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Laurent Louvrier — Senior VP AI & Product Innovation · Confluence Technologies · London, England, United Kingdom.
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About
I run AI for a business that sells to very large financial institutions (9 of the top 10 asset managers, 10 of the top 10 asset servicers, 1,000+ clients across 40 countries). My job is to decide where AI actually pays, build the team that ships it, and own the number at the end.
That combination is rarer than it should be. Most AI leaders have never carried revenue. Most people who have carried revenue have never shipped a model into production.
CURRENT WORK SVP of AI & Product Innovation at PE-backed Confluence. I set architecture direction for every Data and AI initiative firm-wide, run a large team of product people and engineers, and own the P&L for the Portfolio Risk & Performance Analytics division ($40m+). GenAI and agentic capabilities are live in front of institutional clients. I decide what gets built, who builds it, and when it is good enough to put in front of a sophisticated and regulated client.
PORTFOLIO ADVISORY WORKSenior Advisor to ACCURAFY4 since 2023, across several portfolio companies in Agentic AI, Data and Cyber. The work is separating the two or three AI initiatives that move gross margin, win rate or cost-to-serve from the twenty that make a good board slide, then helping the CEO actually execute on them.
WHAT I'VE BUILT AND SOLD Founded SuccessData in 2018 and sold it in 2023: machine learning for unstructured financial documents, years before GPT 3.5 and before document intelligence became a category. Raised VC capital, several large enterprise clients, team of 15, acquired by Cyclone Robotics. Building an AI company from scratch teaches what a strategy deck cannot — how long production AI really takes, what it really costs, and how to tell a real product from a demo.
WHAT I'VE RUN Eight years at MSCI, latterly running the EMEA Analytics business: $150m+ ARR, full P&L, pricing, roadmap, partnerships and M&A. Before that, structured credit and quantitative research.
Engineer by training — MSc in Financial Mathematics & Computer Science (ENSIMAG). London-based.
Experience
SVP AI, Analytics and Product InnovationCurrent
Confluence · London Area, United Kingdom
Jul 2025 – Present
Own the Data & AI agenda for a PE-backed platform serving 900+ institutional clients in 40 countries, and the P&L for the Portfolio Risk & Performance Analytics division. TEAM AND MANDATE • Lead an organisation of 140+ people (engineers, product, data) delivering AI across 5 product lines. • Set architecture direction for all Data and AI work firm-wide: model selection, agent patterns, evaluation, guardrails, and every build-versus-buy call. Pricing strategy (usage-based pricing). • Run the AI investment strategy: every initiative carries a named value case, an owner and a kill date. IN PRODUCTION • Automated document validation (checklist automation for asset servicers). Live with multiple clients since 2025. • MCP access for portfolio analytics. • Confluence POINT agentic layer. • Built the evaluation and observability layer that makes probabilistic output safe to put in front of regulated institutions (eval sets, human review thresholds, lineage). • Unified data foundation (single system of record, data distribution) across all products. COMMERCIAL • Division P&L: $40m+ revenue • AI-attributable outcome: $m of new ARR influenced, significant cost-to-serve reduction.
Senior AdvisorCurrent
ACCURAFY4 · Greater London
Mar 2023 – Present
Operating-partner work for a venture and growth investor across the portfolio (seed to series A), alongside my executive role. • Coverage: 4 portfolio companies in AI and Data - private assets, cyber. • Value-creation planning: run AI opportunity assessments with management teams, what it is worth, what it costs, and the order to do it in. • Execution support: help CEOs and CTOs make the right AI hires, choose an architecture they can staff, and avoid the two typical failure modes - buying a platform before defining a use case, and piloting indefinitely with no production owner. • Cross-portfolio leverage: maintain a shared view of what has worked and what has not. • AI diligence: pre-investment assessment of data assets, technical debt and how much of management's AI story is real. • Outcome: successful rounds raised, $m of identified annualised value, multiple AI initiatives shipped and live in production.
Education
Skills
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