent and silicon.

Synapse builds downward: fewer frameworks, less cloud overhead, tighter deterministic control. Today that means a self-hosted runtime for evaluators, reward functions, policy gates, and other bounded AI-generated logic. A supported Python subset compiles to WebAssembly, runs in a tiny sandbox, and can be verified with Z3. On top of that runtime, the governed control-plane shell adds redaction, receipts, replayable proof packs, and bounded escalation for design-partner workflows. The hosted endpoint is a preview. The product is the stack you deploy yourself.

47x on documented Python-subset workloads3 verifiable invariants todaybuild downward
Why Downward

Available Today

Self-hosted deterministic execution for bounded generated logic. Start with evaluators, reward functions, policy gates, and auditable transforms.

Fewer layers. Lower overhead. Focused execution surface.

Research Direction

The same stack also supports the deeper R&D vector: transpilation, formal verification, receipts, model-improvement loops, energy efficiency, and the long-horizon sovereignty path toward bare metal.

Downward means more control, more assurance, and eventually more sovereignty.

Don't take our word for it.

Use the hosted preview to evaluate the runtime before self-hosting. Production deployments run on your own gateway. This demo shows the same bounded execution model on a public evaluation surface.

⚡ Two-Tier Execution Engine
RUSTArithmetic, loops, if/else, builtins — sub-millisecond
PYTHONClasses, imports, stdlib, try/except — ~72ms via CPython-WASI
Both paths produce cryptographic execution receipts. 67–133× faster than cloud sandboxes for compute tasks.
Source
Pipeline
Python → .syn
Z3 Verify
.syn → Wasm
Wasm → Result
Output
Execute the pipeline to see output
Measured Runtime + Research
47x
Python-subset speedup
48pp
.nerve advantage over weight matrices
45+
R&D phases journaled

Benchmarks and research claims are tied to documented hardware, journaled experiments, and reproducible commands. Start with the runtime, then go deeper into the research.

Deploy the runtime. Read the brief. Explore the research.