Production AISystems
Knowledge and Automation

Enterprise AI Search

Enterprise AI search that connects your documents, SharePoint, Drive, databases, CRM and support systems into one searchable knowledge layer. Users get ranked results and cited answers, not just links. Permission-aware retrieval ensures people only see what they are allowed to access.

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What does enterprise AI search do?

Enterprise AI search connects to your document repositories, databases and business systems, indexes their content with metadata and permissions, and lets users search across all of them from one interface. Results are ranked by relevance, filtered by the user's access rights and presented with citations to the source document. Unlike traditional enterprise search, AI search can also generate a synthesised answer from the top results, so users get a direct response with sources rather than a list of links to read through. It is measured on search success rate, citation coverage and retrieval latency.

Knowledge is scattered across too many systems

SharePoint, Drive, CRM, ticketing, wikis. Employees search one system at a time and miss relevant content in others.

Search returns links, not answers

Traditional search gives a list of documents. Users still have to open and read each one. AI search gives a direct answer with sources.

Permissions are not enforced in search results

Sensitive documents appear in results for users who should not see them. Permission-aware retrieval fixes this.

Search does not understand business language

Generic search does not know your domain terms, product names or internal acronyms. AI search is tuned to your vocabulary.

How This Solution Can Be Built

Different situations call for different implementations. These are the most common variants.

Document search

Search across SharePoint, Drive, Confluence, wikis and internal document stores with full-text and semantic retrieval.

Database and CRM search

Search structured data in your CRM, ticketing and business systems alongside unstructured documents.

Cited answer search

Instead of returning links, the system generates a synthesised answer with citations to the source documents.

Permission-filtered search

Results are filtered by the user's role and access rights at query time. No unauthorised content is returned.

How the System Works

From input to output, each step is engineered for a specific purpose.

1

User query

Employee or customer enters a search query

input
2

Query understanding

System rewrites and expands the query for better retrieval

ai
3

Source selection

Relevant connected sources are searched based on the query and user permissions

data
4

Hybrid retrieval

Keyword and semantic search run in parallel across selected sources

data
5

Permission filtering

Results are filtered by the user's access rights before ranking

control
6

Ranking and reranking

Results are ranked by relevance and reranked for the user's context

ai
7

Answer synthesis

A cited answer is generated from the top results, with links to the source documents

output
8

Feedback

Click-through, dwell time and explicit feedback improve ranking over time

feedback

Production and Enterprise Readiness

What makes this solution work in a real operating environment—not just in a demo.

Permission-aware retrieval

Document-level and passage-level permissions are enforced at query time. No unauthorised content is returned.

Source connectors

SharePoint, Google Drive, Confluence, Notion, S3, CRM, ticketing and custom APIs. New connectors are built as needed.

Freshness

The index is updated through managed ingestion. When a document changes, the search reflects it.

Citations

Every answer links back to its source document. Users can verify and trust the response.

Deployment

Cloud, private cloud or on-premises. Sensitive documents never leave your environment if policy requires.

How Success Is Measured

The right metrics depend on the solution. These are the measures that matter for this system.

Illustrative system view — metrics shown in the dashboard above are labelled examples, not client results.

Search success

Percentage of queries that result in a useful answer or click-through to the right document

Citation coverage

Percentage of answers that include valid, traceable citations to source documents

Permission compliance

Percentage of results that correctly respect the user's access rights—should be 100%

Retrieval latency

Time from query to results, measured at P50 and P95

Unanswered queries

Percentage of queries that return no useful result, flagged for knowledge gap review

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Frequently Asked Questions

Which document systems can it search?
SharePoint, Google Drive, Confluence, Notion, OneDrive, S3, internal wikis, CRM knowledge bases and custom APIs. If your content lives in a system that is not listed, a custom connector is built. The search retrieves from wherever your knowledge actually is.
How are permissions handled?
Document-level and passage-level permissions are stored as metadata during ingestion. At query time, results are filtered by the user's role, department and access rights before ranking. A user never sees a document they are not authorised to access, even if it is semantically relevant to their query.
Does it return answers or just links?
Both. The system returns ranked results with links to source documents, and it can also generate a synthesised answer from the top results with citations. The answer mode is useful for questions; the results mode is useful for exploration. Users can switch between them.
How does it handle our internal terminology?
The system is tuned to your domain vocabulary during indexing. Synonyms, acronyms and product names are mapped so that a search for one term retrieves content that uses related terms. This is configured during setup and refined based on search analytics.
Can it search structured data in our CRM?
Yes. The system can search structured data in your CRM, ticketing and business systems alongside unstructured documents. A query like 'open tickets for Acme Corp' retrieves both the relevant ticket records and any related documents.
How is the index kept up to date?
The ingestion pipeline monitors connected sources for changes. When a document is added, modified or removed, the index is updated through managed ingestion. Freshness rules can be configured per source—some sources may need near-real-time updates, while others can be refreshed daily.