An artificial engineering
super-intelligence for
control software^

Hyperpilot^ is a platform of three one-shot AI engines that take a controls-engineering project from high-level intent to production-grade software. Powered by a foundation model that discovers algorithms — not an LLM.

The platform · three one-shot AI engines

Each engine takes a structured input and emits a structured output. No chat. No back-and-forth. Submit a job — get a result.

/ Input
specs · standards
regulations · or legacy code
/ 01 · Req.Gen
Req.Gen^
LLM

Generates a system architecture and fully decomposed traceable requirements. From prompts, data sheets, standards, even code.

/ Output412 requirements · 31 components
▾ Product · EV-Powertrain
▾ Sys · Inverter Control
· R-001 phase current ≤ 350A RMS
· R-002 fault response < 10ms
· R-003 PWM dead-time 200ns
▸ Sys · Battery Management
▸ Sys · Vehicle Supervisor
… +406 more
/ Input
requirements tree
from Req.Gen^
/ 02 · Test.Auth
Test.Auth^
LLM

Authors complete executable test suites that exhaustively verifies every last requirement. SiL, HiL, Bench, or Product-level Testing.

/ Output1,840 executable test cases
// R-002 fault response < 10ms
test("fault_short", () => {
  inject(short, t=0);
  assert(limit_active <= 10ms);
});
// + 1,839 more cases
// coverage · req 100%
// SiL · HiL · bench · product
/ Input
test suite
from Test.Auth^
/ 03 · SW.Syn
SW.Syn^
IN-HOUSE AI

Discovers a control algorithm that passes 100% of the test suite, guaranteed — no human in the loop. Don't be scared.

/ Output1,840 / 1,840 passing · ship
// inverter_ctrl.c · synthesised
void on_tick(state_t *s) {
  if (s->i_phase > I_MAX)
    s->limit = true;
  s->duty = pi(s->err, ...);
}
✓ 1,840 / 1,840 passing
// 0 human edits · ship
The V-cycle, automated

Where each engine sits on the V — and how autonomously it runs.

The three engines map onto the V-model of controls engineering. The V-model used to be an unfortunate by-product of a robust and safe engineering process; now, it's the reason you can scale.

322°000°038°VNV·04VNV·03VNV·02VNV·01
USER·D-01
Stakeholder Requirements
LLM·D-02
System Architecture Design
LLM·D-03
Software Architecture Design
LLM·D-04
Software Unit Design
USER·T-01
Acceptance Testing
LLM·T-02
System Testing
LLM·T-03
Integration Testing
LLM·T-04
Unit Testing
01 · LEFT ARM
Req.Gen^
SCOPE D-02 → D-04
02 · RIGHT ARM
Test.Auth^
SCOPE T-02 → T-04
HYPR.D-01
Development
03 · ORIGIN
SW.Syn^
SCOPE · V-CYCLE / HC-RDR-001SWEEP 038° · 3 ENGINES
Arms · engineer review
Req.Gen^ + Test.Auth^

The subjective transforms into objective — Agentic LLMs take the pain out of going from a high level down into detailed requirements and tests. Months of work reduced to a final pass of approval.

Origin · full automation
SW.Syn^

The implementation node. Powered by our algorithm-discovery foundation model — submit, ship, no human in the loop.

Top corners · user-defined
Stakeholder intent · acceptance

The “What” and “Why” of the product, plus final approval that it’s fit for purpose, stays with people.

The technology

An algorithm discovery foundation model.

SW.Syn^ is built on a new class of foundation model — one that searches the space of algorithms directly. Its search space is unbounded and application-agnostic, where an LLM's is bounded by the public code it was trained on. Production controls software isn't public — which is why LLMs struggle with this job.

/ LLM Copilots
/ SW.Syn^
Searches over
Tokens & words
Algorithms, directly
Search space
Public corpus, internal code
Unbounded
Application scope
Existing systems
Unbounded
True test-driven development
Watch

The full pipeline,
start to ship.

A four-minute walkthrough of Req.Gen^ → Test.Auth^ → SW.Syn^ on a real problem.

Backed by
/ Investor
Join Capital
join.capital
/ Investor
Octopus Ventures
octopusventures.com
/ Investor
Plug and Play
plugandplaytechcenter.com
/ Investor
tiny.vc
tiny.vc