Programmatic Tool Calling for Privacy-Sensitive AI Assistants

How ForeStrat eliminates data leakage to third-party LLMs with a sandboxed Python execution approach — 90%+ token cost reduction with zero data exposure.

ForeStrat AI

Research Team

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We are publishing a Research Paper - "Programmatic Tool Calling for privacy sensitive AI assistants"


Issue: AI Analytics have to see your entire position book, P&L, and order flow to answer questions and provide insights. This is a huge security risk and very expensive token burn.


Solution: At Forestrat we have built an elegant, generic solution for this problem.


Instead of letting the LLM touch your data, we provided it a sandbox. We let it write Python code that runs entirely inside our platform. Your actual data never leaves the platform. The model only ever sees the shape of the result, never the values. Model Code is validated with our proprietary validator for malicious code / hallucinations.


Outcome: 90+% reduction in token costs. Zero data leakage to third-party LLMs. Convert conversation to results.


Would love your thoughts — have you hit this privacy vs. intelligence tradeoff in your own stack? Message me your email to get the full research paper.


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