When the software decides whether a patient's cardiac signal is clean — or whether audio evidence holds up in federal court — "it usually works" is not an acceptable specification.
Every serious acquisition reaches the same technical inflection point:
the acquirer's regulatory and engineering teams must be able to open the codebase,
trace every result to a defined requirement, and independently reproduce system behavior.
ChirpPoint systems are engineered so that this review succeeds on first pass —
with deterministic execution, stated error bounds, and complete traceability
from specification through verified binary.
Every ChirpPoint algorithm is a fixed mathematical computation. The same input produces the same output on every platform, every run, every driver version — without exception.
Worst-case error is a mathematical property of the algorithm — not an estimate from a test set. Any qualified engineer can verify it independently from the published procedure.
Design History Files, software bills of materials, risk management files, and verification indices. Built to IEC 62304 Class C from the first line of code.
No black boxes. No undocumented assumptions. No results that depend on training data or model weights. An acquirer's regulatory team can open the documentation and begin review immediately.
Both products share the same engineering discipline: ground-up design, strict numerical precision, and complete IEC 62304 Class C compliance documentation as a standard deliverable.
A unified GPU solver for IEC 62304 Class C medical devices — confirmed working on production silicon, April 2026.
A deterministic GPU computing system designed for IEC 62304 Class C environments, unifying real-time hemodynamic simulation and medical signal classification under a single verified FP64 numerical core, with full compliance documentation delivered alongside the system.
The first deterministic, platform-universal engine for optimal-domain audio signal analysis — with a mathematically guaranteed, stated error bound.
Prism finds the optimal analysis domain for each individual signal automatically, in real time. It separates any audio into clean and artifact components using the signal's own statistical structure — not a trained model or fixed filter. The result is a worst-case reconstruction error that is fixed, stated, and independently verifiable. The system is fully deterministic, with behavior reproducible across all supported platforms and inputs.
Architecture, verification structure, and confirmed results are presented here. Full algorithmic detail and source-level artifacts are available under mutual NDA.
The following sections correspond to materials typically reviewed during technical and regulatory due diligence.
Precision is not claimed at the C++ source level — it is verified in the compiled PTX assembly. The NVIDIA compiler can silently introduce floating-point approximations when optimization flags are active. ChirpPoint prohibits these at the architectural level and confirms the prohibition by auditing the PTX output directly.
Documents are built in parallel with the code under IEC 62304 Class C — the highest medical software lifecycle standard. They are not generated retrospectively. Each document listed below is a deliverable, not a promise.
Document set is maintained in parallel with development and version-controlled. All items listed are available for review under NDA.
Ten verification tests were run on a physical NVIDIA RTX 3080 (Compute Capability 8.6) under documented conditions (CPD-ENG-004). All tests passed. Numerical drift across all tests: exactly zero. Full test report, raw output logs, and hardware configuration are available under NDA.
FDA 510(k) clearance for Class II medical devices requires demonstrating substantial equivalence to a predicate device — including a comparable, stated error profile. A fixed mathematical algorithm with a bounded worst-case error can make this comparison directly. A neural network cannot.
This is not a minor regulatory detail. It is the reason deterministic algorithms are structurally superior to machine learning approaches in the regulated medical device market — and the reason ChirpPoint was designed this way from the first line of code.
The medical device and forensic audio industries are converging on the same regulatory requirement: deterministic results with a known error rate. ChirpPoint was built for exactly this moment.
Neural networks fail silently when input distribution shifts and cannot state a worst-case error. A fixed mathematical computation can answer both questions every acquirer and regulator will ask.
PTX binary audit — completed April 2026 — confirms full IEEE-754 precision compliance in the compiled library. Precision is not claimed at the source level. It is verified from the binary.
Design History Files, risk management files, software bills of materials, and verification indices are built in parallel with the code — not generated at the end. They are as engineered as the algorithms.
A deterministic algorithm with a stated error rate can be submitted for FDA 510(k) review using a predicate device comparison. A neural network cannot make that comparison. This is a structural market position.
Lattice-Q is engineered specifically for NVIDIA GPU hardware — optimized for the B200, H100, and A100 — with no abstraction layer between the algorithm and the silicon. Prism targets GPU, CPU, and ARM bare metal through a single API, covering every audio deployment context from cloud to embedded.
Every decision is traceable. Every result is reproducible. Every document is ready for regulatory due diligence. An acquirer does not inherit a black box — they inherit a fully auditable system.
We do not build software that passes tests.
We build software that cannot fail the tests that matter.
ChirpPoint Dynamics was founded on a straightforward conviction: the most demanding customers in the world — the ones whose software decisions affect whether patients live or die, or whether evidence holds up in court — deserve software built to match that standard.
The first approach produces products that work in the lab. The second produces products that work in the operating room and the courtroom. On April 4, 2026, the Lattice-Q engine ran 10 verification tests on real GPU hardware and passed all of them — with a numerical drift of exactly zero. That result is not a performance claim. It is a mathematical proof, confirmed in silicon.
The system is built using an architect-first methodology, where all constraints — numerical, regulatory, and system-level — are defined prior to implementation: automated tools serve the architecture. They do not define it.
Lucas J. Cannon
Founding and Managing Member · Chief Architect · Blaine, Minnesota
ChirpPoint systems are available for licensing, strategic acquisition, or integration into existing regulated platforms.
Complete source code, compliance documentation, and confirmed V&V build results are available to qualified parties under mutual NDA. Lattice-Q is confirmed on NVIDIA GPU hardware; Prism is confirmed on NVIDIA RTX 3080 and Apple M4.
Inquiry types welcomed: technical evaluation, licensing, strategic acquisition, government contracting, and investment.
Founding and Managing Member
ChirpPoint Dynamics, LLC