Transform Your Quant Finance Technology Stack

Discover which implementation route is right for you: modernize legacy systems, build new capabilities faster, or supercharge open-source frameworks. Choose your path to success with AADC.

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Challenges Quant Teams Face

Legacy performance bottlenecks

Traditional AAD slows code by 4×+. Real-time risk requires a breakthrough, not minor optimizations.

Escalating vendor costs

Vendor pricing increases but migration feels impossible.

Greenfield paralysis

Uncertain architectures create long-term risk, causing delays and technical debt.

Out-of-control cloud costs

Compute spending grows faster than business value.

Choose Your Implementation Path

Select the approach that best fits your quant finance needs

Legacy Optimization

Accelerate existing C++ and Python codebases without rewrites

Greenfield Development

Build new applications with performance and AAD built-in

Open-Source Acceleration

Supercharge QuantLib, ORE, and other open-source libraries

Cloud Optimization

Reduce cloud costs by 50-99% with optimized compute

Solutions by Role

Find the right approach for your team

Make Your Pricing Models 100x Faster

MatLogica doesn't replace quant models — we make them run 100-1000x faster with automatic Greeks. Keep your IP, your code, your competitive edge.

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AADC Solutions for Risk Management

AADC accelerates your risk models 6-1000x with automatic, stable sensitivities. Meet regulatory deadlines, improve P&L attribution, and make Monte Carlo affordable—using the models you already trust.

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AADC Solutions for Quant Technology Teams

AADC empowers quant technology teams to accelerate development cycles, reduce cloud infrastructure costs by 50-99%, and modernize legacy systems without risky rewrites. Build Python-based platforms with C++ performance, or accelerate existing C++ codebases with minimal changes.

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SIMM Benchmark Results

Production-scale ISDA SIMM benchmarks demonstrating real-time margin capabilities

AADC vs GPU: 47× Faster SIMM with Exact Gradients

AADC C++ computes ISDA SIMM sensitivities at 34,000 evaluations/sec on CPU — 47× faster than GPU pathwise differentiation on NVIDIA H100. GPU brute-force fails to converge at scale due to noisy finite-difference gradients.

  • 34,000 SIMM evaluations/sec with AADC C++ on CPU
  • 47× faster than GPU Pathwise on NVIDIA H100
  • GPU brute-force fails to converge at 500+ trades
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SIMM Optimization: 33K Evals/sec — 25× Faster than GPU

AADC C++ computes SIMM trade allocation gradients at 33,580 evals/sec — 25× faster than GPU. Optimizes a 50K trade portfolio in 19 ms with 1-2 iterations to converge.

  • 33,580 evaluations/second with AADC C++
  • 25× faster than GPU implementations
  • 19 ms to optimize 50K trade portfolio
View Benchmark

Explore Technology & Benchmarks

Deep-dive into the technology behind AADC and see real-world performance results

Python AADC vs Python, NumPy, C++: 2871× Faster Greeks

Python AADC delivers 2871× faster than Basic Python with minimal recording overhead. CRITICAL: Total eval time (recording + execution) is the true production metric. AADC's 0.067s recording means total_eval_time (0.74s) closely matches eval_time (0.67s), unlike JIT tools where compilation dominates.

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AADC vs GPU: 47× Faster SIMM with Exact Gradients

AADC C++ computes ISDA SIMM sensitivities at 34,000 evaluations/sec on CPU — 47× faster than GPU pathwise differentiation on NVIDIA H100. GPU brute-force fails to converge at scale due to noisy finite-difference gradients.

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AAD Technologies Comparison

Comprehensive comparison of Tape-based, Code Transformation, and Code Generation AAD approaches. Make an informed decision for your quantitative finance systems with detailed analysis of integration complexity, memory usage, performance characteristics, and scalability.

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Live Risk Architecture Blueprint

You've integrated AADC and now you're designing a full live risk system. This optional blueprint covers patterns from successful implementations — use what works for you, ignore what doesn't.

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Accurate Greeks for Autocallables: Production-Ready AAD with Smoothing

90% reduction in computational cost for autocallable Greeks using Automatic Adjoint Differentiation and mathematical smoothing. Complete open-source implementation available.

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ORE LiveRisk Case Study: 100x Faster with AADC

How to Turn ORE/QuantLib into a LiveRisk Service for FX Products: 1 Million Trades Priced in 0.4 Seconds with Complete Delta Risk

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Ready to Transform Your Quant Finance Stack?

See how AADC accelerates your specific use case. We'll run a benchmark on your actual models.

How It Works Schedule Technical Call