AI Representative for Big Pharma
Pharma Marketing Agency – full diclosing
In progress
Project Challenges:
Developing a New Format
We needed to create a new, engaging content format for US physicians – one that effectively communicates about the drug and treatment methods.
Maximizing Data Value
Each physician engagement is costly, so it was essential to extract the maximum amount of meaningful data – ideally, insights that can’t be obtained through traditional methods.
Creating Additional Brand Value
The new format had to deliver extra value for Big Pharma brands, making the most of every interaction within this high-cost traffic segment.
Compliance Constraints
All content had to go through industry-standard medical and legal approvals, imposing strict limitations on how AI could be used.
Our Solutions:
Compliant AI Chat
A new format that speeds up physicians’ access to information – combining AI flexibility with fully approved, controlled content.
Deep NPI-Level Analytics
Captures and maps physician queries to NPI, revealing real intent. Previously, such insights were only available through expensive surveys.
Real-Time Next Best Action Engine
Transforms conversation context into instant CTAs, rep alerts, or helpful tools – enabling immediate value extraction without delay.
Flexible Deployment Options
The same backend powers multiple formats: an embeddable widget, microsite, product theater kiosk, or standalone app.
We Came Up With the Whole Product From Zero
B.A.D.A.S. is a breakthrough multi-agent system for pharmaceutical communication – a chat-based Brand Rep that merges compliant dialogue, actionable analytics, and instant brand orchestration.
It’s designed for pharmaceutical marketing teams seeking precision, compliance, and speed in every HCP interaction.
Chat Architecture
B.A.D.A.S. is built like a brain with two hemispheres and one orchestrator:
- General Hemisphere – keeps conversation human and contextual.
- Clinical Hemisphere – assembles only approved “Bits” of text, ensuring zero hallucinations.
- Brainstem Layer – manages routing, safety filters, and the NBA logic.
Every response is reconstructed from Bit IDs, NBA IDs, and version hashes – total auditability.
Dual-Hemisphere Logic
NBA Engine
The Next Best Action engine weighs dozens of data points to decide what happens next – from the user’s current query to CRM, CLM, Rx, and Marketing signals.
It’s context-aware, self-scoring, and tunable by brand goals, allowing managers to emphasize sample offers one month and formulary wins the next.
Result: instant, personalized activation while the doctor is still in chat.
Data Scheme
NBA Engine Datapoints
True Vision Analytics
While competitors chase clicks, True Vision records physicians’ real questions – the clearest signal of intent.
It merges three analytic layers:
- Aggregated Trends – what doctors ask most often.
- Anonymous Queries – unmet needs and content gaps.
- Recognized HCP Queries – exact intents linked to NPI data, feeding sales strategy.
Each insight flows back into content improvement and NBA optimization – turning conversation into continuous intelligence.
Deployment Options
One core engine, many entry points:
- Standalone Web Interface: brand hub or embedded widget that works 24/7 – even when reps can’t.
- Field Rep Assistant (Mobile): instant access to approved content, NBAs, and voice-activated utilities during calls.
- Embedded Widget for brand sites: the lightest setup – a chat block that lives inside an existing brand website and instantly connects HCPs to approved data and brand actions.
- Tablet App for Pharma events: installed as an interactive kiosk for congresses and product theaters – perfect for hands-on demos and data capture.
Both share the same central knowledge base, keeping compliance airtight and updates synchronized in seconds.
Disease Knowledge Hub – Web Prototype
Result
A completely new format was developed – fully compliant with strict industry regulations – that significantly reduces the cost of physician engagement and solves two major market challenges: understanding physician behavior during content interaction and applying those insights in real time.
