Opening & Welcome
Andrew Delaney, President & Chief Content Officer, A-Team Group

Keynote Fireside Chat: Navigating the next frontier – A buy and build perspective from JP Morgan Asset Management

Interview with: Ashwin Venkatraman, Managing Director and Head of Asset Management Trading Technology, JP Morgan Asset Management
Interviewed by: Irina Sonich Bright, Managing Director, Head of Market Access Product, Agency Trading, UBS

Panel: The new buy/build calculus

• How has AI changed the economics of the buy/build decision in practice – and where is the line actually moving?
• What does partnership mean when vendor capability changes every quarter?
• How are firms restructuring SLAs and third-party risk assessments to mitigate cyber risks from vendors?
• When does buying still win – and what are firms underestimating about the true cost of building, even with AI assistance?
• How are vendors repositioning as AI commoditises capabilities that used to be their moat?
• Who owns the buy/build decision now – is it still the CTO, or has it moved to the business?

Moderator: Seema Arora, Former MD, Head of Execution Services, Stifel
Cathy Gibson, Global Head of Trading, Ninety One
Anvar Karimson, Group Chief Technology Officer, Kepler Chevreux
Will Brown, Manager, Sales Engineering, Genesis Global

Fireside Chat: Building the data infrastructure behind modern trading with Marex and QuestDB

• How is the shift to automated trading reshaping next-gen data needs before, during, and after a trade?
• Why must architecture & backtesting engines share the same underlying data to avoid costly live-trading slippage?
• How should systematic desks navigate buy vs. build for quants—what belongs in-house, what should be bought, and where does open source win?
• What key factors are critical to driving platform adoption among traders and quants—and what are the architectural mistakes to avoid?
  
  Interview with: Julius Uotila, Senior Quant, Marex
  Interviewed by: Nicolas Hourcard, CEO, QuestDB

Keynote: Buying vs. building AI: The real question is who owns it?
Why sovereignty is where ROI starts

• Why has AI changed the economics of buying and building? As enterprise AI costs rise and CFOs ask whether they’re paying for access or ownership, how should firms rethink what they’re actually buying?
• What happens when open-weight models become good enough? As frontier and open-weight models converge on capability but diverge on cost, how does intelligent model routing turn that shift into day-to-day operations?
• How do you deliver sovereign AI without a multi-year build? What does it look like when data, models and decisions stay inside your perimeter, and how does sovereignty become measurable ROI across trading, research, compliance and portfolio management?

Usman Kahn, Co-Founder & CEO, APEX:E3

Panel: Re-imagining the trading experience and the future of the O/EMS

• Where are firms landing on OMS/EMS consolidation — and what is driving the decision?
• What measurable ROI is AI actually delivering in O/EMS workflows today?
• As firms converge OMS and EMS, what regulatory, risk and audit trail considerations get raised first, and what gets missed?
• When investing in a modern O/EMS, how are firms deciding between building in-house and buying a vendor platform?
• How is the day-to-day trading experience itself changing — what does the human-AI working relationship look like on the modern trading desk?
• Cross-asset execution — FX, fixed income, digital assets  is where this gets hardest. What’s the biggest challenge, and what should firms prioritise first?

Moderator: Steve Grob, Founder, Vision57
Yarden Jacobson, Head of Order Management & Analytics, Man Group
Will Winzor Saile, MD Execution Analytics and Architecture, Rothschild & Co
Joe Everitt, Head of Electronic Sales & Trading, Winterflood Securities 

Panel: AI Data readiness for round-the-clock markets — Surveillance and infrastructure at scale

As trading extends toward 24/5 and beyond, firms face a data and AI problem that never sleeps — more hours to monitor, more venues to reconcile, and less human oversight at the margins. This panel looks at what it actually takes to keep data trusted, surveillance effective, and infrastructure resilient when the market doesn’t stop.

• As trading expands to 24/5 and automated bots drive massive data growth, how do firms handle continuous data ingestion without natural breaks to clean and validate it?
• Fast data access is critical for real-time surveillance and fraud detection. How must time-series platforms and AI monitoring tools adapt to catch risks across continuous, non-stop data streams?
• What does it take to keep cloud infrastructure resilient 24/5, and where are providers investing to guarantee zero maintenance downtime?
• Should trading firms buy, build, or take a hybrid approach for 24/5 systems—and why are open data formats becoming essential to avoid AI model lock-in?
• With less human oversight during overnight hours, how do firms keep AI-driven decisions explainable and defensible to regulators after the fact?
• Scaling 24/5 AI infrastructure from pilot to production is expensive. Where are the biggest cost bottlenecks, and how do you actually achieve ROI

Moderator: Andrew Delaney, President & Chief Content Officer, A-Team Group
Jeremy Posner, Data Strategy & Data Management Specialist assigned to buy side firms
Alex Mirarchi, Principal Capital Markets Specialist, Amazon Web Services (AWS)
Nabeel Ebrahim, Chief Revenue Officer, Behavox
Vlad Ilyushchenko, CTO, QuestDB

Innovation ‘Build’ Keynote: AI at scale – a hedge fund perspective

• How is Man Group building its own proprietary AI platform to securely feed internal data into models and drive innovation beyond industry hype?
• What are the practical front-office AI use cases being deployed across critical trading areas?
• How are these AI implementations delivering real business value and what specific metrics and obstacles must be navigated to measure and achieve true ROI?

