The challenge
Analysts were spending hours reading long earnings-call transcripts and filings to pull out the handful of figures, guidance changes and management signals that mattered.
The work was repetitive and error-prone, and it did not scale across a growing coverage list.
- 01
IngestPDF and transcript extraction
- 02
ExtractFigures, guidance, signals
- 03
SummarizeSpecialized summarization agent
- 04
StructureReport-structuring agent
- 05
DeliverClient-ready report
Architecture · 5 stages
- 01
Ingest
PDF and transcript extraction
- 02
Extract
Figures, guidance, signals
- 03
Summarize
Specialized summarization agent
- 04
Structure
Report-structuring agent
- 05
Deliver
Client-ready report
The solution
I designed a crew of specialized agents for extraction, summarization and structuring, orchestrated with CrewAI and LangChain.
Documents are parsed from PDF, each agent works on its own part of the problem, and the crew assembles a consistent, client-ready report. A Streamlit interface lets analysts run and review reports without touching code.
Engineering highlights
- Specialized agents with clear, single responsibilities
- Earnings-call transcripts and financial PDFs as input
- Consistent, client-ready report structure
- Analyst-friendly Streamlit interface
Technology
- Python
- CrewAI
- LangChain
- Streamlit
- PDF extraction
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