Service
Engineering low-latency, high-frequency trading systems that execute complex strategies with sub-millisecond precision.
Modern alpha generation is a battle of microseconds. Legacy systems create a latency gap that erodes profitability. We close it by engineering deterministic, high-speed trading platforms from the ground up.
C++/Rust kernels and FPGA acceleration.Reduce trade latency by 60-80% and increase strategy backtest accuracy with real-time data simulation.
Our development integrates directly with your existing risk and compliance frameworks, ensuring audit-ready systems that meet FINRA and MiFID II standards. We specialize in building the core engines for high-frequency trading (HFT), statistical arbitrage, and market-making strategies.
Our algorithmic trading system development is engineered to deliver concrete, measurable improvements to your trading operations, from latency reduction to enhanced strategy execution.
Engineer low-latency trading pipelines with colocated infrastructure and optimized reinforcement learning agents, achieving execution speeds under 1ms to capture fleeting market opportunities.
Deploy ML-driven market microstructure analysis and risk-parity models to dynamically adjust portfolio allocations, targeting a measurable increase in risk-adjusted returns.
Implement intelligent order routing and execution algorithms that minimize transaction costs and information leakage, preserving alpha across large orders.
Leverage high-performance compute infrastructure to accelerate strategy simulation and validation, compressing development cycles from months to weeks.
Deploy fault-tolerant systems across multiple availability zones with automated failover, guaranteeing operational continuity and meeting stringent uptime SLAs.
Our phased approach to algorithmic trading system development ensures predictable delivery, clear milestones, and measurable ROI at each stage.
| Phase & Deliverables | Strategy & Design | Core System Build | Advanced Integration & Go-Live |
|---|---|---|---|
Market Microstructure Analysis & Strategy Backtesting | |||
Low-Latency Execution Engine Development | |||
Reinforcement Learning Agent Integration | |||
Real-Time Risk & P&L Dashboard | Design Spec | MVP | Full Deployment |
Direct Market Access (DMA) & Exchange Connectivity | 1-2 Venues | Multi-Venue with Smart Order Routing | |
Latency Benchmarking | < 100µs Target | < 50µs Achieved | < 20µs Optimized |
Support & Iteration | Weekly Check-ins | Dedicated Engineering Lead | 24/7 Production Support SLA |
Typical Timeline | 2-4 Weeks | 6-10 Weeks | 4-8 Weeks |
Investment Focus | Feasibility & Architecture | Core Alpha Generation | Scale, Optimization & Monitoring |
We build deterministic, high-performance trading systems using a rigorous, four-phase framework designed to de-risk development and accelerate your time-to-market. Our process integrates directly with your quant research and trading desks.
We translate your quantitative research into executable, deterministic code. Our engineers build high-fidelity backtesting engines that account for market microstructure, transaction costs, and latency to validate strategy alpha before a single line of production code is written.
Learn more about our approach to Financial Time Series Forecasting.
Engineering of the core execution system in C++/Rust for sub-millisecond order placement. We optimize every layer—from kernel-bypass networking and FPGA/ASIC integration to direct market access (DMA) connectivity—to minimize slippage and maximize fill rates.
Concurrent development of a real-time risk gateway that enforces pre-trade limits, position checks, and P&L thresholds. We integrate Market Manipulation Pattern Recognition AI to monitor for spoofing and layering, ensuring regulatory compliance is built-in, not bolted-on.
We manage the full deployment lifecycle into co-location facilities or cloud environments, establishing continuous integration for strategy updates. Our team provides ongoing performance monitoring and latency optimization, treating the trading system as a living asset.
Explore our AI Model Risk Management services for production governance.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
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Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Answers to common questions about our process, timeline, security, and support for building high-frequency trading systems.
We follow a structured, four-phase engagement model designed for low-latency systems:
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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