The Challenge
Your organization's knowledge is scattered across legacy systems, wikis, and tribal memory. Teams waste hours searching for answers that should take seconds, and critical information walks out the door every time someone leaves.
Our Approach
We design RAG pipelines that unify all knowledge sources into a single semantic search layer. Rather than replacing existing systems, we build an abstraction that ingests, chunks, and embeds content with metadata-aware filtering — delivering source-cited answers in seconds.
How We Deliver
Data Audit
Assess your document corpus — formats, quality, gaps, and access patterns
Pipeline Design
Define chunking strategy, embedding models, and indexing architecture
Build
Implement the ingestion pipeline, vector store, and retrieval layer
Tune
Optimize retrieval accuracy with evaluation benchmarks and real queries
Deploy
Launch the query interface with monitoring and feedback loops
“Modulo turned our fragmented knowledge base into a system that actually thinks. Support tickets that took 20 minutes now resolve in under 5.”
Tech Stack
Project Details
Prerequisites
- Document corpus
- Cloud infrastructure
- Data access permissions
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