AI Integration
Our RAG pipelines connect large language models directly to your documents, databases, and knowledge repositories. Every answer is grounded in your data, cited with sources, and hallucination-free.
The Problem
Automated parsing of PDFs, Word docs, Confluence pages, Notion databases, and structured data into optimized vector embeddings - Without this, you risk wasting time, money, and competitive opportunities.
Combine dense vector search with keyword matching and metadata filtering for maximum retrieval accuracy - Without this, you risk wasting time, money, and competitive opportunities.
Advanced document chunking with semantic boundaries, overlapping windows, and hierarchical indexing for optimal context - Without this, you risk wasting time, money, and competitive opportunities.
How We Do It
We catalog your data sources, document types, and information architecture to design the optimal retrieval strategy
Design the ingestion, embedding, indexing, and retrieval components with your scale and latency requirements in mind
Implement the pipeline with rigorous evaluation using golden datasets, measuring retrieval precision, recall, and answer quality
Launch to production with monitoring, A/B testing of retrieval strategies, and continuous quality improvement
The Proof
CodeLeap transformed our vision into a complete product in just 3 months. The quality and commitment were exceptional - we could not have achieved this on our own in an entire year.
Sarah Chen
Chief Technology Officer, TechVista Inc.
Average efficiency gain for clients after AI integration
What You Get
Timeline: 6-10 weeks
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