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Ex-Google Cloud AI head building investigative agents for FIs. Andrew Moore bringing data prowess, AI practicality to fintech Lovelace. Reading Time: 3 mins read Andrew Moore, former head of Google Cloud AI, says building investigative AI agents for risk management workflows is like "solving a huge game of Sudoku, with literally millions of little facts, trying to find a pattern in it all." Moore's fintech startup, Lovelace, which exited stealth mode in late April, is building investigative AI agents that analyze vast data sets to help institutions fight financial crime, monitor and predict market changes, identify risk and bolster compliance operations, Moore told FinAi News. Moore's 14 years at Google helped shape his strategy for Lovelace through learning to maximize extensive data and apply advanced AI to tangible use cases, Moore said. "What I learned during that period was all the ins and outs of what it takes to do this translation of very theoretical, advanced research coming out of these brilliant, incredibly expensive research and engineering labs, and determining how to actually help systems run more efficiently," he said. For example, Moore's experience overseeing fraud prevention at Google translates to anti-money laundering initiatives in banking, he said. "Anti-money laundering investigations inevitably involve lots and lots of data points," he said. "You can't just look at one transaction... It's how that transaction fits in a massive pattern and things that change over time, whether that's geography, different financial instruments, different industries and so forth." Lovelace has raised an undisclosed amount in a seed funding round led by RRE Ventures, with other investors including Magarac Venture Partners, United States Innovative Technology and Carnegie Mellon University, according to a Lovelace spokesperson. Moore cannot yet name its banking partners due to non-disclosure agreements, but said early adopters comprise large and medium-sized institutions in the United States and United Kingdom. Context engine. While data quality is crucial for effective AI agents, successful deployment starts with extensive model training to establish a "knowledge graph" or "context engine," especially when dealing with millions of data points, Moore said. "When the agent starts out with its question, before it ever has to touch all that data in the background, you can formulate a really good plan for what data the agent is going to work with," Moore said. These knowledge graphs also help ensure explainability and auditability, which are a priority for FIs when vetting potential fintech partners, he said. "You have to have multiple independent lines of reasoning," he said. "When [agents] finish their work, they've always got to show this unbroken chain of bits of information they used as evidence... You have to actually show a high-quality statistical analysis, as well." Leaving tech giants. Moore joins others who have left tech giants to start fintechs, including: * Carmelle Cadet, left IBM after 10 years to start central banking infrastructure provider Emtech; * Twitter co-founder Jack Dorsey, left to start payments processing and merchant services platform Block, formerly called Square; and * David Marcus, left Meta to start payments company Lightspark.

Lovelace has released benchmark results showing its lightweight AI model paired with its YottaGraph context engine matched Google's Gemini Deep Research performance at less than 1% of the cost. The benchmark evaluated 12 complex financial and business research tasks, assessing factual accuracy, analytical rigour, evidence use and citation quality. YottaGraph, Lovelace's flagship context engine, connects over 60 million entities and billions of facts across multiple data sources in real time. Whilst Gemini Deep Research accessed the public internet, Lovelace's agent relied solely on YottaGraph without internet search capability. Founded in 2023 by Andrew Moore, former head of Google Cloud AI, Lovelace has made its methodology, evaluation framework and sample reports publicly available on GitHub. The company argues that context, rather than compute power, will define the next generation of enterprise AI.

Lovelace emerges from stealth with context engine builder for mission-critical AI. Published: May 4th, 2026 PITTSBURGH, Pa. - Lovelace has emerged from stealth to introduce Elemental, an enterprise context engine builder designed to meet the demands of speed, scale, and accuracy in high-stakes environments - dramatically increasing the investigative power of AI agents by 1000x on complex queries, making the company the only provider of enterprise-scale context engines for mission-critical applications. By unifying data ingestion, entity resolution, and graph construction into a single pipeline - and enriching it with real-time intelligence from its proprietary YottaGraph - Elemental builds context engines that give AI agents the contextual awareness needed to form fast, high-confidence conclusions in rapidly changing conditions. Led by Andrew Moore, former head of Google Cloud AI, dean of Carnegie Mellon University's School of Computer Science, and the first AI advisor for US CENTCOM, Lovelace brings together an expert team behind technologies used by billions, from Google's core systems to global AI platforms. "Throughout my career, I've been driven by a simple question: how can we use advanced intelligence to help people make the right decision when the cost of being wrong is catastrophic?" said Moore. "AI has extraordinary potential in investigative contexts - but only if it unambiguously helps humans make better decisions. With Elemental, we're giving teams the speed of AI with the confidence of verifiable evidence, so every conclusion can be traced, tested, and trusted in the moments that matter most." As enterprises deploy AI agents into increasingly complex and dynamic environments, the lack of reliable context has become a critical barrier to adoption. To solve this, the Elemental platform creates secure, enterprise-specific context engines that transform fragmented enterprise data into structured knowledge graphs that AI agents can navigate and query within milliseconds with verifiable citations, delivering deep-research-quality analysis with the immediacy of a simple query. Through access to Lovelace's YottaGraph - scaling to trillions of interconnected facts - enterprises can enrich internal data with real-time global intelligence, enabling faster, more accurate decision-making about the state of the world at any given moment. Article tags.