AIKIT SOLUTIONS
AI Knowledge Systems & RAG Solutions
Retrieve the right passage, answer from that passage, show the source, and stop when the files do not support a claim.
What this service is
RAG development here means: ingest PDFs and docs, chunk them, store embeddings, run vector search, then optionally wrap hits in a short answer.
The same index can power an AI knowledge base search box or an AI document assistant. Access control belongs in production, not as an afterthought.
Problems it solves
Keyword search that misses a paragraph because the user asked with different words.
Chat answers with no file behind them, and stale indexes after a handbook update.
Keyword search vs semantic search
Normal keyword search looks for matching words. It is fast and exact on names, SKUs, and error codes.
Semantic / vector search looks for meaning. “How long can I return this?” can match a “refund window” section even if those words never appear in the query.
What I build
- Document retrieval
- Embeddings
- Vector search
- Semantic search
- Source citations
- Internal knowledge bases
- PDFs and documentation
- Confidence handling
- Human escalation
- Access-controlled knowledge
Common use cases
- Internal handbook Q&A
- Policy lookup with citations
- Search across mixed document sets
- Re-index after a documentation release
How the workflow works
Files are ingested and chunked. Each chunk gets an embedding. A question is embedded too; nearest chunks are returned.
An assistant may summarize those chunks and must show the source. Low similarity or missing ACL → no answer, or escalate.
Assistants that sit on this index are described under AI Assistants. Smart Document Search and AI Customer Support are DEMO / CONCEPT views of the same idea.
When this is useful
Useful when the answers already live in documents and people waste time hunting them — not when there is no written source of truth.
FAQ
Is this search, a chatbot, or both?
The same document index can serve a search box that returns passages and an assistant that wraps those passages in a short answer with sources. You choose the interface; the retrieval layer is shared. Demos on this site illustrate both patterns with mock files.
How are private documents handled?
A production system can enforce access control by user or role so people only retrieve what they are allowed to see. Authentication and audit belong in that design. Demos here do not connect to private stores.
Do we still need keyword search?
IDs, error codes, and exact titles often work better as keywords. Semantic search helps when the question uses different words from the document. Hybrid search is a practical default: keywords for precision, vectors for meaning.
How do document updates reach the index?
Changed files can be re-ingested, chunks replaced, and stale vectors removed on a schedule or on publish. An index that never refreshes will keep answering from last month’s PDF. Update jobs are part of a real knowledge system, not an afterthought.
Why show sources with answers?
Sources let a person verify the passage and see when retrieval found nothing useful. They also make evaluation possible: you can check whether the cited file actually supports the reply. Fluent text without a source is harder to trust.
What does a confidence score mean?
Treat the score as a threshold you tune, not as a legal probability. Low similarity can block an answer or hand the question to a person. The threshold is a product rule, not a guarantee that the model is “sure.”