I Built an Open-Source Perplexity for Finance with Bloomberg-level data access
Content Summary
Opinion Analysis
Mainstream opinion supports the project's goal of creating a powerful, open-source financial AI agent that simplifies complex research tasks. Many commenters are impressed by the tool's ability to pull accurate data and generate structured reports. There is also enthusiasm for the open-source model, with users expressing interest in contributing and adding features like local LLM support. However, some users question the reliability of AI-generated insights and ask about the risk of hallucination. Others suggest potential improvements, such as adding investment advice features and better handling of market closure periods. Overall, the discussion reflects a strong interest in the project and its potential to disrupt traditional financial research tools.
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Valyu DeepSearch API | https://valyu.com | Data Search | Provides access to SEC filings, financial data, news, and peer-reviewed journals through a single search API |
| Daytona | https://daytona.ai | Code Execution | Allows AI agents to run real code for analysis (e.g., event windows, factor calcs) |
| Vercel AI SDK | https://vercel.com/docs/ai | AI Agent Framework | Enables natural language querying and tool calling |
| OpenAI GPT-5 | https://openai.com | Language Model | Used as the primary LLM for processing prompts and generating responses |
| Next.js | https://nextjs.org | Web Framework | Used for building the frontend of the application |
USER NEEDS
Pain Points:
- Difficulty in accessing and retrieving reliable financial data (e.g., stock prices, earnings, insider trades)
- Inability to easily search and analyze SEC filings and financial reports
- Lack of tools that can provide end-to-end research with verified sources and structured data
Problems to Solve:
- Need for a unified platform that can retrieve and analyze financial data from multiple sources
- Desire for an AI agent that can produce structured research briefs based on natural language queries
- Requirement for transparency and verifiability of AI-generated insights
Potential Solutions:
- Using a single search API (Valyu DeepSearch) to access all required financial data
- Integrating code execution (Daytona) for on-the-fly analysis
- Building a user-friendly interface that displays results with sources and visualizations
GROWTH FACTORS
Effective Strategies:
- Open-sourcing the project to build community and attract contributors
- Focusing on a specific niche (financial research) to establish product-market fit
- Leveraging existing tools and APIs (Valyu, Daytona) to reduce development time and complexity
Marketing & Acquisition:
- Sharing the project on Reddit and other communities to generate interest
- Encouraging contributions and feedback to build a loyal user base
Monetization & Product:
- The project is currently open-source and free, but could potentially offer premium features or enterprise support in the future
- Emphasis on providing accurate, verified financial data to differentiate from competitors
User Engagement:
- Building a community around the project by encouraging contributions and feedback
- Providing clear documentation and examples to make it easy for users to get started