AI-Powered Due Diligence: Why automated due diligence is only as defensible as the data beneath it (The Demo Room #24)

Welcome to The Demo Room – your front-row seat to the future of RegTech, RiskTech, and AI innovation. 

In this series, we document our research interviews with the most forward-thinking vendors tackling the industry's biggest challenges. Each blog is built around a comprehensive product demo, providing clear insights into how these innovations address industry challenges.

On this occasion, we feature RZOLUT, an AI-powered due diligence and proprietary data platform that helps compliance, risk and financial crime teams onboard faster, reduce false positives, and make confident regulatory decisions.

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Enhanced due diligence has always been a trade-off between speed and rigour. Anti-money laundering obligations require organisations to untangle complex ownership structures, verify sources of wealth, investigate adverse media and litigation, and assess vast quantities of information, all while avoiding unnecessary friction for legitimate customers.

Much of this work remains manual. Analysts sift through more than 150 jurisdiction-specific sources—from corporate registries and litigation databases to adverse media and commercial data providers—before assembling a report.

Data availability also varies significantly by jurisdiction, with some markets offering rich, digitised records and others providing only limited public information.

But access to data is only part of the problem. The real work happens beneath the feed: verifying identities, linking related entities, resolving ownership structures, and uncovering relationships that raw screening data alone cannot reveal. 

For firms doing this at scale, the operating problems compound. Institutional knowledge sits in individuals rather than systems, quality varies between offices, and capacity is capped by headcount. Moving from ten reports a month to one hundred becomes a hiring exercise.

A solution: AI-powered due diligence built on trusted data

RZOLUT approaches the problem from the bottom up.

The foundation is ContentStream, its proprietary diligence data platform: more than 2.2 million PEP profiles, 3.5 million watchlist profiles, 600,000 enforcement records and 150,000 sanctions profiles, resolved into unified entity profiles with continuously refreshed sanctions data, alongside a repository of more than 10 billion news articles in 120 languages. 

Built on that foundation is Unity, RZOLUT's AI due diligence platform. 

Unity orchestrates specialised agents that retrieve evidence, map relationships, parse litigation data, generate report sections and perform quality assurance. Each operates within defined guardrails, with the validated output coming together in one coherent draft, ready for delivery or expert reviews.

The surrounding architecture is designed to improve confidence in the final report. Identity is established through anchor-based entity resolution, combining attributes such as name, jurisdiction and registration number before strengthening confidence as new identifiers are discovered. Data sources are selected according to geography and mandate, while a source reputation engine ranks evidence and treats weaker sources as investigative leads rather than final conclusions. 

A dedicated quality control agent then reviews the completed report against its own supporting evidence, checking for contradictions before delivery.

The outcome

The headline claim is speed with context. The most extensive due diligence reports have historically consumed up to 120 hours of analyst effort. RZOLUT Unity brings this down to 15-20 minutes, often followed by an expert review which cuts delivery times by 70-80%. Against legacy providers, ContentStream reports 24% better coverage, 44% fewer false positives and 50% faster delivery, supported in part by an independent assessment done by a leading consulting firm at the direction of a national regulator.

The operational effect is elastic capacity: ten reports a month to a hundred without proportional hiring, delivered as SaaS, as an API, or as a fully managed programme.

Parker & Lawrence’s View

RZOLUT has approached the problem the right way around. It starts with the data. The hard work lies in resolving entities, enriching profiles, preserving provenance and creating the context that allows an investigation to begin on solid foundations. Unity's agentic workflows then execute the investigation itself, breaking complex due diligence into a series of specialised, sequential tasks rather than relying on a single prompt.

Equally important is the emphasis on trust. Anchor-based identity resolution, source ranking, automated quality control and complete audit trails are core design principles. Together, ensure every conclusion can be traced back to supporting evidence and reviewed before it reaches an analyst for an expert review.

AI for regulated investigations depends as much on disciplined data engineering and workflow design as it does on the model generating the report. RZOLUT's architecture reflects that philosophy throughout.