AI CTRL Standard Data Connectors for RAG

Overview: What Is RAG-Enabled Searching?

Retrieval-Augmented Generation (RAG) is the technology that powers AI CTRL's ability to search, interpret, and respond based on your organization's own documents and data. Rather than relying solely on an AI model's general knowledge, RAG connects the AI to your actual content, pulling the most relevant documents in real time to generate accurate, context-aware, organization-specific answers.

When a user asks a question, RAG:

1. Interprets the meaning and intent behind the query

2. Searches your connected document repositories for the most semantically relevant content

3. Delivers an AI-generated response grounded in your actual data, with source references for verification

This makes RAG ideal for document-level knowledge retrieval with unstructured, text-heavy content, such as documents, policies, knowledge articles, and written procedures. Its semantic search engine interprets meaning within natural language, surfacing relevant context even when users don't know exactly what to search for or how a document is worded.

Conversely, RAG is a poor fit for structured data such as spreadsheets, databases, or formatted tables, where the value lies in rows, columns, and computed relationships rather than narrative content. Attempting to use RAG against structured data typically yields incomplete, misrepresented, or unreliable results, as the AI is retrieving text fragments rather than processing the data as a whole.

What RAG Is and Is Not

Important Distinction: RAG is not a BI tool. It is designed for conversational document search and retrieval, interpreting meaning within content-rich files. For large-scale data aggregation, numerical analytics, or enterprise reporting, purpose-built BI platforms remain the appropriate solution.

Standard Data Connectors

Standard Data Connectors are RAG-enabled integrations to common fileshare and collaboration platforms. These connectors allow Expedient's AI CTRL platform to ingest, index, and semantically search your organization's documents stored in these systems [1].

Configuration & Integration Requirements

Client-Side Responsibilities

Establishing a Standard Data Connector requires configuration actions on the client (your organization's) side. Expedient's role is to configure and maintain the connector infrastructure, however, the client is responsible for creating, provisioning, and maintaining the access credentials and permissions that allow Expedient to connect to your environment.

This includes, but is not limited to:

• Creating a dedicated application or service account within your platform (e.g., an OAuth App Registration in Microsoft Azure for SharePoint Online, a service account in Google Workspace, or an IAM role policy in AWS for S3)

• Granting the appropriate read-level permissions to allow the connector to access the designated file repositories or sites

• Providing Expedient with the required credentials (e.g., Client ID, Tenant ID, certificate/key pair, or API token) via a secure delivery method

• Maintaining those credentials over time — including renewing certificates, rotating secrets before expiration, and updating Expedient when access configurations change

Ownership Note: The client owns and is responsible for the server-side access configuration. Expedient will provide guidance and documentation for each connector type, but the provisioning and ongoing maintenance of connector credentials within the client's environment is the client's responsibility. Failure to maintain valid credentials will interrupt data ingestion and may result in stale or incomplete search results.

Integration Timeline

For a standard integration involving 50,000 documents or fewer, clients should expect a typical turnaround of 5 business days from the time valid credentials and access are confirmed. This timeline covers:

• Connector configuration and authentication setup

• Initial data ingestion and indexing

• Validation and search testing

Timelines may vary based on document volume, file type complexity, permission scope, and credential readiness. Delays in provisioning client-side access are the most common cause of extended timelines.

Upon connector activation, an initial Full Content Sync is performed, collecting, extracting, and vectorizing all content made available to the data connector. Depending on the volume of documents in scope, this process can take anywhere from several hours to several days to complete. Once the Full Content Sync is finished, the connector transitions to an Incremental Sync schedule, running once daily to capture only the delta changes, such as new documents added, existing documents modified, and content removed, keeping the indexed data set current without re-processing the entire repository.

The Value of a Well-Curated Data Set

The quality of your RAG-enabled search experience is directly tied to the quality of the data connected to it. A well-curated document repository amplifies every capability the AI brings to the table.

Benefits of Strong Data Curation

• Higher Relevance, Faster Answers — When documents are organized, titled clearly, and contain purposeful content, the AI retrieves more precise results with fewer irrelevant sources returned alongside them.

• Confident, Trustworthy Responses — Clean, accurate, and up-to-date documents produce grounded AI answers that users can act on. Source verification becomes more meaningful when the sources themselves are reliable.

• Reduced Noise in Multi-Document Synthesis — RAG often synthesizes answers across multiple documents simultaneously. Curated repositories ensure those documents are complementary and consistent rather than contradictory or redundant.

• Better Contextual Understanding — Descriptive file names, logical folder structures, and consistent terminology help the semantic search engine identify and surface the right content, even when users phrase queries differently than the document language.

• Longer-Term ROI — A well-maintained knowledge repository grows in value over time, becoming an organizational asset that continues to improve AI-assisted workflows.

Warning: Data Quality Directly Impacts Performance and Accuracy

RAG is only as good as the data behind it.

When the connected data set is poorly curated, containing outdated files, duplicate content, vague or misleading document titles, structured data, or large volumes of irrelevant material, search performance and AI response accuracy will degrade accordingly.

Common curation problems and their impacts include:

Custom Data Connectors

Not every organization stores its knowledge in a standard fileshare platform. For data sources not listed among the Standard Data Connectors above, Expedient offers Custom Data Connector development, as purpose-built integrations designed to connect AI CTRL's RAG capabilities to virtually any content source with an accessible API or data interface.

Custom connectors follow the same RAG-focused principles as standard connectors: the goal is to collect, extract, and vectorize unstructured, text-heavy content so it can be semantically searched and retrieved in response to natural language queries. Use cases may include proprietary internal platforms, industry-specific applications, legacy document management systems, or any other content repository outside the standard connector library.

Because custom connectors require scoping, development, and ongoing maintenance beyond standard offerings, additional charges and fees may apply, both for the initial build and for continued support of the connection over time. All custom connector engagements are initiated through a Statement of Work (SOW) process, which defines the data source, access requirements, expected document scope, and desired RAG outcomes before development begins.

To explore a custom data connector, clients should contact their Expedient Account Manager. The account manager will lead the exploratory phase, assess feasibility, and coordinate the SOW process with the appropriate Expedient teams.