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Multi-Modal RAG Knowledge Hub for Pharmaceutical Research Acceleration

Summary:

  • VentureSEA Digital partnered with a leading pharmaceutical research laboratory in Indonesia; addressed slow, manual review of biochemistry and pharmaceutical journals
  • Built a multi-modal RAG knowledge hub that understands both text and scientific visuals
  • Reduced research review time from weeks to hours, without compromising scientific rigor

Overview

A leading research laboratory under one of Indonesia’s largest pharmaceutical groups partnered with VentureSEA to modernize its research knowledge management process. The laboratory manages an extensive and growing collection of biochemistry and pharmaceutical journals, many of which contain complex scientific charts, experimental figures, and unstructured tables.

Traditional document review required weeks of manual effort by highly specialized researchers, creating bottlenecks across the research lifecycle. VentureSEA delivered a multi-modal Retrieval-Augmented Generation (RAG) platform that transformed these journals into a searchable, interactive knowledge hub—dramatically accelerating insight discovery and research productivity.

Challenge

The laboratory faced several structural challenges:

  • Scientific journals contained complex visual information such as charts, graphs, and experimental tables

  • Conventional text-based search tools failed to capture insights embedded in images

  • Manual review of hundreds of journals took weeks per research initiative

  • Retrieving specific experimental evidence to support ongoing research was slow and inefficient

  • Knowledge was siloed across documents, limiting reuse and cross-project learning

These constraints slowed discovery cycles and limited the organization’s ability to make timely, data-driven research decisions.

Goal

The laboratory aimed to build a centralized, AI-powered knowledge system that could:

  • Extract insights from both text and visual elements within scientific documents 

  • Enable semantic search across hundreds of journals and experiments

  • Allow researchers to quickly retrieve evidence-backed answers

  • Preserve traceability by linking insights back to original charts, tables, and pages

  • Significantly reduce research time while maintaining scientific rigor

How did VentureSEA search across hundreds of scientific journals semantically, not manually?

VentureSEA designed and implemented a multi-modal RAG platform tailored to pharmaceutical research workflows.

  • Built a multi-modal ingestion pipeline capable of processing PDFs and image-heavy scientific documents

  • Used vision-capable LLMs to interpret charts, figures, and experimental tables alongside text

  • Generated structured summaries from visual and textual content

  • Embedded extracted knowledge using high-dimensional text embeddings for semantic retrieval

  • Implemented a RAG-based chatbot that answers research queries with grounded, reference-backed responses

  • Attached original document pages, charts, and tables as citations to support validation and reporting

  • Developed a user-friendly interface for document upload and search

  • Built an administrative console to manage documents, monitor system usage, and ensure data integrity

The system was designed to be scalable, auditable, and aligned with scientific research best practices.

Outcome & Impact

  • Reduced journal review and insight discovery time from weeks to hours

  • Enabled researchers to retrieve targeted experimental insights on demand

  • Improved reuse of historical research and experimental findings

  • Increased research throughput without additional headcount

  • Enhanced confidence in AI-assisted research through transparent source attribution

Technical Stack

  • LLM Framework: LangChain

  • LLM Models: GPT-4 Vision, GPT-4o

  • Embedding Model: text-embedding-3-large

  • Vector Store: Pinecone

  • Image Store: Google Cloud Storage

  • Session & Metadata Store: PostgreSQL

  • Backend: FastAPI

  • Frontend: React, HTML/CSS

Business Value

By transforming unstructured scientific literature into a searchable, multi-modal knowledge asset, VentureSEA enabled the laboratory to accelerate discovery while preserving scientific accuracy and traceability. The platform positioned the organization to scale research efforts, shorten innovation cycles, and fully leverage its accumulated scientific knowledge.

Industry

Healthcare & Insurance

Client profile

Pharmaceutical research laboratory (Indonesia)

Core problem

Manual, time-intensive review of image-heavy scientific journals

Services Delivered

Multi-modal Retrieval-Augmented Generation (RAG) Knowledge Hub

Impact

Journal review time reduced from weeks to hours, faster insight discovery, increased research throughput without additional researchers

More topics

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