internal search

At Cashcrown, we build it as a RAG pipeline: documents are indexed locally (BGE-M3 embeddings on our own hardware), the user query goes to a router that selects the relevant fragments, and the response includes the path or title of the source document. PII is masked before any data leaves for the cloud model; sensitive datasets are processed locally. The search engine respects your existing access policies—it doesn’t replace them.

Permission scope is configured jointly before deployment. We start with a pilot on a single document set with a measurable test question before expanding the index to other areas.

How much does this service cost, and when does it pay off?#

We work in tiers based on the number of indexed datasets and the chosen hosting model. We start with a pilot on one dataset so you can see the value before a larger investment. When employees stop wasting time manually searching documentation, the return comes quickly. Calculate the exact ROI in the ROI calculator. We don’t provide upfront pricing because costs depend on document volume and access requirements.

Does it integrate with our systems and comply with GDPR?#

Yes. We connect to SharePoint, Confluence, databases, and other sources via controlled connectors. PII is masked before leaving for the model, and sensitive datasets are processed locally (zero data sent to the cloud). Indexing and search are designed from the ground up to comply with GDPR and AI Act. Access to new datasets always requires human approval.

Where do we start?#

With a review of available sources, permission mapping, and selecting one pilot dataset with a clear test question. We demonstrate working search with citations before requesting broader access, then gradually expand the index. More on the approach: how to choose the first process.