By the Engineering Team at Synthio Labs

Pharmaceutical field teams face a significant data bottleneck. Representatives spend hours weekly manually routing data between CRMs, dashboards, and email clients. We designed Jarvis as a conversational AI copilot to address this friction. A critical engineering challenge was converting unstructured data into precise, structured enterprise actions.

We developed the Agentic Extraction Engine to solve this.

The Middleware Challenge

Standard Large Language Models produce text summaries from user inputs. Enterprise workflows require specific system triggers. A single voice command regarding a physician's interest in safety data must simultaneously log a CRM interaction, tag sentiment, retrieve a document, and schedule a follow-up.

Connecting a generative model directly to a database creates reliability issues. We built a dedicated middleware solution, the Agentic Extraction Engine, to bridge the gap between unstructured inputs and structured database records.

Architecture and Event Ingestion

We moved away from hard-coded logic for specific tasks. We implemented a modular Agentic Extraction Layer that functions through three distinct stages.

1. Unified Event Ingestion

Inputs flow into a single ingestion pipe. Data enters the system from Pre-Call queries, Post-Call voice notes, or system-generated alerts like "New Next Best Action." This approach decouples the data source from the processing logic.

2. Intelligent Extraction

The extraction logic parses raw data to identify three critical components:

• Event: The occurrence type (e.g., meeting, objection, app usage)
• Context: The entities involved (e.g., specific HCP, NPI number)
• Intent: The necessary downstream operation

This layer filters conversational noise to isolate the "golden record" variables required for database entry.

3. The Helper Service

Structured data passes to the Journey Helper Service. This service uses a modular framework. The system dynamically selects the correct module to execute based on the extracted tags.

This modularity allows the system to programmatically take different agentic actions such as:

• CRM Integration: Automatically populating fields in systems like Veeva or Salesforce
• Next Best Action (NBA): Triggering marketing journeys based on detected competitor mentions
• Content Generation: Constructing draft emails to HCPs based on specific clinical interests mentioned in the voice note

Summary

The Agentic Extraction Layer transforms voice inputs into Field Intelligence. This architecture reclaims time for representatives and ensures immediate data integration for the business. Jarvis effectively structures unstructured data to drive enterprise automation.