Greenfield Development

Build Quant Platforms 3-4x Faster

Python productivity with C++ performance. Automatic AAD built-in. Focus on models, not optimisation. Ship to production in months, not years.

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Write the model, not the optimisation

Starting from nothing, your quants can build in the language they prefer and still get production speed. No optimisation phase and no architecture stand-off: write clear code and let the compiler do the performance.

Common Challenges

Sound familiar? These greenfield hurdles slow teams down

Paralysis by analysis on architecture decisions?
Team knows Python but it's too slow for production?
Months spent on optimisation instead of modelling?
Fear of technical debt from day one?
Unsure which framework will scale?
Over-engineering before you even start?
Scalability regrets when growth hits?
Balancing Python ease with production speed?

The AADC Solution

Develop C++ or Python models without an optimisation phase. Production performance, AAD and real-time capability are there from the prototype. The architecture debate gets shorter because performance is no longer the thing being argued about.

Python to Production
Write in Python, run faster than optimised C++
Production Performance Day One
No optimisation phase
Automatic AAD Built-In
Greeks and sensitivities without extra work
Months, Not Years
Live Risk platforms in production in <1 year

Perfect For:

Greenfield projects and new initiativesTeams wanting Python productivity with C++ performanceFirms building proprietary trading/risk systemsHedge funds and fintechs building competitive advantagesTeams that have postponed builds due to complexity concerns

Key Benefits

3-4x Faster Development

Ship features faster by focusing on business logic

Production Performance Day One

No optimisation phase needed

Automatic AAD Built-In

Greeks and sensitivities without extra work

Python to Production

Use Python without performance penalties

Proven Results

Client-Results

Tier-2 European Bank Achieves 15-20x Faster Risk with 70% Less Code

A Tier-2 European bank fixed the performance of its front-office risk infrastructure with MatLogica AADC. They achieved 15-20x speedups compared to manual adjoint differentiation, reduced overnight batch from 8+ hours to 2 hours, and eliminated 70% of their modelling codebase while unlocking new revenue streams.

  • ›15-20x faster risk vs manual adjoints
  • ›Overnight batch: 8+ hours to 2 hours
  • ›70% codebase reduction
  • ›50% grid cost savings
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SIMM Benchmark Results

Production-ready SIMM performance from day one

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

Legacy Optimisation

AADC goes into the analytics you already have, with few code changes. The years of business logic in the code stay; the run time drops 6 to 100x and the Greeks come with it.

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Open-Source Acceleration

Integrate AADC with open-source frameworks like QuantLib and ORE to get community validation plus proprietary-grade performance. No vendor licensing fees, automatic AAD for all models.

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Cloud Cost Optimisation

A workload that runs 6 to 1000x faster needs a fraction of the instances for a fraction of the time: 50 to 99% off the bill. Only compiled kernels go to the cloud; the source stays home.

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Ready to Build Your Platform?

See how AADC can accelerate your greenfield development with a technical demonstration.

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Frequently Asked Questions

Can Python really be fast enough for production trading systems?
Yes, with AADC Python Accelerator, your Python code can achieve performance that exceeds hand-optimised C++. Teams have built complete Live Risk platforms in Python that went to production in under a year.
How does AADC compare to building our own AAD?
Building production-quality AAD typically takes 2-3 years and significant expertise. AADC provides this capability immediately, letting your team focus on business logic rather than infrastructure. You also benefit from continuous AADC improvements.
What if we need to switch from Python to C++ later?
AADC provides a consistent programming model across Python and C++. You can start with Python for rapid development and move performance-critical paths to C++ incrementally. The same AADC kernels work in both environments.
How do we avoid architecture paralysis?
Several of the decisions that slow a new build down go away. You do not have to choose between Python and C++; you can use both in one kernel. You do not have to plan an optimisation phase; the compiled recording is already fast. Start with readable code and scale it.
What reference implementations are available?
We provide reference implementations for common quant workflows including Monte Carlo pricing, portfolio risk aggregation, and Greeks calculation. These let you start fast and customise for your specific needs rather than building from scratch.