RAG systems that
eliminate hallucinations.
We build retrieval-augmented generation pipelines that give your AI accurate, sourced answers from your private data. No fine-tuning required.
Your data, AI-accessible.
RAG (Retrieval-Augmented Generation) is a pattern that lets AI models answer accurately about your private data. Instead of training a model on your documents — which is expensive and quickly outdated — you search for relevant information at query time and inject it into the AI's context.
The result: your AI assistant answers questions about your internal knowledge base, contracts, product documentation, or customer records with citations pointing back to the source. When documents change, the answers update automatically. No retraining, no stale data.
A well-built RAG system is the difference between an AI that says "I think the policy is..." and one that says "According to section 4.2 of the Employee Handbook (updated March 2026), the policy states..." — with a link to the source document.
How we build RAG systems.
Every RAG project starts with understanding your data landscape and ends with a production system your team can rely on.
1. Data audit & strategy. We catalog your data sources — documents, databases, APIs, wikis — and assess quality, volume, and update frequency. We design a chunking strategy that preserves context and maximizes retrieval accuracy.
2. Ingestion pipeline. We build automated pipelines that process your documents, split them into semantically meaningful chunks, generate embeddings, and store them in a vector database. The pipeline handles PDFs, Word docs, HTML, Markdown, and structured data.
3. Retrieval & ranking. We implement hybrid search combining semantic similarity with keyword matching. A re-ranking step ensures the most relevant chunks surface first. We add metadata filtering so your AI respects access controls and document categories.
4. Evaluation & deployment. We build test suites that measure retrieval accuracy, answer quality, and citation correctness. We deploy with monitoring for query latency, retrieval hit rates, and user satisfaction signals.
What you get.
- Production-ready RAG pipeline with automated document ingestion
- Vector database setup (Pinecone, pgvector, or Qdrant based on your needs)
- Hybrid search with semantic and keyword retrieval plus re-ranking
- Citation tracking — every AI answer links back to its source documents
- Accuracy evaluation framework with test suites you can run continuously
- Full source code ownership and deployment documentation
- 30-day post-deployment support and optimization window
Typical engagement.
Most RAG projects take 3-5 weeks from data audit to production. Timeline depends on data volume, number of source types, and accuracy requirements.
Single data source, straightforward documents. Ideal for internal knowledge bases, product docs, or FAQ systems.
Multiple data sources, complex documents, access controls. Hybrid search with re-ranking and metadata filtering.
Multi-modal data, agentic RAG with tool use, real-time ingestion. Full evaluation framework and compliance features.
All quotes are fixed-price. We provide a detailed proposal after a free 30-minute discovery call.
Ready to unlock your data with RAG?
Book a free discovery call. We'll assess your data landscape and design the right retrieval architecture.
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