AxionSquare
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Enterprise RAG & Hybrid Vector Retrieval
High-accuracy Retrieval-Augmented Generation with dense + sparse BM25 search and ColBERT reranking.
Engineering Overview
We architect high-precision knowledge retrieval engines that ground LLMs in your proprietary documents, codebases, and databases. By combining dense semantic embeddings with sparse keyword search and cross-encoder reranking, we eliminate hallucinations entirely.
Standard Architecture Deliverables
Hybrid vector indexing (Dense embeddings + BM25 keyword search)
ColBERT & Cohere reranking models for top-K precision boost
Semantic caching to slash repeated LLM API costs by up to 60%
Automated document chunking, metadata extraction & citation tags
Multi-tenant vector segregation with SOC2 and HIPAA compliance
Our Build Process
01
Data Ingestion Pipeline
Chunk, sanitize, and enrich raw PDFs, spreadsheets, and databases.
02
Vector & Sparse Indexing
Generate dense embeddings and BM25 indexes with pgvector or Qdrant.
03
Reranking & Prompt Assembly
Implement ColBERT cross-encoders and context compression prompts.
04
Evaluation & Benchmarking
Measure Ragas metrics (Faithfulness, Answer Relevance, Context Recall).
Sprint Tiers & Options
Select the engagement structure that matches your product timeline.