Why "Chatting with Data" isn't enough
Common AI tools allow you to ask questions, but they often struggle with the precise mathematical nature of sports analytics. MarketFrame's Agentic Architecture bridges this gap. Instead of just "guessing," our agents generate and execute Python code against institutional data feeds to ensure every insight is backed by verified computations.
Code Interpretation
Dynamic script generation to solve specific analytical prompts.
Reasoning Models
LLMs that understand "basketball context" like rest days and injury impact.
Live Execution
Direct access to the same data systems used by professional quants.
The Workflow of a Professional Analyst
In the past, running a custom model meant spending hours in Jupyter Notebooks. MarketFrame automates the entire pipeline:
- Data Selection: The agent identifies relevant possession streaks and efficiency metrics.
- Ad-hoc Modeling: A specific analysis script is written to account for unique matchup variables.
- Result Interpretation: The system translates complex statistics into actionable insights for human analysts.
Free Trial Thresholds
We believe in proving value before asking for commitments. That's why we've made our AI agents accessible to the public with tiered access:
Security & Reliability
Our code execution environment is fully sandboxed and optimized for speed. Our agents use a specialized verifier system to ensure that the code they write is mathematically sound, minimizing the "hallucinations" common in standard AI systems.