Ground AI in your own knowledge
A foundation model on its own is a generalist: confident, articulate, and frequently wrong about your business. Retrieval-Augmented Generation (RAG) fixes that by retrieving the right context from your own systems and giving the model the source material it needs to answer accurately, with citations.
Appoly designs and builds production RAG systems, not weekend prototypes. That means real document pipelines, real evaluation, real cost control, and real maintenance once it's live.
What a production RAG system actually needs
- Robust ingestion: PDFs, Word, Confluence, SharePoint, Slack, Notion, databases.
- Smart chunking and embedding: tuned per content type, not one-size-fits-all.
- Hybrid retrieval: semantic + keyword + metadata filters.
- Reranking so the model sees the right snippets, not just the closest ones.
- Source citations built into the response, so users can verify.
- Evaluation harness: measurable accuracy against a question/answer test set.
- Refresh pipelines: updates to source content propagate to the index automatically.
Where it's most powerful
Customer support knowledge bases, internal policy and procedure assistants, sales enablement, compliance lookups, technical documentation Q&A.
