Sonto is building the knowledge factory for equity research.
We don't think the future is AI agents generating more isolated Excel models and ad hoc datasets.
We believe it will be a controlled production line of agents assembling data, evidence, estimates, and analysis at scale. Sonto is designing and building this factory.
Research infrastructure for public markets
Covering the long tail of companies and questions
The current cost of expert human equity research means current 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 analysis and makes the long tail of coverage economically viable.
Coverage frontier
A lower cost base changes what gets covered.
The opportunity is not one more broker report for existing well-covered names. It is high-quality coverage for a much larger set of companies and questions that sit beyond the economics of traditional 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
The equity research factory - enabling AIs to collaborate and re-use 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.