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Your paper implementation has a bug.
We'll fix it.

Specialized debugging for ML teams replicating research papers. We trace the math, find the mismatch, and deliver a tested fix with a plain-English explanation.

# Before: Paper says W @ x + b, code does x @ W + b  (transposed!)
- output = torch.matmul(x, self.weight) + self.bias
+ output = torch.matmul(self.weight, x) + self.bias

# Radar fix: aligned matrix multiplication with Eq. 3 in paper
# Added shape assertion to catch future regressions
+ assert output.shape == (batch_size, d_model), f"Expected {(batch_size, d_model)}"

Sound familiar?

Every ML team building on research papers hits these walls. You're not alone.

!Spent 3 weeks debugging a single tensor shape mismatch
!The paper's pseudocode doesn't match the released code
!Training diverges at epoch 50 and nobody knows why
!The open-source repo is abandoned and full of issues
!You found 4 different implementations, all producing different results
!The loss converges but the outputs are garbage

How it works

From bug report to verified fix in days, not weeks.

01

Submit Your Bug

Share your paper implementation, describe the issue, and point us at the failing code.

02

We Diagnose

Our ML engineers trace through the math, the code, and the paper to find the root cause.

03

Fixed-Price Quote

You receive a clear quote based on complexity. No surprises, no hourly billing.

04

Fix Delivered

We deliver the fix with a plain-English explanation and validation tests you can run.

Transparent pricing

Fixed-scope quotes. Pay only when the fix is validated and working.

Quick Fix

$500
1-2 days turnaround

Single isolated bug

  • Shape mismatch
  • Wrong activation
  • Off-by-one in indexing
Get Started
Most popular

Deep Diagnosis

$2,000
3-5 days turnaround

Complex multi-component bug

  • Training instability
  • Numerical precision issues
  • Architecture misalignment with paper
Get Started

Full Pipeline Review

$5,000
1-2 weeks turnaround

End-to-end implementation audit

  • Complete implementation validation
  • Performance optimization
  • Paper-to-code alignment check
Get Started

What clients say

We'd been stuck on a NeRF variant for two weeks. Radar found the bug in the ray sampling code in under 3 hours. Worth every penny.

Sarah Chen
ML Engineer, Percepto AI

The fix came with a clear explanation of where the paper's notation was ambiguous. We learned as much as we saved in debug time.

Marcus Rivera
Research Engineer, DeepScale

Fixed a critical gradient computation error in our diffusion model pipeline. The validation tests they provided caught two other latent bugs.

Anika Patel
CTO, SynthLab

Stop debugging. Start shipping.

Book a free 30-minute consultation. We'll assess your bug and give you an honest scope estimate.

Book Free Consultation