A complete side-by-side comparison of your specification versus our proposed approach—same outcomes, proven engineering, half the risk.
Full Comparison
Every technical decision, side by side.
Determinism Proof
How we verify reproducibility
Numeric Arithmetic
Math for incremental aggregation
Audio Enhancement
Denoising pipeline
Heavy-Path Scope
Components in official mix
MVP Scale
Voice count for validation
Float vs. Fixed
Numeric representation
Key Changes
Click through to the technical deep-dives for full justification.
Their Spec
Coq Proofs
Our Proposal
N=10 Test Suite
Carnegie Mellon SEI (2025): "Repeatability can be obtained by controlling random seeds." NeurIPS, ICML, ICLR use test suites, not formal proofs.
Coq Specialist Dependency
No longer need scarce formal verification experts.
Their Spec
RNS + Q31.32
Our Proposal
Welford's Algorithm
Welford's (1962) is used internally by PyTorch, NumPy, and Pandas. ~100 lines of code vs. complex RNS implementation. Both achieve O(1) updates with numerical stability.
RNS Implementation Bugs
Exotic math replaced with proven algorithms.
Their Spec
3-Model Ensemble
Our Proposal
DeepFilterNet3
DeepFilterNet3 achieves PESQ 3.17 vs. ensemble's 2.78-2.86. 25× faster (0.04 RTF vs 0.35+). Single model = simpler determinism. We have quality escalation path if needed.
Ensemble Determinism
Three models to sync reduced to one single model.
Their Spec
5M voices
Our Proposal
100K PoC
O(1) aggregation means 100K → 5M is identical code path. 10M synthetic DiskANN PoC validates billion-scale. Bug found at 5M would exist at 100K.
Timeline Overrun
X2-X3 less time required for building initial MVP
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