About datawiser
Building the next generation of public data products - transparent, affordable, and designed for builders.
Our Story
datawiser was born from a simple frustration: high-quality public-market data should be transparent, verifiable, and testable - not something you only understand after signing a long-term contract.
Current data vendors are running opaque data pipelines with vendor-defined interpretations. We're building something different: transparent datasets that show exactly where every value comes from, how it was derived, and how it fits into a company's capital structure.
Existing vendors do a lot of things well, but they leave gaps: opaque methodologies, limited event detail, and pricing that shuts out smaller teams, independent researchers, and new products. At the same time, public filings are richer than ever - if you have the time and infrastructure to parse them.
datawiser aims to bridge that gap. We use AI-assisted pipelines to transform public filings into clean, audit-ready datasets: free float events, shares outstanding history, and a security master that stays consistent across them. The goal is simple: make high-quality capital structure data accessible to anyone who can make good use of it.
What We Believe
Quality over coverage
We’d rather cover fewer instruments with high accuracy than many with guesses. Every record should have a clear lineage back to filings and events.
Events, not just endpoints
A single number is rarely enough. We focus on event chains - what changed, when, and why - so you can understand the story behind the time series.
Built for engineers and quants
Clean JSON, predictable schemas, and stable identifiers. The API is designed to drop into your existing pipelines with minimal friction.
Transparent, sustainable pricing
Pricing is designed to be transparent and sustainable, with a clear path for smaller teams and independent funds to access institutional-grade data as coverage expands.
Team
Founder
Founder & Builder
datawiser is currently a founder-led project, built by a quantitative data engineer who has spent years wrestling with reference data, corporate actions, and SEC filings from the other side of the terminal. The aim is to bring that hard-earned experience into a product that feels like it was designed for the people who actually use the data.
If you'd like to know more about the background behind datawiser or discuss specific use cases, get in touch via the contact page - this is very much a conversation with early users, not a finished product dropped from above.