Knowledge Management Systems
We modernize legacy knowledge management by implementing AI-powered semantic search and RAG systems. We unify your scattered documents, technical manuals, PDFs, and historical data into a single, intelligent repository that understands context, intent, and complex technical concepts.
Core Features
Semantic & Hybrid Search
Move beyond exact keyword matches. Our systems use vector embeddings to understand the meaning behind queries, combined with BM25 for precise keyword accuracy.
Automated Metadata Generation
Using LLMs to automatically read incoming documents and generate accurate tags, categories, and summaries for better organization.
Multi-Format Ingestion
Pipelines capable of reading scanned PDFs, technical diagrams, video transcripts, and complex spreadsheets into a unified index.
Knowledge Graphs
Mapping relationships between entities across your documents to provide deep, contextual answers that span multiple sources.
Our Process
Data Audit & Taxonomy Design
Week 1-2Evaluating existing knowledge bases and designing a modern taxonomy and metadata structure optimized for AI retrieval.
Ingestion & Enrichment Pipelines
Week 3-5Building the ETL systems that extract text from various formats, run OCR, and use LLMs to auto-generate metadata and summaries.
Vector & Graph Database Setup
Week 5-7Configuring the hybrid search infrastructure using vector databases (Pinecone/Milvus) and potentially graph databases (Neo4j).
Search UI & API Development
Week 8-9Developing the user interface or internal APIs that allow employees to query the knowledge base and view rich, cited results.
Tuning & Feedback Loops
Week 10Implementing analytics to track search success rates and fine-tuning the retrieval algorithms based on user interactions.
Technologies We Use
FAQ
How is this different from Enterprise Search (like traditional Elasticsearch)?
Can the system handle complex, scanned technical PDFs?
Do users just get a list of links like Google?
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