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Insights for financial research teams

The team writes about the architecture, the failure modes, and the workflows behind FinCatch Studio. We aim for plain words and concrete examples over clever one-liners.

AgentRecurring Monitoring: From AI Assistant to Proactive Research PartnerRecurring monitoring turns FinCatch from an on-demand assistant into a proactive research partner that keeps watching the market, judging relevance, and extending the user's research thread over time.Lok Kan Chan and Austin Cheung · June 4, 2026
AgentHow FinCatch Is DifferentHow FinCatch uses a three-layered 'financial mind' to uncover second-order effects and structural market truths.Lok Kan Chan · May 30, 2026
AgentAgents, Personal Guidance, and Memory: How We Think About Building FinCatchWhy FinCatch is being built as a layered agent system that combines a market world model, personal memory, and active guidance for equity research workflows.Lok Kan Chan · May 29, 2026
Equity ResearchHow We Built an Agentic Equity Research PipelineA behind-the-scenes look at how we redesigned equity research into a modular, agent-orchestrated pipeline that produces industry-grade reports with tighter runtime and better control.Harsh Vardhan Gupta & Shahitya Khan Shammo · May 29, 2026
MemoryHow FinCatch Remembers What You Think About the MarketMost AI assistants forget everything the moment you close the chat. FinCatch doesn't. We built a structured knowledge graph that remembers your investment views, tracks how they evolve over time, and reasons across companies and sectors — so your AI actually knows you as an investor.Austin Cheung · May 27, 2026
AgentWhy the Hard Part Isn't the LLM: The Application of Harness Engineering in FinCatchReliability in financial AI doesn't come from the model. It comes from the scaffolding. Here's how we built it.Harry Lui · May 21, 2026