AI-Powered Pharmaceutical Sales Training Platform
Multi-agent AI platform for pharmaceutical sales enablement — built on Graph-Enhanced RAG technology to unify cases, clinical evidence, and sales methodology into a closed-loop knowledge system. Tech Lead for 3 role-specific agents (Archive, Strategy, Advisor) with a hybrid graph/vector data layer that turns every successful visit into reusable team knowledge.
Alex, a pharma sales rep, uses the Sales Advisor Agent to prepare for a challenging first visit — finding similar peer cases, building an evidence-backed visit plan, and practicing with an AI role-play simulation.
The Business Problem
Front-line sales knowhow is locked in individuals — the organization cannot inherit it
Onboarding relies on word-of-mouth, leading to long ramp cycles and inconsistent quality
Success stories cannot be standardized or scaled across the sales organization
Healthcare Professional (HCP) visit windows are getting shorter — every interaction must be precise and high-value
The Solution — A Closed-Loop Knowledge System
Applicable across Sales, Training, Marketing, Medical, and Market Access departments
Market strategies, success cases, and product knowledge are AI-parsed to extract core problem-solving methods — human-AI collaboration builds the knowledge system
AI helps reps quickly master sales techniques, diagnose real scenarios, and generate evidence-backed visit recommendations
Successful cases and validated strategies feed back into the system — the knowledge flywheel continuously accelerates
Graph-Enhanced RAG + Hybrid Data Architecture
See Graph-Enhanced RAG project →Combining subjective case knowledge with objective clinical evidence through a hybrid graph/vector data store — multi-hop traversal for grounded, traceable recommendations
Knowledge graph nodes: product information · treatment guidelines · clinical evidence · research papers · competitive intelligence
Medical Evidence Graph (Neptune): drug properties, clinical trials, safety profiles, treatment guidelines · Case Event Graph (Neptune): visit events, doctor profiles, objections, outcomes · STAR Case Index (OpenSearch): semantic + keyword hybrid retrieval
Auto entity extraction — drug, physician, objection, and outcome linked into a queryable knowledge graph
Multi-hop reasoning — connect sales strategy to clinical evidence to peer success cases in a single query
Hallucination reduction — every recommendation traced back to a real case, paper, or validated strategy
Three Agents, One Closed-Loop Workflow
Accepts raw market strategies and training materials. AI extracts structured strategies with human review before committing to the strategy library.
Extracts structured STAR (Situation, Task, Action, Result) data from visit notes and case materials. Matches to strategy library entries, then saves to the case library.
Cross-graph retrieval across case library, knowledge base, and strategy library. Dynamic skill loading — SPIN selling, objection handling, customer profiling loaded on demand at runtime without redeployment. This is the main demo video above.
System Architecture
Competitive Advantages
Every successful visit feeds back into the knowledge layer — the more the team uses it, the smarter it gets. Experience stops being locked in individuals.
New capabilities (SPIN selling, competitive analysis, objection patterns) are Markdown-configured and hot-loaded at runtime — business users extend AI without touching code.
Every recommendation is grounded in real cases, clinical papers, or validated strategies — powered by the same Graph-Enhanced RAG engine built for enterprise knowledge retrieval.
See Graph-Enhanced RAG project →Tech Stack
Agent Frameworks & AI
Knowledge Graph
Backend & Data
Document Processing
Infrastructure & Deployment
All data, metrics, screenshots, and demos shown are based on publicly available information or anonymized data.