The buzziest corner of finance AI is not the trading desk. It is the dull middle office, where staff keep thousands of portfolios in line with spreadsheets. A London-founded startup just raised $27 million to automate exactly that.
The company is MDOTM. It has closed a $27 million growth-equity round led by Expedition Growth Capital, bringing total funding to $36.5 million. Founded in London in 2015, with offices in New York and Milan, MDOTM sells an AI platform called Sphere to banks and asset managers.
Sphere now supports more than $100 billion in assets across over 60 financial institutions, the company said. Its named clients include Morgan Stanley, Amundi, and Zurich Bank. The new money will fund hiring and a push deeper into the US and European markets.
The spreadsheet problem
Sphere is built for the grind between the big decisions. It reads market and macroeconomic data to flag shifts in conditions. Investment teams add their own views, and the software turns all of it into portfolios it can build and rebalance at scale. A generative-AI layer then writes the client reports and commentary on its own.
MDOTM splits the platform into three parts. One spots shifts in market conditions and generates forward-looking signals. A second, Portfolio Studio, builds and rebalances portfolios from those signals and a manager’s own views. A third, StoryFolio, drafts the client reports. Behind it sits the MDOTM Lab, an academic network of more than 20 professors and PhDs working on machine learning, portfolio theory, and AI ethics.
The pitch rests on a squeeze every wealth manager knows. Fees are falling, and clients want portfolios tailored to them. Hiring an army of analysts does not scale. The work in the middle still happens in spreadsheets: rebalancing, keeping portfolios aligned with house views and generating client commentary. This layer is where AI can finally do useful work.
The problem is widespread. Wealth managers often manage hundreds or thousands of portfolios, each with different risk profiles, tax considerations, and client goals. Manual rebalancing can take days or weeks, and human error leads to compliance headaches. Spreadsheets are brittle; they break when data changes or when a new regulation hits. MDOTM aims to replace that manual drudgery with a system that can ingest market data, apply investment rules, and rebalance portfolios in minutes.
MDOTM’s technology uses machine learning to detect regime changes in financial markets—such as shifts from bull to bear trends, changes in volatility, or macroeconomic turning points. These signals are then fed into the portfolio construction engine, which builds portfolios that align with both the market outlook and the manager’s strategic views. The generative AI layer, StoryFolio, then produces client-friendly commentary explaining why changes were made, saving analysts hours of writing.
The company has built its platform with an emphasis on explainability. Asset managers and banks are under strict regulatory scrutiny, and they cannot use black-box models. MDOTM ensures that every portfolio decision can be traced back to a clear signal or rule, and the AI can generate plain-English explanations for regulators and clients.
A crowded race for the back office
MDOTM is not alone in spotting the gap. In the US, former Citadel quants raised $78 million to build an AI system for wealth managers. Banks are shipping their own tools too, such as Starling’s agentic assistant. The same logic of automating the regulated middle office is spreading well beyond finance, from legal back-offices to hospitals. It lands as Europe’s fintech sector braces for consolidation, which tends to reward firms with real enterprise contracts.
The competition is fierce but fragmented. Many startups focus on specific parts of the workflow—portfolio optimization, reporting, or data aggregation. MDOTM’s bet is that an end-to-end platform that covers signal generation, portfolio construction, and client communication will be more attractive to large institutions that want a single vendor. The company’s existing client list suggests the strategy is gaining traction.
But the race is not just about technology; it is also about trust and distribution. Wealth managers are notoriously slow to adopt new software, especially when it touches core processes like portfolio management. MDOTM’s decade-long journey to $100 billion in AUM shows the patience required. The new funding gives it runway to accelerate sales in the US, where the wealth management market is the largest in the world.
Selling AI to sceptical institutions
Selling AI into investing brings a catch. Money managers answer to regulators, and they distrust tools they cannot explain. MDOTM itself operates in the UK as an appointed representative under the Financial Conduct Authority regime. Its answer is to keep humans in charge and to make the AI show its work.
Asset and wealth managers are no longer asking whether to use AI in investment decisions, but how to deploy it at scale across thousands of portfolios while maintaining control, said chief executive Tommaso Migliore. The round also brings grey hairs onto the board. Steve Twomey, the Expedition partner who led the round, takes a seat, as does James Hays, a former chief executive of Wells Fargo Advisors with nearly 40 years in the industry. These additions signal that MDOTM is ready for the institutional growth phase.
The challenge of regulation is real. In Europe, MiFID II and the AI Act impose obligations on firms using AI for investment decisions. In the US, the SEC is scrutinizing algorithmic trading and portfolio management. MDOTM’s approach—keeping a human in the loop and maintaining an audit trail—is designed to meet these standards. The company also emphasizes that its AI does not make autonomous trades; it provides recommendations that portfolio managers can accept, reject, or modify.
The market is receptive. A 2024 survey by Deloitte found that 70% of asset managers are actively exploring or deploying AI in portfolio management. The main drivers are cost reduction, personalization, and faster decision-making. MDOTM claims that its platform reduces the time spent on manual rebalancing by up to 80% and cuts reporting time by 90%.
MDOTM’s lab network is a differentiator. By partnering with academics, the company stays at the frontier of machine learning while ensuring its models are based on rigorous research. The lab has published papers on topics like reinforcement learning for portfolio optimization and ethical AI for finance. This academic credibility helps when pitching to risk-averse institutions.
The funding round is also notable for its timing. Venture capital into European fintech has slowed since 2021, but deals for B2B software companies with strong revenue growth are still happening. Expedition Growth Capital specializes in backing software firms that have grown efficiently with little outside capital. MDOTM fits that profile: it took a decade and $36.5 million to reach this point, suggesting disciplined spending and product-market fit.
Looking ahead, MDOTM plans to use the new funds to expand its engineering and sales teams, particularly in the US. The company will also invest in product development, adding more asset classes and geographies to Sphere. The ultimate goal is to become the standard operating system for wealth management middle offices.
The bet now is that the boring middle of finance is where the next wave of AI money will land. If MDOTM is right, the days of spreadsheet-driven portfolio management could be numbered, and the vast majority of wealth management workflows will soon be powered by AI.