CASE STUDY

AI-driven Chatbot Optimization: Achieving Human-Level Accuracy and Speed in Data Retrieval

Key Highlights

A leading pharmaceutical company needed a no-code solution for querying large-scale multi-modal data. This company faced challenges with fragmented data and limited coding expertise, making data retrieval and analysis time-consuming and complex for non-bioinformaticians.

Elucidata developed an LLM-powered chatbot with natural language query processing, allowing users to query harmonized, multi-modal data without coding. The chatbot used a harmonized, AI-ready knowledge base and RAG system, supported by a user-friendly GUI, to streamline data access and enhance biological context in responses.

This solution led to 5x broader adoption by researchers, real-time query responses, and a $100K annual reduction in infrastructure costs.

Get your case study now
Please enter only business email ids.
Thank you for showing interest!

To know more about us, book a demo here.
Oops! Something went wrong while submitting the form.

All Case Studies

Case study: Accelerated Target ID using ML-Ready data on Polly

Data-Centric Cross-Species Target Discovery with Polly KG

Read More
Case study: Accelerated Target ID using ML-Ready data on Polly

Polly Knowledge Graph (KG): Co-built, evidence-backed biology for target discovery & validation

Read More
Case study: Accelerated Target ID using ML-Ready data on Polly

Data-Driven Capacity Modeling with Agentic AI

Read More
Case study: Accelerated Target ID using ML-Ready data on Polly

Polly’s High-Throughput Spatial Metabolomics Pipeline Accelerated Data-to-Insight by 3X

Read More
Case study: Accelerated Target ID using ML-Ready data on Polly

Modernizing a Legacy R&D Platform with GenAI-Driven Workflow and Reporting Automation

Read More
Case study: Accelerated Target ID using ML-Ready data on Polly

Data Interoperability as a Service: 6X Faster Clinico-Genomic Data Integration & Analysis with Polly

Read More
Request Demo