RAG Development Services — Retrieval-Augmented Generation
Build AI systems that answer from your proprietary data — not hallucinations. Enterprise-grade RAG pipelines with hybrid retrieval, re-ranking, and citation-level transparency.
Enterprise RAG Architecture Components
Six critical layers that separate a production RAG system from a weekend prototype.
Document Ingestion Pipelines
Ingest PDFs, Word docs, HTML, Confluence, Notion, SharePoint, and custom data sources — with intelligent chunking strategies that preserve semantic meaning across paragraph and section boundaries.
Vector Database Architecture
Purpose-built vector store selection and configuration — Pinecone for managed scale, Weaviate for hybrid search, pgvector for existing Postgres stacks, Chroma for local development.
Embedding Model Selection
Model selection across OpenAI text-embedding-3-large, Cohere embed-v3, and open-source alternatives — benchmarked on your domain for retrieval accuracy before committing.
Hybrid Retrieval System
Combine dense vector similarity with sparse BM25 keyword search. Hybrid retrieval consistently outperforms pure vector search on domain-specific terminology and named entities.
Re-Ranking & Query Optimization
Cross-encoder re-ranking of candidate chunks for precision. Query decomposition, HyDE (Hypothetical Document Embeddings), and query expansion for improved recall on complex questions.
Guardrails & Hallucination Prevention
Retrieval confidence thresholds, source grounding validation, citation injection, and factual consistency checks — so every answer is traceable to a source document.
85% Hallucination Reduction
Hallucinations are a systemic failure of naive RAG — not an inevitable limitation of LLMs. We engineer multi-layer prevention so your AI only says things it can back up with sources.
RAGAS Evaluation Scores
Measured using RAGAS framework on held-out eval sets.
How We Build Production RAG Systems
End-to-end delivery in 6–10 weeks for most corpus sizes.
Data Audit & Corpus Analysis
We audit your document corpus — volume, formats, quality, structure, and update frequency — to inform chunking strategy, embedding model selection, and retrieval architecture decisions.
Embedding & Indexing Pipeline
Build the ingestion pipeline: document parsing, intelligent chunking (semantic, token-aware, or hierarchical based on content structure), embedding generation, and vector store indexing with metadata.
Retrieval System Build
Implement hybrid retrieval (dense + sparse), re-ranking, and query optimization strategies. Benchmark retrieval metrics — Recall@K, MRR, NDCG — against a held-out evaluation set from your documents.
Generation Pipeline & Guardrails
Wire the retrieval layer to your LLM with prompt templates optimized for grounded answering. Add confidence scoring, citation formatting, and fallback behavior for out-of-corpus queries.
Evaluation & Continuous Improvement
RAGAS-based evaluation framework covering faithfulness, answer relevancy, context precision, and context recall. Continuous monitoring with drift detection as your document corpus evolves.
RAG Technology Stack
Want AI that actually knows your data?
Free 30-minute RAG architecture consultation — we'll assess your document corpus and recommend the right retrieval architecture.
Related Services
Frequently Asked Questions
Technical answers about RAG systems from our AI engineering team.
Build an AI System Grounded in Your Data
Schedule a 30-minute RAG architecture review with our senior AI engineers. We'll assess your document corpus, recommend retrieval strategies, and give you a realistic accuracy estimate before you commit.