AI Solutions · RAG and knowledge search
RAG and knowledge search with answers you can check.
We build search that reads your policies, manuals and records, then answers with citations. If the answer is not in your documents, it says so.
In short
What is retrieval-augmented generation, or RAG?
Retrieval-augmented generation, or RAG, is a method where an AI model first finds relevant passages in your documents, then writes an answer based only on them. technocity builds RAG knowledge search for staff and customers, with source citations, document permissions, regular re-indexing and testing against real questions.
Sound familiar?
The problems we solve
Answers are buried across drives, wikis and old emails
New staff ask the same questions senior staff answer
Keyword search misses documents that use other words
Generic AI tools answer confidently without sources
What’s included
How we can help
Internal knowledge assistant
Search across policies, procedures and manuals for staff, with cited answers.
Customer help search
Public help search that answers from your published guides and product pages.
Permission-aware search
Search that only returns documents each user is allowed to see, using your existing access rules.
Index care and quality review
Scheduled re-indexing, broken source checks and review of unanswered questions.
Timelines are typical starting points. Every project is scoped with you first.
What you get
- A search interface or chat panel for your users
- Connectors to your document sources
- Answers with links to the exact source passages
- Access controls matched to your user roles
- A test set of real questions with expected sources
- A report of unanswered questions to fill content gaps
Tools and technology
How it works
From first call to results
Audit the sources
We list where your knowledge lives, which documents are current and who may see them.
Prepare and index
We clean, split and index documents so the right passage can be found quickly.
Test retrieval first
We check that search finds the right passages before judging the written answers.
Launch and keep fresh
We schedule re-indexing and watch unanswered questions to show where content is missing.
Questions
Common questions
What is RAG in AI?
RAG, or retrieval-augmented generation, is a method where an AI system searches your documents for relevant passages, then writes an answer using only those passages. It keeps answers tied to your own information and lets users check the sources behind each answer.
Does RAG stop AI hallucinations?
RAG reduces AI hallucinations but does not remove them completely. Answers are much more reliable when the model must cite retrieved passages and is told to admit when it cannot find an answer. We test with real questions and show sources so users can verify.
Can AI search respect document permissions?
Yes, AI search can respect document permissions if access rules are applied at retrieval time. Each user only receives answers built from documents they are allowed to open. We map permissions from your existing systems, such as Google Drive or SharePoint.
Which documents can a RAG system read?
A RAG system can read most text-based documents, including PDFs, Word files, web pages, wikis, help desk articles and spreadsheets. Scanned documents need text recognition first. Tables, images and handwritten notes may need extra processing to be searched reliably.
Do you need to train an AI model on our documents?
No, RAG does not require training a model on your documents. Your documents are indexed separately and only relevant passages are sent to the model when a question is asked. This keeps your content easier to update, remove and control.
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