Gatien
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Gatien
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RAG
RAG is useful when a team wastes time searching documents, procedures, contracts, tickets, or knowledge bases. The value does not come from the model alone. It comes from source quality, permissions, and tests that prove the answers hold up on your real questions.
What can be built
Every project starts from a real workflow, real examples, and an output the team can check.
Answer internal questions with excerpts, sources, and warnings when the information is missing.
Find the relevant documents even when the user does not know the exact wording.
Structure the useful documents, exclude the noise, and track updates.
Create a set of expected questions and answers to measure reliability before rollout.
Use cases
These examples are a starting point. The right scope depends on your data, your users, and the business risk.
Answer from policies, guides, SOPs, HR documents, or technical procedures.
Help teams find the right answers in the product documentation.
Prepare consistent answers from product sheets, offers, and internal notes.
Explore contracts, reports, PDFs, and archives without rereading everything by hand.
How a mission runs
Clarify the business problem, the users, the available data, the constraints, and the expected result.
Build a first testable version on real examples, without overbuilding the architecture.
Check answers, errors, edge cases, permissions, and how much human review is needed.
Connect the tool to existing systems: internal app, CRM, Slack, Teams, email, document store, or API.
Hand over a maintainable base: code, configuration, known limits, success criteria, and next steps.
Stack
Deliverables
Scope
FAQ
Have a document base that is hard to use? Start with a limited corpus and 10 real questions.
Contact GatienNo. It reduces some risk by providing sources, but you still need to test answers, handle missing documents, and force the assistant to say when it does not know.
The ones that are consulted often, kept up to date, owned by a business stakeholder, and useful to a precise use case.
Often yes for semantic search, but the choice depends on volume, permissions, cost, and the existing architecture.
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