--- title: "AI CTRL Standard Data Connectors for RAG" slug: "ai-ctrl-standard-data-connectors-for-rag" updated: 2026-07-31T13:54:11Z published: 2026-07-31T13:54:11Z canonical: "kb.expedient.com/ai-ctrl-standard-data-connectors-for-rag" --- > ## Documentation Index > Fetch the complete documentation index at: https://kb.expedient.com/llms.txt > Use this file to discover all available pages before exploring further. # AI CTRL Standard Data Connectors for RAG ## Supported platforms | Connector | Provider | |---|---| | SharePoint (Cloud / Online) | Microsoft | | OneDrive (Enterprise) | Microsoft | | Google Drive | Google | | Dropbox | Dropbox, Inc. | | Box | Box, Inc. | | Confluence (Cloud) | Atlassian | | ServiceNow | ServiceNow, Inc. | | Notion | Notion Labs, Inc. | | Amazon S3 | Amazon Web Services | If your content lives somewhere else, see [Custom Data Connectors](#custom-data-connectors) below. --- ## What a connector does A data connector indexes the documents in a system you already use, so AI CTRL can search them by meaning rather than by keyword and answer questions from them — with citations back to the original file. That makes connectors a good fit for text: policies, procedures, knowledge articles, contracts, written guidance. Someone can ask about "escalating an angry customer" and find the right document even if it never uses that phrase. They're a poor fit for structured data. Spreadsheets and databases carry their value in rows, columns, and calculations, and retrieval pulls text fragments rather than reading the file as a whole — so the answers come back incomplete or wrong. | Good fit | Not a fit | |---|---| | Natural-language questions against documents | Analytics across large datasets | | Policy, procedure, and knowledge lookup | Aggregating or computing metrics over many rows | | Summarizing across several documents at once | Replacing SQL or reporting tools | | Answers with sources you can check | Real-time transactional processing | | Follow-up questions on the same content | Dashboards and trend visualization | **This is not a BI tool.** For reporting and numerical analysis, a purpose-built BI platform is still the right answer. --- ## What we need from you Expedient configures and maintains the connector. Your organization creates and maintains the access it uses. - **A dedicated application or service account** in your platform — an OAuth app registration in Azure for SharePoint Online, a service account in Google Workspace, an IAM role policy in AWS for S3 - **Read-level permissions** to the repositories or sites you want indexed - **The credentials themselves**, sent by a secure method — client ID, tenant ID, certificate or key pair, API token - **Ongoing maintenance** — renewing certificates, rotating secrets before they expire, and telling us when access changes > **Credentials that lapse stop the sync.** The connector doesn't fail loudly; it stops collecting, and search results quietly go stale. Expiring secrets are worth tracking on your side. --- ## Timeline For a standard integration of 50,000 documents or fewer, expect **5 business days from the point valid credentials are confirmed** — not from the date of the request. That covers configuration and authentication, the first ingest, and search validation. Larger volumes, unusual file types, and complex permission scopes all extend it. The most common cause of delay by a wide margin is waiting on client-side access. Once the connector goes live it runs a **full content sync**, collecting and indexing everything available to it. Depending on volume that takes hours to days. After that it moves to an **incremental sync** once daily, picking up only what's changed — new files, edits, and deletions — rather than reprocessing the whole repository. --- ## Keeping results accurate RAG is only as good as the documents behind it. A connected repository full of stale and duplicated files will produce stale and contradictory answers, and it will do so confidently. | Problem in the source | What it does to answers | |---|---| | Outdated or superseded documents | Obsolete information gets presented as current | | Duplicates and near-duplicates | Conflicting answers, and less reason to trust any of them | | Vague or generic file names | Harder to rank the right document first | | Large volumes of irrelevant content | More noise retrieved, less precision | | Inconsistent terminology | Queries don't match the language of the documents | | Missing documents | Gaps show up as hedged or incomplete answers | The fix is unglamorous and it works: point the connector at content that's already curated, and review it periodically to remove what's gone stale. --- ## Custom Data Connectors For content that isn't on the supported list, Expedient builds custom connectors against most sources with an accessible API — proprietary internal platforms, industry-specific applications, legacy document management systems. The principle is the same as a standard connector: collect text-heavy content, index it, make it searchable by meaning. The difference is scope. Custom connectors require design, development, and ongoing maintenance, so **additional fees may apply** for both the build and continued support. Every custom connector starts with a **Statement of Work** defining the source, access requirements, document volume, and what you expect to get out of it. **To start, contact your Expedient Account Manager.** They'll assess feasibility and coordinate the SOW.