Building a Healthcare Accounts Receivable Platform With an AI-First Engineering Approach
Healthcare
Target Synergy and Clinical Insight Discovery platforms for immunology research.
In neuropharma, business development decisions are increasingly data-driven, but the tools supporting them often lag behind the complexity of the data itself.This case explores how a global pharma company used AI to transform fragmented scientific and market data into a scalable system for identifying high-value licensing opportunities in Alzheimer’s disease.
Nextigent AI implemented an AI-powered Target:Disease Pairing Agent tailored for licensing intelligence and partnership evaluation.
- Healthcare
- 6 months
- 6 specialists
30
30 ranked Alzheimer’s targets and assets
70%
Evaluation time reduced: 70%
↓
Enabled multiple new partnership reviews
Project Scope
The project was delivered over 6 months and included data integration, AI model adaptation, and dashboard deployment.The focus was on building a system that could continuously ingest new data and support real-time decision-making for business development teams.
Tech Stack
- Python
- Vertex AI
- Spotfire
- REST APIs
Data sources
- OpenTargets
- ChEMBL
- Pharmaproject
- PubMed
- GWAS Catalog
The Client
The client is a global pharmaceutical company specializing in neurology, with a strong focus on Alzheimer’s disease research.Their goal was to improve how partnership and licensing opportunities were identified, evaluated, and prioritized.
Business Requirements
- Reduce time required to evaluate licensing opportunities
- Combine scientific evidence with commercial potential in one model
- Continuously monitor emerging targets and assets
- Improve consistency and transparency in decision-making
- Support collaboration between BD and R&D teams
The Problem
Business development teams relied on manual analysis of fragmented data sources.Evaluating potential partnerships required weeks of work, with analysts reviewing literature, patents, and drug pipelines separately. This slowed decision-making and increased the risk of missing time-sensitive opportunities.
The Challenges
- Integrating diverse datasets across science, patents, and market intelligence
- Designing AI models that balance biological and commercial evaluation
- Ensuring outputs are usable for both BD and scientific teams
- Maintaining up-to-date insights in a rapidly evolving research landscape
Problems Revealed
During implementation, it became clear that the main issue was not lack of data, but lack of a unified evaluation system.Scientific insights and business signals existed in parallel but were not connected in a way that supported fast, confident decisions.
The Solution
Nextigent AI deployed the Target:Disease Pairing AI Agent adapted for Alzheimer’s licensing intelligence.The system integrated multi-omics data, literature, patents, and pipeline information into a unified model.Each target and asset was scored across two dimensions:
- scientific strength
- business opportunity
Achievements
- 30 Alzheimer’s targets and assets identified and ranked
- 70% reduction in evaluation time
- Faster initiation of partnership discussions
- More consistent and data-driven prioritization
The system turned licensing evaluation into a continuous, AI-driven process instead of a manual, episodic effort.
Future Plans
The client is expanding the system to other neurodegenerative and CNS disorders.Future development includes predictive modeling of partnership success and automated monitoring of competitor pipelines.
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