RAG Systems

RAG Development Services

Retrieval-augmented generation (RAG) lets you give any LLM accurate, up-to-date knowledge from your own documents, databases, and APIs — without fine-tuning. SaTekk builds production-grade RAG pipelines that ingest your data, chunk and embed it intelligently, store it in a vector database, and retrieve the most relevant context at inference time. The result: an AI that answers questions about your business with genuine accuracy, not hallucinations.

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What our RAG builds include

01

Vector Database Setup

Pinecone, Weaviate, Qdrant, pgvector, or Chroma — we select and configure the right vector store for your data volume and latency requirements.

02

Document Ingestion Pipelines

Automated ingestion from PDFs, Word docs, websites, Notion, Confluence, Google Drive, and databases — with smart chunking and metadata extraction.

03

Hybrid Search

Combines dense vector similarity search with sparse keyword search (BM25) for significantly higher retrieval accuracy across diverse query types.

04

Re-ranking & Query Expansion

Cross-encoder re-ranking and query expansion techniques that improve retrieval precision, especially for complex or ambiguous questions.

05

Evaluation & Quality Monitoring

Automated RAG eval pipelines (using RAGAS or custom evals) that track faithfulness, answer relevance, and context recall in production.

06

Streaming & Latency Optimization

Token streaming, caching layers, and retrieval optimization to hit sub-2-second response times even with large knowledge bases.

Free
30-min strategy call
100%
source-code ownership
Fixed
timeline & pricing
30d
post-launch support

Related reading

01

RAG vs fine-tuning: which one your AI needs

A decision framework, side-by-side comparison, and the real cost signals.

02

Building a private ChatGPT on company data

The honest build-vs-buy breakdown for retrieval over your own documents.

03

Knowledge and retrieval projects we delivered

Outcomes from production SaTekk builds, with the numbers behind them.

Frequently asked questions

What is RAG and when do I need it?+
RAG (retrieval-augmented generation) lets an LLM access your specific knowledge — internal docs, product manuals, case files, research — at query time, rather than relying on its training data. You need RAG when you want AI answers grounded in your proprietary data, updated frequently, and without hallucinations about your business.
How accurate are RAG systems?+
Accuracy depends on retrieval quality, chunk size, and the LLM used. Well-engineered RAG systems typically achieve 85–95%+ answer faithfulness on domain-specific queries. We run evaluation benchmarks on your data before launch and monitor accuracy in production to maintain quality.
Can RAG work with my existing documents and databases?+
Yes. We ingest virtually any source: PDFs, Word documents, spreadsheets, websites, Notion pages, Confluence wikis, SQL databases, and any REST API. We build the ingestion pipeline, set up scheduled re-indexing to keep your knowledge fresh, and handle all the chunking and embedding logistics.
How long does it take to build a RAG system?+
A focused RAG system with a clean data source typically takes 2–4 weeks. Multi-source systems with hybrid search and evals take 4–8 weeks. Enterprise-grade systems with high availability, complex ingestion pipelines, and custom eval frameworks take 8–12 weeks.
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Your data deserves better than hallucinations.

Book a free call and we'll show you exactly how a RAG system would work on your documents — with a live demo using your data.

Book your free call →[email protected]