Introduction: The Digital Wilderness of Pharma Data
In the pharmaceutical industry, knowledge is power—but gathering that knowledge is often an uphill climb. Competitor data, especially across regulatory and procurement portals, is buried deep within hundreds of country-specific websites. Each site is a different mountain, with its own trails, gates, and barriers. Scaling all of them manually takes immense effort, time, and patience.
At Luna Lab, our role is to act like Luna—the snow leopard guiding through difficult terrain. We research, experiment, and build intelligent systems that help organizations move with precision instead of exhaustion. Our latest development addresses a challenge that pharmaceutical companies face daily: how to extract competitor data across multiple geographies without wasting valuable human time.
The Challenge: Manual Footprints in the Snow
Traditionally, teams must:
- Visit each regulatory website manually.
- Search for product details medicine by medicine.
- Download files in Excel/CSV formats.
- Clean and filter them to extract meaningful insights.
This workflow is not only slow but also repetitive. It invites human error, especially when scaled across hundreds of medicines and dozens of markets. For companies competing on razor-thin timelines, delayed or inaccurate intelligence can be costly.
We asked ourselves: What if the snow leopard could leave silent, intelligent footprints across all these sites—fetching data, cleaning it, and returning only what matters?
Our Solution: Agentic AI Powered by LangGraph
To answer that question, Luna Lab designed an Agentic AI system that automates the entire journey from search to structured output. At its core is LangGraph, an orchestration framework that lets multiple AI agents collaborate like a pack of specialists.
The workflow looks deceptively simple:
Input: A list of target websites and the medicine name.
Output: Clean, structured Excel/CSV files with only the relevant competitor data.
But beneath this simplicity, multiple intelligent agents quietly execute complex tasks with digital precision.
How It Works: Agents in Action
Think of our system as a team of specialized guides, each one adapted for a different part of the climb:
- Search Agent – Navigates to the website, identifies input fields, and executes product searches. It remembers the trails (selectors like XPaths) for future climbs.
- Download Agent – Extracts results either by downloading available datasets or, where necessary, scraping data directly from the page.
- Process Agent – Cleans and filters the extracted datasets with Python libraries like Pandas. Only the relevant columns remain, ensuring consistency and accuracy.
Once a site is configured, the agents “remember” how to move through it. This memory ensures that when new data is published, the system doesn’t start from scratch—it simply retraces its silent paths and delivers the update.
Current Progress: Silent Power in Practice
We have already tested this system across multiple regulatory websites. Results have been encouraging:
- Structured Sites: Agents handled them effortlessly once selectors were provided.
- CAPTCHA Barriers: Human intervention is currently required, but integration with AI-based solvers is on our roadmap.
- Learning Ability: Agents stored selectors, enabling automatic future runs without reconfiguration.
In essence, what once took hours of human time can now be done in minutes—with higher accuracy and zero fatigue.
Future Scope: Expanding the Horizon
Our vision for this system mirrors Luna’s instinct to explore broader terrains. We are working on:
- Scaling Coverage: Expanding to 100+ international regulatory and procurement websites.
- Adaptive CAPTCHA Handling: Blending AI-based solvers with human-in-the-loop fallbacks.
- Structural Adaptability: Agents that self-learn new website layouts without manual retraining.
- Continuous Monitoring: Scheduled runs for uninterrupted updates, ensuring real-time intelligence.
Each of these steps moves us closer to a living digital system—adaptive, responsive, and quietly powerful.
Why This Matters: Beyond Speed
For pharmaceutical companies, the benefits are significant:
- Time Saved: Teams can focus on analysis, not data janitorial work.
- Consistency: Automated cleaning ensures uniform data structures.
- Accuracy: Fewer human errors mean more reliable competitive insights.
- Scalability: Monitoring hundreds of medicines across dozens of markets becomes feasible.
In a landscape where every detail counts, having the right data at the right time is not just an advantage—it is survival.
The Luna Philosophy: Silent Power, Digital Precision
What excites us most is not just the solution itself, but how it embodies Luna Intelligence’s brand promise: “Silent Power. Digital Precision.”
Like the snow leopard, our Agentic AI doesn’t make noise. It works quietly in the background, navigating complex digital mountains with grace and accuracy. It doesn’t roar—it delivers.
At Luna Lab, this is our role: to research, to experiment, and to ensure that technology feels less like a rigid tool and more like a natural extension of human capability.
Closing: A Trail Into the Future
The digital wilderness is vast and complex, especially for industries like pharmaceuticals. But with agentic AI, we believe it doesn’t have to be exhausting. Our latest development is just one step toward a future where intelligent systems carry the repetitive load, leaving humans free to focus on insight, strategy, and innovation.
As Luna leaves her tracks in the Himalayan snow, so too does Luna Intelligence leave its quiet mark across the digital terrain. And with each footprint, we move closer to an ecosystem where technology adapts to us—not the other way around.
