Software infrastructure for the AI-native asset manager

Singularity Technologies is a Singapore software company building execution, risk and research infrastructure for professional asset managers.

Our software is designed for AI-driven trading within robust guardrails built for institutional governance and oversight.

What we build

Building infrastructure for the AI-native asset manager, from research to execution to risk.

System architecture diagram showing the flow from Strategy Layer through Risk & Governance Engine to Execution Engine and trading venues

Execution & Order Management Infrastructure

Execution infrastructure handling order submission, routing, and lifecycle management across multiple venues.

Multi-venue order routing with real-time position tracking and complex order types including spreads and multi-leg strategies.

End-to-end latency monitoring and performance analytics with microstructure-aware execution.

e.g. multi-venue spread execution with automatic hedging when legs fill asynchronously, all flowing through authoritative risk validation.

Authoritative Risk & Governance Layer

Authoritative pre-trade risk system that validates orders before execution. Not post-trade alerts — actual hard gates that block invalid orders.

Multi-dimensional limit enforcement across actor, instrument, venue, and exposure dimensions with policy-driven envelope contracts.

Comprehensive audit trails with standardized metadata for explainability and compliance review.

e.g. hedge orders validated against signed envelope contracts specifying allowed venues, expiry, and proof of exposure reduction — rejected if invalid.

AI Research Agents Roadmap

Planned: Autonomous agents that scan markets, generate trade ideas, and support macro, equity, and systematic research.

Will integrate with our execution and risk infrastructure to enable AI-driven research workflows.

Human PMs retain control over objectives, mandates, and final decisions.

e.g. agents that propose and backtest new basis trades across futures curves.

Current development

Where the software stands today. We distinguish deliberately between what runs and what is still being built.

Operational today

  • Multi-venue execution and order routing with order lifecycle management
  • Authoritative pre-trade risk validation — orders are checked against limits before reaching a venue
  • Independent book of record for position, PnL and NAV reconciliation
  • Reference data and instrument lifecycle management, with controlled propagation downstream
  • Normalised market data collection, with a schema-enforced historical data catalogue
  • Shared analytics services for options pricing, PnL and volatility surfaces
  • Operator interfaces for order entry and curve analysis behind a single authenticated gateway

Engineering discipline

Practices implemented across the platform. Production deployments are governed by explicit readiness gates and automated testing.

  • Role-based access control with enforced separation of duties between reference data, risk approval and trading
  • Audit logging of privileged actions, recording actor, action, state transition and reason
  • Automated test suites gating each release
  • Static type checking enforced in core services, with performance-critical components in Rust
  • Staged deployment with explicit production-readiness gates

In development

  • Research and backtesting harness for systematic strategy validation
  • Deterministic simulation engine for reproducible backtests
  • Systematic decision and quoting engines
  • AI research agents Roadmap

Who we build for

Hedge funds

For funds pushing the frontier of systematic and discretionary trading.

Accelerate idea generation, improve execution, and industrialise internal research workflows.

Institutional & real-money

For asset owners and long-only managers who need AI without sacrificing governance.

Focus on transparency, explainability, and policy alignment with long-term mandates.

Our vision

We believe the asset manager of the future is AI-native. Most marginal decisions — what to research, which signals to test, how to route orders, when to rebalance — will be taken by machines.

Humans will define objectives, risk appetite, and constraints. Infrastructure will encode these into systems that are observable, testable, and designed for institutional governance.

We are building execution and risk infrastructure from the ground up, designed to support AI-driven trading decisions from day one.

Singularity Technologies exists to build this infrastructure: technology that makes AI-driven investing robust enough for both hedge funds and real-money institutions.

Who we are

Singularity Technologies is an early-stage Singapore software company focused on the intersection of quantitative finance and generative AI.

We are a software company, not an investment manager: we do not currently provide investment advice or manage client capital. We build execution, risk and research infrastructure for professional asset managers, designed to survive contact with real markets, regulatory scrutiny and real capital. We plan to use our own proprietary trading as a testbed so that the systems we build are exercised in live conditions.

Our architecture is shaped by deep experience in quantitative trading. We build with automated testing, structured threat modelling and comprehensive documentation — the engineering discipline institutional deployment demands.

Founder

Nicolas Bentz

Interest-rates derivatives trader with macro and quant focus. Software engineer by training. 10+ years trading G3 rates and options, running macro portfolios, and building models close to risk and execution.

Selected experience

  • Symmetry Investments — Assistant Portfolio Manager, G3 short-end & global macro
  • Deutsche Bank — Rates & Options Trader, 6 years in London
  • Sagem / Total — C++ Developer & Project Manager

Education & credentials

  • MBA, Wharton School
  • MSc Engineering, Télécom Paris
  • CFA Charterholder
Read full founder biography

Singularity Technologies was founded by Nicolas Bentz, an interest-rates derivatives trader with a macro and quant focus, and a software engineer by training. He has spent more than a decade trading short-end G3 rates and options, running macro portfolios, and building tools and models that sit close to risk and execution.

Most recently, Nicolas was an Assistant Portfolio Manager at Symmetry Investments in Singapore. There he generated trade ideas in G3 short-end and global macro, managed an autonomous carve-out portfolio, and built models in Python and Excel to value LIBOR/SOFR fallback scenarios and to recombine risk system outputs into synthetic risk views used in day-to-day portfolio management.

Earlier in his career, Nicolas spent six years on the interest-rate desk at Deutsche Bank in London. He first traded short-term rates — FRAs, swaps, basis swaps and OIS — in GBP, USD and EUR, acting as a main market maker and developing relative-value, carry and mean-reversion approaches. He then moved to the interest-rate options desk, market-making short-dated EUR and GBP gamma (swaptions, cap/floors, CMS) for major hedge funds and implementing global macro views with options structures.

Before moving into markets, he worked as a C++ developer and project manager in the tech and energy sectors. At Sagem (Safran Group) he developed cryptographic and biometric algorithms and managed international smartcard projects. At Total he led a USD 10m billing-system build, part of a larger USD 50m renewal of the company's European fuel card platform, supervising specifications for systems that process billions in annual customer billings and complex fiscal and commission flows across multiple countries.

Nicolas holds a Master of Science in Engineering (Computer Science major, Economics minor) from Télécom Paris and an MBA from the Wharton School. He is a CFA Charterholder and has long-standing practical experience in programming (Python, C/C++, Matlab, SQL) and applied machine learning for financial markets.

Contact

Interested in this direction or exploring similar problems? We're happy to compare notes and discuss where AI-native infrastructure for trading and investment might go.

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