In this clip, the LeanData Product team walks through LeanData’s AI Inference Node.
Using this node, you can embed a reasoning engine directly into your workflow to interpret unstructured data – think activity notes or open-text survey feedback – and extract structured values that you can use within your routing logic. By deploying advanced LLMs (e.g., Gemini, ChatGPT) natively within your routing graph, you can target exactly where AI is needed to analyze messy data while keeping your core business logic visible and controlled within the visual flow. This empowers revenue teams to transform complex data into real-time insights and immediate, automated decisions.
Use Cases:
- Intent Categorization: Use LLM reasoning to classify “Contact Us” inquiries as ‘Sales’, ‘Support’, or ‘Billing’ to eliminate manual triage and accelerate lead response times.
- Data Extraction: Automatically track competitive insights by extracting competitor mentions from “Loss Reason” or “Discovery Notes” to instantly trigger automated nurture tracks or competitive alerts.
- Sentiment Analysis: Protect retention by analyzing sentiment in survey feedback or case comments and immediately routing “Negative” signals to a Customer Success Manager for intervention.
- Data Normalization: Improve logic scalability by replacing complex Regex with natural language prompts to extract email domains or clean up “messy” job titles.
Read the full documentation here, or catch the full conversation here.
