Greenfield Development

Build Quant Platforms 3-4x Faster

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

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Focus on Models, Not Infrastructure

Starting fresh? AADC lets you build with the tools your quants prefer while delivering the performance production demands. No premature optimization, no architecture paralysis—just write clear code and let AADC handle 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 optimization instead of modeling?
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

Use MatLogica AADC to develop C++/Python models faster, without optimization concerns. Get production-grade performance, automatic AAD, and real-time capabilities right from prototyping. Skip the months of architecture debates—start building today and scale tomorrow.

Python to Production
Write in Python, run faster than optimized C++
Production Performance Day One
No optimization phase needed—ship fast
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 optimization 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 transformed their struggling front-office risk infrastructure using 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 modeling 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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Legacy Optimization

Integrate AADC to modernize existing analytical systems with minimal code changes. Preserve institutional knowledge embedded in your codebase while achieving 6-100x speedup and automatic AAD.

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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 Optimization

AADC's 6-1000x efficiency improvement transforms cloud economics. Slash infrastructure costs by 50-99% while maintaining or improving performance. Secure binary kernel deployment keeps your IP protected.

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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-optimized 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?
AADC eliminates many architecture decisions that slow teams down. You don't need to choose between Python and C++—you get both. You don't need to plan for optimization phases—performance is built-in from day one. Start with clean, readable code and scale naturally.
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 customize for your specific needs rather than building from scratch.