Collaboration Established Between TT and SIGMA AI

Collaboration Established Between TT and SIGMA AI

Trading Technologies International (TT) and fintech company SIGMA AI have strengthened their partnership, with TT making a minority investment in SIGMA AI in July 2025. This collaboration aims to create AI-driven solutions that enhance the speed, intelligence, and user experience in the capital markets sector [1].

SIGMA AI is developing a proprietary AI and innovation hub within TT, which will provide global TT teams with innovative tools and processes to boost internal productivity. The hub will focus on real-time data integration and the application of AI to generate actionable insights that improve trading strategies, execution, and operational efficiency [1].

Andy Simpson, the founder and CEO of SIGMA AI, has taken on additional leadership responsibilities at TT, becoming the Head of AI and Innovation. His role will be instrumental in driving these developments further [1].

The partnership leverages TT’s global footprint and existing client base—consisting largely of blue-chip financial firms—to co-develop products that small fintechs like SIGMA AI might otherwise struggle to access. The focus is on real-time data integration and the application of AI to generate actionable insights that improve trading strategies, execution, and operational efficiency within fintech and capital markets [1].

According to a report from McKinsey, the AI revolution in asset management is seen as a timely opportunity to break out of entrenched cost structures by increasing efficiency across business functions [2]. In investment management, gen AI is transforming the way insights are generated and decisions are made, and can have an 8% efficiency impact [2].

Financial services businesses have been capitalising on AI developments for years, with $35bn spent on AI in 2023, and estimates reaching $97bn by 2027 [3]. Analysts are using gen AI-powered research assistants to synthesize data from earnings calls, financial reports, and conferences, accelerating the insight generation process [2].

Enhanced risk models and automated reporting are further supporting a more data-driven investment approach, according to McKinsey [2]. In client-facing roles, gen AI is enabling more seamless and personalized interactions, and can have a 9% efficiency impact [2].

The World Economic Forum and Accenture predict that AI will become a key feature of trading and investment, leading to a massive increase in productivity [4]. Portfolio managers are leveraging gen AI tools to refine strategies, narrow investment options, and optimize portfolio construction [2].

SIGMA AI has built fault checking and fact checking agents to look for common errors, and has also built its own feed handlers for unstructured data, such as news [5]. The company uses a RAG (retrieval-augmented generation) system to improve the reliability of its AI models [5].

Justin Llewellyn-Jones, chief executive of TT, stated that the partnership will deliver AI-driven solutions to TT's clients [1]. The partnership between TT and SIGMA AI represents significant progress in combining AI with real-time financial data to advance fintech capabilities in capital markets.

References: [1] Markets Media report, 2025 [2] McKinsey report, 2024 [3] World Economic Forum and Accenture report, 2023 [4] McKinsey report, 2022 [5] SIGMA AI website, 2025

1. The partnership between Trading Technologies International (TT) and fintech company SIGMA AI, backed by a minority investment, aims to create AI-driven solutions that boost internal productivity and improve trading strategies, execution, and operational efficiency in the capital markets sector using artificial-intelligence technology.
2. Financial services businesses have been capitalizing on AI developments, with spending on AI anticipated to reach $97bn by 2027, as analysts leverage AI-powered research assistants to synthesize data from earnings calls, financial reports, and conferences, expediting the insight generation process.
3. SIGMA AI has developed fault checking and fact-checking agents to identify common errors and built its own feed handlers for unstructured data like news, enhancing the reliability of its AI models using a retrieval-augmented generation (RAG) system.

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