6 to 1000x faster and every derivative included, for less than 1% of the code changed. You work on the model; AADC does the performance and the AAD.
An adjoint factor below 1 means the compiled kernel returns the value and every derivative in less time than the original code took for the value by itself.
All complicated by decades-old legacy systems
AADC records the numerically heavy part of your code as it runs and compiles the recording to machine code, derivatives included. The same mechanism, MatLogica's patented JIT graph compiler, is what gives you the speed, the Greeks and the integration path into a legacy library.
6 to 100x faster Monte Carlo, scenarios and what-if analysis, which is what cuts the cloud bill
Every Greek and sensitivity, adjoint factor below 1, no differentiation by hand
Less code to maintain, fewer bugs, faster turnaround, because nobody is hand-optimising
A type replacement and a small API; results in weeks
Everything you need for production-grade quantitative development
Proprietary JIT Compiler
Automated Migration
Reverse Debugger & Diagnostics
Handle Complex Control Flow
Calibration Without Refactoring
Production-Ready Blueprints
Complete API reference, integration guides, and code examples
AADC compiles your pricing code to machine code with exact derivatives: 6× to 1000× faster, every Greek in one pass, no rewrite.
Accelerate existing C++ and Python codebases without rewrites
Build new applications with performance and AAD built-in
Supercharge QuantLib, ORE, and other open-source libraries
Reduce cloud costs by 50-99% with optimised compute
"It's hard to develop well-performing models in C++. I've been very impressed by performance gains up to two orders of magnitude obtained by MatLogica after some integration work on QuantLib, and by the fact that the same work also enabled AAD; especially considering that the library contains hundreds of thousands of lines of code developed over more than 20 years."
Luigi Ballabio
Co-founder and administrator of QuantLib
"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
"MatLogica offers a graph-based framework in C++, optimised for finance applications on modern hardware aware and complete with state-of-the-art AAD and proprietary on-the-fly compilation. Quants will find that MatLogica seamlessly fits with their libraries and effortlessly accelerates pricing and risk by impressive amounts."
Antoine Savine
Author of "Modern Computational Finance"
"The approach MatLogica takes to acceleration is novel in both its easy-to-use programming interface and high performance it achieves out-of-the-box. Straight-forward and minimal code changes, to make use of the libraries, offer leaps in performance for both xVA Pricing and Greeks Calculations."
Mahesh Bhat
Principal Engineer, Intel
Organisation-Wide Deployment
Contact us for pricing based on your organisation size and use cases
Individual Development
Perfect for evaluation and individual use cases
Free Options: Demo licences for evaluation • Academic licences for educational institutions
See how AADC can transform your quantitative development
The AADC Toolkit is a quantitative development suite built on MatLogica's proprietary JIT graph compiler. It delivers 6-1000x speedups and automatic derivatives with less than 1% code changes. The toolkit includes six core components: AADC Engine (JIT compiler), Integration Scripts, Debugging Toolkit, Branch Manager, AIFT Solver, and Reference Implementations.
AADC uses Code Generation AAD technology with a proprietary JIT compiler (not LLVM) that compiles computational graphs to highly optimised machine code with automatic vectorisation (AVX2/AVX512) and multi-threading. This means computing your function plus all derivatives is actually faster than your original code computing just the function alone. Typical adjoint factors range from 0.3-0.8.
The AIFT (Automated Implicit Function Theorem) Solver enables derivatives through optimisation and calibration without code refactoring. It was #1 most-read on Risk.NET for over a quarter and is a key technique for Live Risk implementations. It allows AAD to work through iterative solvers and calibration routines automatically.
AADC includes a debugging toolkit with a reverse debugger that can compare kernel values against original code, monitor adjoint propagation, detect numerical discrepancies, troubleshoot bump & revalue issues, and track intermediate variables throughout the calculation graph.
The Branch Manager handles if-statements and discontinuous payoffs (barriers, autocallables) through two mechanisms: static branches are hard-coded into the kernel at compile time, while dynamic branches use bool to ibool conversion for runtime flexibility. It includes automated conversion reports and smooth approximation options for discontinuous payoffs.
AADC provides open-source reference implementations on GitHub including Phoenix autocallables with full AAD, XVA frameworks, American Monte Carlo using Longstaff-Schwartz, and Python DSL for rapid prototyping. These serve as production-ready blueprints for common quantitative finance applications.
AADC provides full support for C++ and Python, with C# also supported. Java support is currently in progress. The mix-mode execution feature allows unified integration of C++, Python, and C# within a single kernel.
AADC runs on Intel and AMD CPUs with automatic vectorisation for AVX2 and AVX512 instruction sets. GPU support is on the product roadmap. The JIT compiler automatically optimises code for available hardware capabilities.
AADC offers flexible licensing: Enterprise licences for organisation-wide deployment with full support and integration consulting, and Desk/Machine-Bound licences for individual development and prototyping. Free demo licences are available for evaluation, and academic licences for educational institutions.