Sonto provides the financial context for your agents.
We are building Sonto to give financial AI agents the context they require in the form they work best with.
We start from source documents and pre-assemble data, estimates, and analysis, so you and your agent can focus on the specific task you care about.
Starting from Source
Financial information designed for agents
The best way to extract, store, process, and structure financial information for an AI agent that can read tens of thousands of words a second is fundamentally different to what we designed for humans and traditional quant algorithms.
Starting from source
Starting from raw source documents gives AI agents the richest data from the full breadth of companies, news, events, reports, and filings.
Every Data Point
We collect every data point from source documents, whether it's from a CEO's comments to the press, part of an analyst question, or deep in the notes.
Context included
Alongside the number, we collect the footnotes, adjustments, and surrounding commentary, which the agent needs to properly understand the data point.
Pre-computed analysis
We don't stop at data: pre-computing detailed standard analysis across company financials and news flow means your agent is faster, smarter, and better informed.
AI Native Research infrastructure for public markets
Covering the long tail of companies and questions
The current cost of expert human financial research means coverage is limited in both its breadth and depth. This leaves a long tail of companies and questions that aren't served by existing analysts. By building an automated production system, Sonto structurally lowers the cost of data extraction and analysis. This makes providing your agent with detailed coverage on the long tail of companies viable for the first time.
Coverage frontier
A lower cost base changes what gets covered.
The opportunity is not more broker reports for existing well-covered names. It is high-quality data, analysis, and insights for a much larger set of companies and questions that sit beyond the economics of human analysts.
Coverage
Long-tail coverage becomes economic
The constraint today isn't demand for high-quality analysis. It is cost. Lower the cost base and a much broader research surface opens up.
Sources
Starting from source documents
One of the largest costs of long-tail coverage is collecting high-quality data from company documents. AI makes professional-quality extraction and structuring viable for the first time.
New questions
Unlocking analysis across silos
Existing analysis is often siloed based on sector, geography, and industry. This means cross-cutting questions that would require normalising data across these boundaries aren't answered. Scalable AI agents make these tractable.
Agent Collaboration is an Unsolved Problem
The financial research factory - getting AIs to autonomously scale analysis
Agent collaboration is the real systems problem
Coordinating many workers across evidence, assumptions, and intermediate outputs is an unsolved problem. Long-term iterative analysis needs process discipline.
Insights come from building on prior work
Serious research cannot start from zero every time. Intermediate judgments, extracted facts, and structured estimates need to remain available for the next layer of analysis.
Cross-company work requires consistent outputs
Comparison breaks if every agent invents its own assumptions and structure. Reusable analysis needs normalised outputs that can travel across companies and questions.
Updates require indexed, traceable analysis
To refresh analysis when a filing lands or assumptions change, the system needs lineage across every step: what was used, what was produced, and what depends on it.
ASSEMBLY LINE STATION: ESTIMATE_METRICS
PROGRESS: 00 / 24
Status: INITIALIZING
Blog
Knowledge factories
We believe specialised knowledge factories are the next stage in the evolution of AI within knowledge work. Read more on our blog.