RAG and knowledge systems
Quantlix builds retrieval-augmented generation systems that connect language models to approved organizational knowledge, with source citations and access-aware retrieval.
A clear purpose. A practical approach.
A knowledge assistant depends on the quality and accessibility of its sources. Quantlix tests retrieval and answer quality separately to identify the real constraint.
Bring representative documents and questions, including questions the system should decline. Define who owns source updates and permission changes.
Where we can help
A focused scope, shaped around your priorities.
Knowledge preparation
Review document structure, freshness, ownership and permissions.
Retrieval design
Combine search, ranking and context selection for real questions.
Grounded answers
Show sources and evaluate unsupported or incomplete responses.
Knowledge with a traceable source
Retrieve authorized material before generating an answer with references.
- Approved sources
- Retrieval
- Language model
- Cited answer
What the work puts in your hands
Agree on useful, reviewable deliverables before the work begins.
- Knowledge inventory
- Retrieval prototype
- Question evaluation set
- Update strategy
Good work starts with a shared understanding.
We make the decisions together, then make the next step clear.
Start with the real problem
Discuss the people, business goals and constraints behind the request. Agree on the scope and what a useful outcome looks like.
Make the direction tangible
Use working sessions, research and early drafts to explore the options. Review the tradeoffs with your team before committing to a direction.
Work in reviewable increments
Bring the agreed work into focus through regular reviews. Document the decisions, hand over the deliverables and plan any ongoing support.
Questions, answered
The practical details to consider before starting.
How does RAG differ from fine-tuning?
RAG retrieves source material at request time. Fine-tuning adapts model behavior through training examples.
Can users see restricted documents?
Retrieval must apply source permissions before presenting material to the model or user.
Does RAG eliminate incorrect answers?
Retrieval can support grounding, but answer correctness still requires evaluation, useful sources and fallback behavior.
Quantlix develops AI systems for prediction, language understanding and image analysis, with evaluation, human oversight and integration into business workflows.
Quantlix builds analytics pipelines and business intelligence tools that turn operational data into defined metrics, useful reports and decision support.
Quantlix builds generative AI workflows for drafting content and creating assets, with brand guidance, review controls and integration into publishing processes.
Let’s talk about what needs to change.
Bring your challenge, your questions and your starting point. We’ll work out the next step together.