Our counter-proposal eliminates the highest-risk elements from your specification while maintaining the same business outcomes.
Risk Reduction
These high-risk elements from your specification are replaced with proven alternatives.
Original Risk: Formal verification requires scarce specialists ($200-400/hr) with 6-12 month unpredictable timelines.
Our Solution: Test-based determinism (N=10 runs) using standard engineering skills. Industry standard per CMU SEI, NeurIPS, ICML.
Original Risk: Residue Number System + Q31.32 is exotic math designed for FPGA, not software. Hard to debug, easy to introduce bugs.
Our Solution: Welford's algorithm—100 lines of Python, proven for 60+ years, used by PyTorch/NumPy/Pandas internally.
Original Risk: Three denoising models means 3× the determinism surface area to control and synchronize.
Our Solution: Single DeepFilterNet3 model with better quality metrics (PESQ 3.17 vs. ensemble 2.78-2.86) and 25× faster.
Remaining Risks
These risks remain but are well-understood with clear mitigations.
| Risk | Likelihood | Impact | Mitigation |
|---|---|---|---|
|
PyTorch Determinism Edge Cases Some GPU operations may not be deterministic |
Low | Medium | CPU fallback for problematic operations; well-documented in PyTorch 2.9 |
|
DeepFilterNet Quality Insufficient May not meet quality bar on specific audio |
Low | Medium | Test on client audio Week 2; quality escalation path defined (WPE tuning → post-filter → secondary model) |
|
DiskANN Scale Limits Potential issues at billion scale |
Low | Medium | PoC validates architecture; sharding strategy documented |
|
TON Integration Friction SDK issues or testnet instability |
Low | Medium | Well-established SDK; testnet validation in first sprints of development |
|
Client Rejects Test-Based Approach Insists on Coq proofs despite evidence |
Medium | High | Emphasize industry standard; offer Coq as post-MVP add-on if truly required |
Side by Side
Your specification vs. our counter-proposal.
Your Specification
HIGH RISK
Our Proposal
MEDIUM RISK