The Agentic Data Platform
for Financial Services.
Not another dashboard. Not another alert system. A platform where autonomous agents extract, validate, and structure financial documents so your team operates on clean data from the moment it arrives.
Financial Data Is Broken at the Source
We built ZetaRun because we lived the problem. As quantitative finance professionals at institutions including Morgan Stanley, HSBC, and Credit Suisse, we spent years watching critical data arrive late, incomplete, or manually processed by analysts who should have been doing something else.
The documents existed. Filings, prospectuses, DDQs, fund reports, research. But extracting usable data from them meant armies of analysts doing work that should never have required a human. Every institution we worked at had the same bottleneck: unstructured documents that could not be operationalised without manual effort.
That bottleneck costs analyst hours, delays decisions, and introduces errors that compound across portfolios and risk models. It is not a tooling problem. It is a structural problem, and it has not been solved because building it properly requires both deep financial domain knowledge and serious AI infrastructure expertise.
The First Agentic Data Platform for Financial Services
ZetaRun is not a search tool or an alert system. It is a platform where autonomous AI agents run continuously on your behalf, ingesting, validating, and structuring financial data from every document your organisation touches.
Each agent is purpose-built for a financial document workflow: client onboarding packs, SEC filings, DDQs, fund reports, regulatory filings, loan documents. They do not wait for queries. They process documents as they arrive and deliver clean, structured data to your systems automatically.
This is a new category. The tools financial teams use today were designed for a world where humans extract and process data manually. We are building for a world where agents do that work, and your team makes better decisions because their data is clean, fast, and consistent.
We started with financial services because that is where the document volume is highest, the cost of manual processing is greatest, and the domain expertise required to do it properly is most scarce. The platform we are building applies wherever decisions depend on structured, reliable data. Finance is where we are starting.
Autonomous
Agents process documents continuously. No manual extraction. No queues. No analyst intervention required.
Structured
Raw documents become clean, validated, query-ready data. Not summaries, not links, not raw text.
Domain-native
Built for the document types, data schemas, and precision standards of financial services specifically.
Compounding
Schemas improve over time. Validation rules sharpen. Data quality compounds as the platform learns your workflows.
The Timing Is Not Coincidental
Three things converged to make this possible that were not true before: large language models that understand context across complex financial documents, inference costs that make running agents at scale economically viable, and the volume of unstructured financial documents reaching a critical mass.
The infrastructure to build a truly autonomous financial data platform did not exist two years ago. It does now. And the organisations that deploy it first will have a structural data advantage that is very difficult to replicate manually.
Built by People Who Understand the Problem
The ZetaRun team comes from quantitative finance, not from generic SaaS or media technology. We have worked at institutions including Morgan Stanley, HSBC, and Credit Suisse, building models, running data workflows, and managing the document processing bottlenecks that ZetaRun is designed to eliminate.
We know what financial professionals actually need: precision over recall, structure over raw data, speed over comprehensiveness, and trust over novelty. Every design decision we make is filtered through that lens.
We're in Early Access
If you are serious about eliminating manual data processing from your workflows, let's talk.