StocksTalk: A Voice-Enabled Conversational Agent for Structured Query Generation over Web Data
StocksTalk is a voice-enabled conversational agent that transforms spoken financial screening requests into executable, validated SQL queries over real-world market data The system integrates streaming speech recognition, retrieval-augmented constraint extraction, schema-grounded LLM-based SQL generation, rule-based validation, and human-in-the-loop verification within an interactive dashboard Unlike traditional template-driven financial assistants, StocksTalk exposes intermediate reasoning arti
Analysis
TL;DR
- StocksTalk is a voice-enabled conversational agent that transforms spoken financial screening requests into executable, validated SQL queries over real-world market data
- The system integrates streaming speech recognition, retrieval-augmented constraint extraction, schema-grounded LLM-based SQL generation, rule-based validation, and human-in-the-loop verification within an interactive dashboard
- Unlike traditional template-driven financial assistants, StocksTalk exposes intermediate reasoning artifacts (extracted constraints, normalized metrics, operator grounding, generated queries) for user inspection and refinement
- A curated benchmark of 150 spoken financial prompts across multiple investment strategies and input noise conditions was used for evaluation
- Experimental results demonstrate that retrieval grounding, constrained query generation, and interactive verification substantially improve constraint extraction accuracy, SQL executability, logical consistency, and multi-turn stability compared to baseline LLM approaches
Why It Matters
This work addresses a critical gap in financial AI: bridging natural language voice interaction with structured, executable database queries while maintaining transparency and user trust. For AI practitioners building domain-specific conversational agents, StocksTalk provides a proven architecture combining retrieval augmentation, schema grounding, and human-in-the-loop verification—techniques directly transferable to other high-stakes domains like healthcare, legal, and enterprise analytics where accuracy and explainability are paramount.
Technical Details
- Architecture: The system combines five core components: (1) streaming speech recognition for real-time voice input, (2) retrieval-augmented constraint extraction to ground financial terminology, (3) schema-grounded LLM-based SQL generation that maps natural language to executable queries, (4) rule-based validation ensuring query correctness, and (5) human-in-the-loop verification through an interactive dashboard
- Transparency Mechanism: StocksTalk exposes intermediate reasoning artifacts at each pipeline stage—extracted constraints, normalized financial metrics, operator grounding mappings, and generated queries—allowing users to inspect, understand, and refine outputs before execution
- Benchmark: A custom benchmark of 150 spoken financial prompts was curated, spanning multiple investment strategies (value, growth, momentum, etc.) and input noise conditions to test robustness
- Evaluation Metrics: The system was evaluated on constraint extraction accuracy, SQL executability, logical consistency, and multi-turn conversational stability, with improvements measured against baseline LLM-based approaches
- Domain Focus: Specifically designed for financial screening and stock analysis over real-world market data, with emphasis on structured query generation rather than free-form text generation
Industry Insight
- Trust through Transparency: The exposure of intermediate reasoning artifacts represents a best-practice pattern for high-stakes AI applications—users are more likely to adopt systems when they can audit and correct the reasoning pipeline, not just the final output
- Retrieval Augmentation + Schema Grounding: The combination of retrieval-augmented constraint extraction with schema-grounded LLM generation addresses two common failure modes in conversational AI: domain terminology ambiguity and structural query errors. This hybrid approach should be considered the new standard for domain-specific agents
- Voice + Structured Data Gap: StocksTalk demonstrates that voice interfaces can effectively bridge to structured data operations when paired with proper validation and human oversight—opening opportunities for voice-first analytics tools in finance, operations, and enterprise decision support where keyboard interaction is impractical
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