RAG, data and AI quality
AI your team can trust, because it is measured.
Retrieval-augmented generation, or RAG, lets an AI answer from your own documents and data instead of from guesswork, and show where an answer came from. Done well, you can ask a question about your own business in plain language and get a sourced, checked answer.
Ltiora builds RAG pipelines, and the measurement that makes them trustworthy: LLM judges and evals so quality is scored rather than guessed, and token optimization so the bill stays predictable. We also model data and build data-quality alerting, because an AI is only as reliable as the data underneath it.
What it looks like in practice
- RAG pipelines that answer from your own documents and data
- LLM judges and evals so quality is scored, not guessed
- Token optimization so the AI bill stays predictable
- Answers that cite the documents they came from
- Evaluation sets built from your own questions
- Cost tracking designed in from day one
Imagine Ask a question about your own business in plain language and get an answer that cites where it came from, checked before you see it.
How an engagement runs
- 01
Discover
A real conversation about the problem, then a close look at how the work actually happens today. We tell you plainly if we are not the right fit.
- 02
Prototype
A working slice on your own data, not a slide deck, so you can judge it with your hands.
- 03
Ship
Production-grade delivery: security, cost controls and the foundations that keep it standing.
- 04
Operate
Evals, monitoring and steady improvement, so the system gets better after launch instead of decaying.
Questions people ask
What is RAG?
RAG stands for retrieval-augmented generation. The system first retrieves the most relevant passages from your documents or data, then asks a language model to answer using only that material, which makes answers more accurate and traceable.
What is an LLM judge, and what are evals?
Evals are tests for AI behaviour: a set of example questions with a way to score the answers. An LLM judge is a model used to grade answers against criteria, so quality can be measured at scale and tracked over time.
How do you stop an assistant from making things up?
By grounding answers in a defined source of facts, checking answers against that source, and declining when the information is not there. Evaluation then verifies the behaviour continues to hold as things change.
Can you reduce what we spend on AI?
Token optimization and financial optimization of AI systems are part of what we do: choosing the cheapest approach that is reliably right and measuring it.