Auquan

Auquan is a London/New York-based agentic AI platform for institutional finance, marketed as a 'system of action' rather than a research assistant. Unlike tools that surface information for analysts to synthesize, Auquan's agents decompose complex financial workflows into hundreds of discrete subtasks, execute them autonomously, and deliver publish-ready outputs — IC memos, credit reviews, ESG reports, risk monitoring alerts — in minutes rather than days. Platform launched in late 2023; as of early 2025 it counts one-quarter of the top 25 private equity firms among its customers.

Super-agent architecture: a master orchestration layer that routes work across specialized sub-agents for data gathering, extraction, analysis, and report formatting. Core workflow modules include: (1) Deal Screening & IC Memo Generation — ingests CIMs, VDRs, financials, and prior memos to produce committee-ready documents; (2) Due Diligence — answers ad-hoc queries using financials, legal docs, expert calls, broker research; (3) Portfolio Risk Monitoring — Risk Agent autonomously monitors hundreds of companies across millions of sources to surface early-warning signals across operational, regulatory, reputational, and governance risk; (4) Sustainability/ESG — screens and reports against SFDR, CSRD, and SASB frameworks; (5) LP Reporting — automates investor update document production. Outputs delivered in Word, Excel, and PDF. Users describe the workflow as: state what you want, provide an example format, and the system executes the entire process autonomously. Complete audit trail with source citations on every output.

RAG-based agentic AI (Retrieval-Augmented Generation) that decomposes tasks into hundreds of discrete subtasks. Covers 550,000+ companies across 2M+ data sources in 65+ languages. Underlying LLM infrastructure runs on Microsoft Azure OpenAI, Azure Machine Learning, Azure AI Search, and Azure Files. Proprietary Intelligence Engine™ applies finance-domain logic on top of the retrieval stack rather than generic prompting. No public MCP server listed or documented. No public REST API documented for external programmatic access — the platform is a self-contained SaaS interface, not an API-first tool. AI-native design; complete source traceability on every generated insight. Not suitable as a composable API layer in an AI-native workflow stack.

Custom enterprise pricing only — no public tiers or per-seat list prices. Licensing is structured around modules, use cases, or data volume. No free plan available. Available for direct purchase or procurement via the Microsoft Azure Marketplace, which may enable simplified enterprise invoicing. Professional services layered in for large clients requiring onboarding and internal system integration. Confirm all pricing directly with vendor.

Deep Microsoft integration: runs on Azure OpenAI, Azure ML, Azure AI Search, Azure Files; available on the Microsoft Azure Marketplace. Outputs natively in Word, Excel, and PDF — the dominant format for finance teams. Selected for the Microsoft UK GenAI Accelerator (with NVIDIA, GitHub, WeTransact). No documented native CRM, data provider, or third-party SaaS integrations published publicly. No REST API or MCP server available for external workflow composition.

Built on Microsoft Azure with Microsoft Enterprise Data Protection as the security foundation — covers encryption at rest and in transit, dedicated regional data residency options, and Azure's compliance infrastructure. No independently-held SOC 2 Type II, ISO 27001, or GDPR certifications publicly disclosed on Auquan's own trust/security page (as of research date). Security posture relies heavily on Azure's inherited certifications. Vendor states customer data is not used to train external models. Financial institutions including a €10B+ AUM PE firm and a $180B+ AUM global PE firm have cleared Auquan through enterprise security reviews. Confirm specific certifications, DPA availability, and penetration testing history directly with vendor.

Founded 2018 in London by Chandini Jain (CEO) and Shubham Jain (CTO). Chandini previously worked as a derivatives trader at Optiver and interest rate structurer at Deutsche Bank; Shub came from engineering roles at Microsoft and Gusto. Techstars London 2018 alumnus. Total funding approximately $8M seed (per company's own announcement), with lead investor Peak XV Partners and participation from Episode 1 Ventures, Neotribe Ventures, and Techstars; note: PitchBook reports $19.8M and Tracxn reports $11.5M — figures vary across sources, $8M is the company-confirmed seed total as of early 2025. Selected for Microsoft UK GenAI Accelerator backed by NVIDIA and GitHub. Headquartered in London with offices in New York and Bangalore; ~50 employees. Named to CB Insights AI 100 (2025), AIFinTech100 (2025), and shortlisted for the Drawdown Awards 2025 (Due Diligence Technology). Customers include UBS, T. Rowe Price, Capital Group, MetLife, Blue Owl, BC Partners, and Federated Hermes — one-quarter of the top 25 PE firms.

Strong. Auquan is purpose-built for exactly the workflows a firm runs: deal screening, IC memo preparation, portfolio company monitoring, ESG/diligence on targets, and LP reporting. The agent-based model directly compresses analyst time on the most manual diligence steps, which is high-leverage for a growth equity firm evaluating complex technology companies. The depth of private-markets PE customer traction (Blue Owl, BC Partners, MetLife) is directly analogous to a firm's workflows. Coverage of 550,000+ companies including private companies is critical for growth equity sourcing. The main gap for a firm: no MCP server or open API means it cannot be composed into a broader AI-native internal toolchain — it operates as a standalone SaaS portal. Security posture relies on Azure inheritance rather than Auquan-held SOC 2/ISO 27001 certifications, which may require additional diligence for LP-sensitive data flows.

No MCP server and no documented external API — cannot be composed as a node in an AI-native workflow stack, which is a meaningful constraint for a firm's AI-native ambitions. Security certifications (SOC 2, ISO 27001) not independently held by Auquan; inherited from Azure. Pricing is fully custom and opaque — no self-serve or transparent tier structure. Early-stage company (~50 employees, ~$8M raised) serving very large institutions — execution risk relative to client complexity. Integration breadth outside the Microsoft ecosystem is undocumented. Output quality on highly specialized deep-tech sectors (quantum, defense, space) may require validation against expert judgment.