Ship faster, spend less, keep the legacy code

Your models. Your IP. Dramatically faster.

Build in Python and run at C++ speed, or keep the C++ and change very little of it. Either way the cloud bill drops and the old library does not have to be rewritten.

Schedule Technical Call See Challenges We Solve

What MatLogica Does

We don't replace quants or provide models.
We empower quants to build and run models 6-1000x faster - with automatic Greeks.

Your Python/C++ models AADC Compiler 6-1000x faster + auto AAD

The Transformation

Same models. Same methodology. Your IP protected. Transformed performance.

✗ Quant Development Today

⏱️
Model development Months of optimisation work
🐍
Python "Too slow for production"
📊
Greeks Manual AAD or bump & revalue
🏗️
Greenfield builds Always postponed - "too expensive"
💰
Cloud/compute costs Spiraling upward

✓ Quant Development With AADC

⚡
Model development 3-4x faster to production
🐍
Python Faster than hand-optimised C++
📊
Greeks Automatic - adjoint factor <1
🏗️
Greenfield builds Production in <1 year
💰
Cloud/compute costs 50-99% reduction

You keep your models, your IP, your competitive edge. We just make everything faster.

Sound Familiar?

Common challenges we help quant teams solve

Management wants vendor solutions, but you want to keep IP in-house?

Build in-house with AADC. Get vendor-class performance while retaining full control of your models and methodology.

→ 200x faster XVA, $0 vendor costs

Models too slow and complex for production?

AADC accelerates your existing code 6-1000x with minimal changes. No rewrite required.

→ <1% code changes

Greenfield build always postponed?

Python-first development with AADC means production-ready platforms in months, not years.

→ Production in <1 year

Python too slow, but C++ legacy is a barrier?

AADC Python outperforms hand-optimised C++. Use Python for new development, accelerate C++ legacy.

→ 277x faster Python

Can you say what the agent-written code actually computed?

Set AADC_TRACE=1 and the run records every step with the line that produced it. A change is checked against the record of what ran, not against the diff.

→ AADC Tracer

Choose Your Implementation Path

Select the approach that best fits your quant technology needs

Legacy Optimisation

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 Optimisation

Reduce cloud costs by 50-99% with optimised compute

"MatLogica's product changes the paradigm for quantitative software development eliminating the need to invest in optimisations. Quants can now focus on the models, and performance will be taken care of by MatLogica's JIT compiler."

Paul A. Bilokon

CEO, Thalesians Ltd & Visiting Professor, Imperial College London

Let's Be Clear

✗ We Don't Replace

  • Quants or quant teams
  • Your pricing models
  • Your risk methodology
  • Your proprietary IP
  • Your development process

Your team, your models, your edge.

✓ We Empower

  • Quants to build models 3-4x faster
  • Models to run 6-1000x faster
  • Python to match C++ performance
  • Teams to use automatic Greeks
  • Organisations to cut cloud costs 50-99%

More output, less infrastructure.

SIMM Performance Benchmarks

CPU vs GPU throughput on production-scale ISDA SIMM workloads

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
View Benchmark

SIMM Optimisation: 33K Evals/sec, 25× Faster than GPU

AADC C++ computes SIMM trade allocation gradients at 33,580 evals/sec, 25× faster than GPU. Optimises 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 optimise 50K trade portfolio
View Benchmark

Solutions by Role

Accelerate Python Models 345× with AADC

Prototype in Python, achieve C++ performance. Use AADC to accelerate your existing Python pricing models with minimal code changes, no rewrites required.

Learn More

Your pricing models, 100x faster

MatLogica does not replace your models. It makes them run 100 to 1000x faster, with the Greeks computed in the same pass. Your code and your IP stay yours.

Learn More

AADC Solutions for Risk Management

Your own risk models, 6 to 1000x faster, with stable sensitivities from the same run. Regulatory deadlines met, cleaner P&L attribution, full Monte Carlo where you used approximations.

Learn More

Next step

Read how it works, or book a technical session with the engineers who did the integrations.

How It Works Schedule Technical Call

Or explore: How AADC Works | Full Benchmark Details | Case Studies

Frequently Asked Questions

Can Python really be fast enough for production quant systems with AADC?
Yes, AADC can accelerate Python code up to 1000x, achieving performance that exceeds hand-optimised C++. This enables building complete Live Risk platforms in Python that go to production in less than 1 year. AADC integrates with NumPy and provides automatic AAD for Python workflows.
How much can AADC reduce cloud infrastructure costs?
Typically 50 to 99%, because the same workload runs 6 to 1000x faster and needs far fewer instances for less time. Models can also stay on-premises while only the compiled kernels go to the cloud.
Can AADC modernise legacy C++ code without a full rewrite?
Yes, AADC accelerates existing C++ code 6-100x with typically less than 1% code changes. This preserves the institutional knowledge embedded in your codebase while delivering modern performance. You can modernise incrementally without risky big-bang rewrite projects.
How does AADC accelerate QuantLib and ORE?
AADC has demonstrated 245x acceleration of ORE (Open Source Risk Engine). It provides vendor-class performance while maintaining the flexibility and control of open-source. AADC automatically adds AAD capabilities to QuantLib models, enabling complete Greeks computation.
What programming languages and platforms does AADC support?
AADC supports C++ (11/14/17/20) and Python (with NumPy/SciPy integration). It works with standard compilers (GCC, Clang, MSVC) without special toolchains. AADC runs on Linux and Windows, and can be deployed on any cloud provider (AWS, Azure, GCP) or on-premises.
What is the typical ROI timeline for AADC adoption?
Most organisations see ROI within 6-12 months through a combination of infrastructure cost savings (50-99% reduction), reduced development time (5x faster delivery), and avoided rewrite costs. The exact timeline depends on your starting point and implementation path.