Oskar Wantola, MD, Head of Execution Technology, Man Group 

Panel: Lessons learned building software at scale with AI

• How should technology leaders evaluate whether to ‘buy’ a specialised AI vendor solution for a particular domain (e.g. commodities, compliance) vs.’building’ internally?
• Beyond the industry hype, how are engineering teams establishing metrics and measuring the productivity gains delivered by coding tools?
• What are the best practices for optimizing spend and allocating resources when scaling agentic ecosystems across the firm?
• How should firms manage shadow AI and operational risks of citizen development as non-technical staff build and deploy their own AI tools?
• How must engineering leaders upgrade their security testing pipelines to detect complex vulnerabilities generated by autonomous AI agents?
• How can firms pivot AI governance from an innovation bottleneck into a competitive advantage that accelerates front-office adoption and unlocks business value?

Moderator: Steve Hamilton, Strategic Advisor and Independent Non Executive Director
Richard Bell, Head of Execution Technology, Nickel Digital Asset Management
Mari Parry, Principal AI Engineer, Blackrock
Matt Day, Lead Developer Architect, Google Cloud UKI
Usman Khan, Co-Founder & CEO, APEX:E3

AI Showcase:
Featuring developer productivity, code quality, and workspace automation vendors who provide the intelligent tools, metrics that allow capital markets engineering teams to build high-performance software faster and safer.

Pitch 1: Will Brown, Manager, Sales Engineering, Genesis Global
Pitch 2: Eren Mendi, Co Founder, Expanse
Pitch 3: Philip William, Chief Product Officer, AiMi Technologies 

Fireside Chat: The question isn’t buy or build. It’s can you prove it?

• When a regulator asks how your surveillance was validated, who built it and who can change it, what evidence do you produce, and where did it come from?
• AI tools have made software faster to build. But you are designing a surveillance programme, not just building a system. Has AI changed the part that was ever the hard part?
• A surveillance build is never finished. Who funds the maintenance, the regulatory changes and the fixes in year three, when the original budget and the original team have moved on?
• Regulators benchmark firms against each other. How do you know whether your controls are keeping pace with your peers, and who can actually tell you?

Interview with: James Peters, Former Managing Director, Goldman Sachs and MUFG; Compliance & Technology Advisor, Behavox
and Nabeel Ebrahim, Chief Revenue Officer, Behavox

Panel: Low latency: buying hardware and building performance at the edge

From the moment a tick leaves the exchange to the moment it becomes a trading decision, every layer of the stack — network, feed processing, messaging, storage — has its own latency budget. This panel follows that path and asks where firms should buy, where they should build, and where the lines between layers are blurring.

• As data moves from exchange to network to application, where in that path does latency actually get won or lost today — and how much does time-to-market dictate whether firms buy a vendor platform or build in-house at each layer?
• Where does the line sit between a low-latency message log and a queryable time-series store — and when do firms need both versus just one?
• How do architectural approaches differ when you’re solving for absolute latency versus latency jitter, and does that change depending on which layer of the stack you’re optimising?
• How can technology leaders implement robust security and governance controls at the edge without compromising execution speed and deterministic market performance?
• How are trading firms redesigning infrastructure lifecycles and colocation strategies as AI workloads compete with trading infrastructure for power, cabinet space, and chip inventory?
• How are firms tackling the acute skills shortage at the intersection of low-latency systems engineering and high-performance machine learning?

Moderator: Mike O’Hara, Editor, A-Team Group
Sapna Swaly, Independent Consultant, Arb Trading Group; FIX trading Community
Tom Dellmann, Director of Engineering, Chronicle Software
Diana Stanescu, Director, Financial & Capital Markets, Keysight Technologies
Laurent de Barry, Chief Product Officer, Exegy

Champagne Roundtables

Join a roundtable discussion for a deep-dive, interactive discussion with your peers on some of the day’s most important themes. Address common problems, benchmark your progress and come away with practical solutions and takeaways!

Roundtable 1:Legacy tech modernisation
Hosted by: Mike O’Hara, Editor, A-Team Group

• When migrating from legacy monoliths what approaches work in practice?
• How are firms keeping integration costs and architectural complexity from spiralling as they scale modern components?
• Will GenAI and agentic coding tools make legacy refactoring trivial, or will they accelerate the creation of unmaintainable technical debt?
  

Roundtable 2: AI Risks & Governance
Hosted by: Irina Sonich Bright, Managing Director, Head of Market Access Product, Agency Trading, UBS

• What does practical AI governance look like on the trading desk?
• How are firms managing and governing shadow AI and citizen developers?
• When an agent or model-driven workflow makes a mistake, who owns the accountability—and what does ‘explainability’ actually mean when defending a decision to regulators after the fact?

Roundtable 3: Market data disruption
Hosted by: Martina Satherlund, Global Head of Market Data Strategy, LGIM

• From cost control to strategic value – where is the next wave of innovation coming from?
• As data volumes, vendors and AI use cases continue to grow, what has been the biggest challenge in balancing innovation, governance and commercial control?
• Looking ahead, what will have changed most dramatically in the way investment organisations consume, manage and derive value from market data?
  
  Roundtable 4: Evolution of low latency infrastructure
  Hosted by: Sapna Swaly, Independent Consultant, Arb Trading Group; FIX trading Community

• What approaches can firms take for solving for Determinism vs. Absolute Speed?
• As trading desks push machine learning models directly to the bare-metal edge, how are firms navigating severe colocation power limits, rack density constraints, and global chip lead times?
• How are firms managing skills and talent shortages?