We just published research on a new pattern: Machine Learning as a Tool (MLAT) [Research]

We just published our research on what we’re calling “Machine Learning as a Tool” (MLAT) – a design pattern for integrating statistical ML models directly into LLM agent workflows as callable tools.

The Problem:

Traditional AI systems treat ML models as separate preprocessing steps. But what if we could make them first-class tools that LLM agents invoke contextually, just like web search or database queries?

Our Solution – PitchCraft:

We built this for the Google Gemini Hackathon to solve our own problem (manually writing proposals took 3+ hours). The system:

– Analyzes discovery call recordings

– Research Agent performs parallel tool calls for prospect intelligence

– Draft Agent invokes an XGBoost pricing model as a tool call

– Generates complete professional proposals via structured output parsing

– Result: 3+ hours → under 10 minutes

Technical Highlights:

– XGBoost trained on just 70 examples (40 real + 30 synthetic) with R² = 0.807

– 10:1 sample-to-feature ratio under extreme data scarcity

– Group-aware cross-validation to prevent data leakage

– Sensitivity analysis showing economically meaningful feature relationships

– Two-agent workflow with structured JSON schema output

Why This Matters:

We think MLAT has broad applicability to any domain requiring quantitative estimation + contextual reasoning. Instead of building traditional ML pipelines, you can now embed statistical models directly into conversational workflows.

Links:

– Full paper: Zenodo, ResearchGate

Would love to hear thoughts on the pattern and potential applications!