RAG & Generative AI
Grounded generative AI that uses your knowledge.
Start Your ProjectWe build generative AI features grounded in your data through retrieval, search, Q&A, summarization, and content generation, so output is relevant, current, and tied to what you actually know.
- Knowledge base design
- Embedding and vector store
- Retrieval pipeline
- Generation with grounding
- Evaluation framework
- Continuous improvement loop
You want AI features, search, Q&A, summarization, content generation, that reflect your own knowledge and stay current, not a model answering from stale training data. A plain LLM does not know your business and will confidently fill gaps with guesses. The fit here is any feature where output must be grounded in your real, changing content. If relevance and trustworthiness matter more than generic fluency, retrieval-grounded generation is the approach.
How We Approach It
Design the knowledge base
We structure your content for retrieval, deciding what to index and how, so the system draws on the right source material.
Build embeddings and retrieval
A vector store and retrieval pipeline that surface the most relevant content for each query before anything is generated.
Generate with grounding
Generation tied to retrieved content so output stays relevant and current, with far less hallucination than an ungrounded model.
Evaluate and improve
An evaluation framework tracking relevance, accuracy, and grounding over time, with a loop to keep improving quality as content changes.
The Difference It Makes
Grounded Output
Generation tied to your real data.
Current
Answers reflect your latest content.
Trustworthy
Less hallucination, more citation.
Improves
Evaluation loop for ongoing quality.
Technologies We Use
Common Questions
How is this different from a plain LLM?
How do you measure quality?
Related Services
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