Role
Development
Technologies
Python, FastAPI, React
AI: Anthropic Sonnet 3.5, Langchain, LCEL, Langsmith
employees
Backend and frontend developers, a QA engineer, 2 AI engineers and an AI solutions architect.
duration
3 Months
customer
The client specializes on investment fund analysis. The software is aimed to dramatically reduce the time investment analysts spend reading fund descriptions, presentations and financial data. The solution allows to create condensed and to the point analysis of the funding firm, track record of previous funds, etc.
Background and problem
The software built for financial and investment analysts ingests a set of documents in various formats and generates customized financial documents, tables, reports and diagrams. The program supports history of changes (with undo/redo functionality), and a native chat interface, answering user questions about the financial data and generating document sections upon natural language request.
The application uses multiple Anthropic agents, complex workflows and multi-step prompting with Langchain and LCEL.
solution
The main challenge of the project was to analayze multi-page (over 100) documents and make the model produce consistent results over multiple prompts, without using RAG. We utilized a huge 200k token context of Sonnet 3.5 and achieved good results in context retention, fact extraction and generation accuracy.
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