Finance ops · Production

Captide redefines equity research with agentic workflows on LangGraph and LangSmith

The problem

Investment research analysts faced inefficiency extracting financial metrics from large volumes of regulatory filings and investor relations documents, constrained by the fixed-schema limitations of legacy platforms that could not accommodate customized or scalable analysis.

First attempt

Legacy platforms imposed fixed-schema constraints that prevented analysts from building customized datasets or adapting analysis to specific company metrics.

Workflow diagram · grounded in source

User defines analysis task

trigger

“allowing users to articulate complex analysis tasks in natural language”

Agent orchestrates retrieval pipeline

ai_action

“Captide's agents take over, orchestrating the entire data retrieval and processing pipeline from a big corpus of financial documents”

Parallel document processing

ai_action

“Multiple agents work simultaneously to execute ticker-specific vector store queries, retrieve relevant documents, and grade each document chunks. This approach not only minimizes latency but also eliminates the need to complicate the cod…”

Structured output generation

output

“users can request table outputs with their custom schemas to structure metrics found in distinct documents. Trustcall makes the output adhere strictly to predefined JSON schemas”

User feedback loop

feedback_loop

“thumbs-up and thumbs-down options within the platform where users can directly rate the quality of outputs. This feedback is collected and analyzed, creating a growing dataset that helps refine agent behavior and improve system performan…”

Reported outcome

Captide's agents autonomously orchestrate data retrieval and processing from large financial document corpora, enabling analysts to create customized datasets with extreme efficiency, with continuous improvement driven by user feedback.

Reported metrics

Analyst efficiencyextreme efficiency

Processing latencyminimizes latency

Investment volume analyzedexponentially larger volumes

Reported stack

LangGraphLangSmithLangGraph PlatformLangGraph Studiotrustcall

Source

https://blog.langchain.dev/how-captide-is-redefining-equity-research-with-agentic-workflows-built-on-langgraph-and-langsmith/

Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Captide's agents autonomously orchestrate data retrieval and processing from large financial document corpora, enabling analysts to create customized datasets with extreme efficiency, with continuous improvement drive…

What tools did this team use?

LangGraph, LangSmith, LangGraph Platform, LangGraph Studio, trustcall.

What results were reported?

Analyst efficiency: extreme efficiency; Processing latency: minimizes latency; Investment volume analyzed: exponentially larger volumes (source-reported, not independently verified).

What failed first in this deployment?