Search finds documents. Your team needs answers.
An enterprise RAG system lets people ask questions in plain language and get answers drawn from your own policies, contracts, SOPs, and customer context — with the source attached.
CarbonSilicon Labs builds RAG systems that cite their sources, respect who is allowed to see what, and stay current as the documents change.
Enterprise RAG turns scattered company knowledge into answers employees can rely on. Every engagement answers:
Clean ingestion from documents, policies, contracts, tickets, and the systems knowledge lives in.
The pipeline that finds the right passages, not just the plausible ones.
Every answer traceable back to the source it came from.
Access controls so people only see what they are cleared to see.
Re-indexing so answers reflect the latest version, not last quarter's.
Tests that measure accuracy and catch regressions before users do.
We pick the knowledge and the audience worth serving first.
We choose the sources, retrieval approach, permissions, and citation model.
We ingest the sources and engineer the retrieval and answer pipeline.
We launch with permissions, citations, and evaluation in place.
We monitor accuracy, refresh sources, and add knowledge as trust grows.
Retrieval-augmented generation over your own documents: instead of answering from model memory, the system retrieves the relevant passages from your policies, contracts, and SOPs and answers from them, with citations and permissions.
A normal chatbot answers from what the model learned in training. RAG grounds every answer in retrieved source documents, so it reflects your actual policies and current information — and can show where the answer came from.
Policies, SOPs, contracts, customer context, internal documentation, and tickets — whatever knowledge lives in your systems. We connect the sources that matter and handle their formats during ingestion.
Through retrieval quality, source ranking, freshness, and evaluation. We tune what gets retrieved, keep the index current, and run tests that measure accuracy and catch regressions before users hit them.
Yes — citations are core to the design. Every answer is traceable back to the document it came from, so people can verify it rather than trust a black box.
Access controls are enforced at retrieval, so each user only sees answers drawn from documents they are cleared to access. Someone in support cannot pull an answer out of a document only legal should see.
Stale data, poor retrieval, missing permissions, and weak evaluation. Most failures trace back to the system answering from the wrong or outdated passages — which is exactly what the retrieval, freshness, and evaluation work prevents.
As often as the underlying documents change. We set up re-indexing so answers reflect the latest version, not last quarter's, with the cadence tuned to how fast each source moves.
Search returns a list of links and leaves you to read them. RAG returns a synthesized, cited answer to your actual question. Search finds documents; RAG gives answers.
When off-the-shelf search cannot meet your accuracy, permissions, or governance needs — usually when answers must be trustworthy, access-controlled, and auditable. If the knowledge is core to how your team works, a custom system is worth it.