Redefining the Modern Equity Research Stack.
Most "AI for equity research" tools are the same thing under the hood: an LLM bolted onto a financial data feed. Ask a question, get an answer. Great demo, shaky foundation.
We think the real unlock isn't a smarter chatbot; it's a stack. Four layers, each reducing noise and increasing decision confidence:
Data Platform — the foundation of truth. Clean ingestion, knowledge graphs, and guardrails where every claim traces back to source.
Orchestration — decides what actually matters now (which filing change is material, which analysis should run).
Workflows — repeatable analytical processes that compound depth every time they run.
Agents — synthesise and communicate, but never invent truth. They stand on the certainty built below them.
The shift: alpha isn't about who has the most data anymore. It's about who has the best system to turn data into decisions.
One thing we keep coming back to from watching real analysts: they don't start their day with questions – they start with changes. That reframed how we build the whole thing.
Full breakdown here → https://knowyourcompany.ai/blog/the-modern-equity-research-stack
Curious how others here think about the retrieval-vs-reasoning line. Where does a chatbot stop being enough for serious research work?
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