At a Glance

93% faster information search | $5M annual savings | 40% productivity gain | 600% ROI in first year

A global pharmaceutical organization transformed enterprise knowledge access from 45-minute manual searches to 3-minute AI-powered discovery, while eliminating duplicate work that cost over $5 million annually.

The Challenge

A global pharmaceutical organization with thousands of employees faced a critical knowledge management crisis. Despite having vast institutional knowledge, employees spent over 30% of their time searching for information, often failing to find relevant insights. Knowledge workers lost 15-20 hours per week to inefficient information discovery.

Information overload created significant barriers, with over 100,000 documents scattered across SharePoint, Google Drive, AWS S3, and local drives. Traditional keyword search missed 70% of relevant documents, while teams unknowingly recreated analyses, wasting over $5 million annually in duplicate work.

Critical knowledge remained locked in diverse formats - PDFs, Word documents, PowerPoints, Excel sheets, videos, and audio recordings. When employees left, years of expertise disappeared. The organization faced compliance risks from inability to quickly locate regulatory documents.

Technical challenges included achieving semantic understanding beyond keyword matching, processing 12 diverse file formats, enabling real-time search across 150 million tokens, maintaining data security with role-based access control, scaling to handle 1,000+ concurrent users, and controlling LLM costs.

The Solution: Enterprise RAG Platform

Devkraft built an enterprise-grade semantic search and RAG platform that transforms how organizations access institutional knowledge. The platform combines cutting-edge AI models, vector databases, and intelligent routing to deliver fast, accurate answers.

The multi-LLM architecture leverages OpenAI models including GPT-4o, GPT-4-turbo, GPT-4, and GPT-3.5-turbo for reasoning, with OpenAI embeddings for semantic search. The platform integrates Sonar models for advanced research with intelligent routing that automatically selects optimal models based on query complexity and cost.

Vector search utilizes PGVector, a PostgreSQL extension for high-performance similarity search. The hybrid search strategy combines semantic vector search with BM25 keyword retrieval, while FlashRank re-ranking optimizes relevance.

Multi-modal processing includes document intelligence through PyMuPDF, python-docx, python-pptx, and openpyxl. AssemblyAI provides audio and video transcription with speaker detection. Vision models extract insights from diagrams and charts, while web research capabilities through Perplexity, Tavily, and SerpAPI enable real-time external knowledge integration.

The technical architecture runs on FastAPI with async Python, using PostgreSQL with PGVector for embeddings. Celery manages distributed task queuing, while Redis handles caching. AWS S3 stores documents with CloudFront CDN, while Keycloak manages enterprise SSO.

Implementation Journey

The platform was built across four strategic phases over 18 weeks.

Phase 1: Foundation and data ingestion, deploying FastAPI backend, migrating over 100,000 documents, and generating embeddings for 150 million tokens.

Phase 2: RAG system intelligence, implementing hybrid search combining vector similarity and BM25. Multi-LLM integration connected OpenAI GPT-4o, GPT-4-turbo, and Sonar models with intelligent routing. The team implemented fallback mechanisms and added semantic caching with RediSearch.

Phase 3: User experience, integrating Keycloak for SSO and implementing role-based access control. The team developed a web portal, built Slack integration, created RESTful API, and implemented streaming responses.

Phase 4: Optimization and scale, reducing query response time from 8 seconds to under 2 seconds. Intelligent LLM routing reduced API costs by 50%, semantic caching eliminated 30% of redundant calls, and batch processing optimized operations. The team integrated Langfuse for LLM tracing.

Transformative Business Impact

Average information search time dropped from 45 minutes to 3 minutes, representing 93% improvement. Documents searched per query increased from 5-10 to thousands automatically, scaling by 100x. Duplicate work incidents decreased from 200 per year to just 20, a 90% reduction. Employee productivity gained 40% improvement, while knowledge base utilization surged from 15% to 75%, marking a 400% increase. New employee onboarding time was cut in half from 8 weeks to 4 weeks.

The strategic benefits delivered $5 million in cost avoidance from eliminating duplicate work. Innovation velocity accelerated as teams built on existing knowledge faster. Institutional memory became captured and accessible 24/7. Complete audit trails ensured compliance readiness. The combined gains delivered 600% ROI in the first year.

Key Innovation: Hybrid Intelligence

The platform's competitive advantage comes from combining semantic and keyword search.

Semantic search using PGVector finds documents by meaning, understanding synonyms and context, while BM25 keyword matching ensures exact term matches aren't missed.

FlashRank re-ranking analyzes retrieved results and reorders them based on relevance. Intelligent LLM routing automatically selects the most cost-effective model - using GPT-3.5-turbo for simple lookups and GPT-4o for complex analysis.

Semantic caching with RediSearch stores frequently asked questions, reducing redundant LLM calls by 30% and cutting costs while maintaining instant response times.