Stop your LLM from hallucinating. A full 6-layer RAG architecture engineered for your data — built for regulated industries where accuracy is not optional.
You built a RAG pipeline. It returned wrong answers three times. Your team quietly went back to manual search. The system still runs — it just costs money and delivers no value. This is the most common RAG outcome we see.
Most RAG failures start with how documents are split. Overlapping chunks that miss context boundaries, fixed-size splits that cut tables in half, no hierarchy for multi-document relationships. The retrieval cannot work if the index is wrong.
Vector similarity alone is not enough. Without re-ranking, cross-encoder scoring, and hybrid retrieval combining semantic + keyword search, you get relevant documents with irrelevant sections filling your context window.
Finance, legal, and healthcare cannot use hallucinating AI. They need citation-backed answers, source traceability, and — often — fully on-premise deployment with no data leaving the environment. Generic RAG tutorials do not cover this.
Document-type-specific chunking rules, overlap ratios, hierarchy handling, table and image extraction, and metadata tagging schema.
Benchmarked model comparison for your domain. Fine-tuning recommendations if needed. Dimension optimization for your query volume.
Index design, namespace/collection strategy, filtering metadata schema, and scaling plan for your projected document volume.
Combined vector + BM25 keyword retrieval, query expansion, and dynamic retrieval count based on query complexity.
Cross-encoder re-ranking, context compression, deduplication, and context window budget allocation by source type.
System prompt architecture, citation format, confidence scoring, fallback behavior, and hallucination guardrails.
Investment research, regulatory filings, credit memos, earnings transcripts. RAG built for accuracy under compliance scrutiny.
Contract analysis, case research, discovery document review. RAG that cites sources and never fabricates precedent.
Clinical protocols, formularies, patient intake. RAG with HIPAA-compliant architecture and on-prem deployment options.
We map your data sources, document types, query patterns, and current system if one exists. 45 minutes.
Diagnostic report on your current RAG system (or gap analysis if starting fresh). Identifies exactly which layers are failing.
Full written architecture specification covering all 6 layers with implementation details and vendor recommendations.
You take the blueprint and build in-house, or we implement it. Either way, you own every component permanently.
Yes. Most of our RAG engagements are rescue projects. We start with a diagnostic to identify which of the 6 layers is failing — chunking strategy, embedding model, retrieval logic, re-ranking, context assembly, or generation instructions. Most problems are fixable without a full rebuild.
Pinecone, Weaviate, Qdrant, pgvector, Chroma, and Azure AI Search. We recommend the right one based on your data volume, query pattern, and infrastructure — not what is easiest for us to implement.
Regulated industries often cannot use cloud-hosted LLMs. We design RAG architectures for fully on-prem deployments using open-weight models (Llama 3, Mistral, Phi-3) with no data leaving your environment.
Ingestion and chunking strategy, embedding model selection and tuning, vector store architecture, hybrid retrieval (vector + keyword), re-ranking and context compression, and generation prompt engineering. Each layer has measurable quality impact. Most RAG failures are single-layer problems.
The Architecture Blueprint takes 10 business days. Full implementation is typically 4–8 weeks depending on data volume and integration complexity. The blueprint gives you everything needed to build it in-house or with us.
From $5,000. 10-day blueprint. Architecture you own permanently — whether you build it in-house or with us.