Grounded answers from your own data, with citations you can trust
RAG development connects a language model to your own content so answers are grounded in real documents instead of guesses. We handle the full pipeline: chunking, embeddings, and vector search using stores like pgvector, Pinecone, or Qdrant, followed by reranking to surface the most relevant passages. Then we wire retrieval into the LLM integration with prompts that cite sources and refuse when evidence is thin. Because retrieval quality decides answer quality, we evaluate recall, faithfulness, and citation accuracy on your real questions, not generic benchmarks. RAG reduces hallucination but never eliminates it, so we design honest fallbacks and clear, source-linked responses.
What's included
- Chunking and embedding tuned to your content
- Vector search with pgvector, Pinecone, or Qdrant
- Reranking to surface the most relevant passages
- Answers grounded with inline source citations
- Refusal when retrieved evidence is too thin
- Evaluation of recall, faithfulness, and citations
A process that delivers
A clear, collaborative path from first conversation to a product in the hands of your users.
Discover & Strategy
We dig into your goals, users, and constraints to define the right thing to build, and why.
Design & Prototype
Wireframes, UI, and interactive prototypes you can test and feel before a line of code is written.
Build & Integrate
Senior engineers ship in tight iterations with clean, reviewed, well-tested code and clear demos.
Launch & Scale
We deploy, monitor, and keep improving, so your product grows with confidence, not chaos.
RAG Development questions
Common questions about our rag development work.
It reduces hallucination but does not eliminate it. By grounding answers in passages retrieved from your data and asking the model to cite them, RAG keeps responses closer to your source of truth. Mistakes still happen when retrieval misses context, so we add citations, confidence signals, and refusals so users can verify claims.
Related services
LLM Integration
We integrate large language models into your product with streaming, structured outputs, evaluation, and sensible cost controls.
Vector Database Development
We design embedding storage and similarity search with Pinecone, Qdrant, Weaviate, or pgvector for production scale.
AI Integration
AI integration that adds LLMs, RAG, and copilots into your existing product through clean, reliable APIs.
Ready when you are
Let's build something that scales
Tell us what you're building. We'll bring the senior team, the clear process, and the engineering to make it real.