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.
This page is a controlled disclosure subset. Full system architecture, algorithmic implementation, and internal design are not disclosed here.
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. Verification protocol and numerical bounds are available to qualified parties under executed NDA.
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.
Daubert admissibility requires a known error rate, a testable method, and a forensic record. Every Prism processing run satisfies all three by construction. Every Lattice-Q result is reproducible to the bit on the hardware it was validated on.
FDA 510(k) predicate comparison requires a stated, bounded error rate. A deterministic algorithm has one by mathematical definition. A neural network does not — and cannot acquire one by retraining. This is not a feature advantage. It is an architectural one.
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.
The first commercially available, audit-ready unified GPU solver for IEC 62304 Class C medical devices — confirmed 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 inherent properties — 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. Algorithmic implementation is not disclosed in this document.
This section presents a limited, controlled view of architecture and verified results. Core algorithmic implementation, internal design, and numerical bounds are not present in this document.
Architecture, verification structure, and confirmed results are presented at a high level. Detailed algorithmic behavior, validation datasets, and source-level artifacts are maintained in a controlled disclosure environment and made available to qualified parties under mutual NDA as part of formal technical due diligence.
The following represents a controlled subset of materials available during formal technical and regulatory due diligence. Full detail requires executed NDA.
IEEE-754 precision compliance is enforced at the binary level and independently verified against the compiled output. The specific enforcement mechanisms, compiler constraints, and audit methodology are not disclosed in this document — they are available under executed NDA as part of the technical due diligence package.
The documentation set listed below exists and is maintained in a review-ready state. Each item is a confirmed deliverable. Content, structure, and version history are not disclosed in this document — access is restricted to qualified parties under executed NDA as part of formal due diligence.
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-006). All tests passed. Numerical drift across all tests: exactly zero. Full test report, raw output logs, and hardware configuration are available for review under mutual NDA as part of technical due diligence.
The IEC 62304 V&V suite confirms determinism and reproducibility — that the same input produces the same output. The physics validation suite confirms something distinct and additional: that the engine produces correct physical results on real, varying input. Reproducible wrong physics would pass V&V and fail physics validation. Both suites are required for medical deployment confidence.
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 8, 2026, the Lattice-Q engine completed its REV 2 validation — 10/10 V&V tests at exactly zero numerical drift, plus a physics validation suite confirming correct physical results against peer-reviewed benchmarks. That combination is not a performance claim. It is documented, reproducible engineering, 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 maintains a complete, audit-ready documentation and verification package. Access is provided to qualified parties under mutual NDA as part of formal technical and regulatory review.
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