Sherlocq Launches AI Regulatory Intelligence Platform for Financial Compliance

The regulatory technology landscape received a new entrant on May 13, 2026, when Sherlocq officially launched its AI-native regulatory intelligence platform to the public. The company positions this as the emergence of a new enterprise AI category specifically designed for financial services compliance.

According to the FinTech Global announcement, the platform targets compliance officers, lawyers, risk professionals, and regulators across more than 30 jurisdictions including the US, UK, UAE, Singapore, and Hong Kong.

Three core capabilities define the initial release. The regulatory research module handles multi-jurisdiction research, cross-border comparison, and obligation mapping. Document intelligence provides structured review, gap assessment, and policy analysis against regulatory standards. Sanctions intelligence offers real-time, multi-regime research across OFAC, OFSI, the EU, and the UAE with access to more than 320 data sources in a single query.

The platform launches with availability on web, iOS, and Android for both individual professionals and enterprise organizations. AI connectors are live for Claude and ChatGPT, with Microsoft Copilot and Google Gemini integrations scheduled for future release.

Founder and CEO Bhavin Shah built the platform around a non-negotiable starting point: compliance professionals cannot rely on outputs they cannot verify. Every design decision follows from that principle.

Shah explained that the platform uses retrieval-augmented generation, a technical architecture that grounds every response in primary regulatory sources before a single word is generated. That means laws, guidance notes, enforcement actions, supervisory letters, and consultation papers from official sources across 30+ jurisdictions. No unattributed AI output. Every answer carries full citations so the professional can interrogate the source directly.

The second principle was domain specificity. Generic AI tools were not designed for the compliance domain, where hallucinated regulatory interpretation is not an inconvenience but a liability risk. Sherlocq's retrieval layer, knowledge graph, and prompt architecture are built exclusively for financial regulation. That specificity is what separates intelligence from search.

ISO 27001 and ISO 27701 certification, configurable data residency, privacy-aware architecture, and audit-trail outputs were prerequisites, not post-launch additions. If a platform cannot pass a regulated institution's security and privacy review, it will never be deployed at the scale where it can actually change how compliance teams work.

Underpinning the system is a proprietary knowledge graph layered above the platform's vector search capabilities, enabling Sherlocq to map relationships between regulatory obligations, enforcement actions, and legal provisions across jurisdictions. The result is a platform capable of handling complex, multi-step, cross-border queries rather than returning a list of links.

When a query is submitted, Sherlocq combines dense vector search, keyword matching, and metadata filtering before re-ranking results for relevance and diversity. The model is then instructed to cite sources, avoid speculation, and identify areas where guidance may remain open to interpretation.

To illustrate the practical application, Shah described a scenario in which a compliance officer at an international wealth manager asks Sherlocq to compare retail investor suitability and disclosure obligations across the US, UK, and Singapore. The platform simultaneously retrieves and synthesises requirements from SEC Regulation Best Interest, the FCA's Consumer Duty, and MAS Notice FAA-N16, presenting the obligations side-by-side with full source attribution.

Work that would traditionally require days of manual research across three regulatory websites can instead be delivered in minutes through a structured, actionable output (the kind of time savings that compliance teams have been desperately waiting for).

Financial institutions, law firms, regulators, and consultants collectively spend more than $300bn annually on regulatory compliance, with over ten million professionals managing that complexity on a daily basis. Sherlocq positions itself as a solution to tools that, until now, have provided monitoring without interpretation, alerts without answers, and search without synthesis.

The company differentiates itself from generic AI assistants, conventional RegTech monitoring tools, and document management platforms. It was built to meet the security, privacy, and domain standards required by regulated institutions — standards it says no general-purpose AI platform has been designed to deliver.

Shah's background informs the platform's design. He spent his career sitting across the table from regulators, leading investigations at the highest levels, and advising institutions in the most consequential moments of their existence. In every one of those engagements, the same problem recurred: brilliant professionals, overwhelmed by regulatory complexity.

The official Sherlocq website confirms the product capabilities and pricing structure, which starts with a free tier offering 100 lifetime AI regulatory queries and 10 lifetime document uploads.

Enterprise adoption will depend on whether the platform can actually reduce the friction compliance teams face when navigating regulatory fragmentation. The technology promises to transform how professionals interact with regulatory content, but the real test comes when institutions begin deploying it at scale.

Whether compliance officers actually trust the outputs enough to present them to boards and regulators remains the critical question. The platform's architecture addresses verification concerns, but institutional adoption cycles in financial services move slower than most AI vendors anticipate.

For now, the regulatory intelligence market has a new option that claims to solve the interpretation gap. Whether users actually pay for it at enterprise scale is what will determine if this becomes a category-defining platform or another well-intentioned tool gathering dust in compliance departments.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn