Gen AI & Data Engineering Workshop [Part-4]: Background Checks – AI Agent & Conversational Analytics

The Problem Worth Solving

Background verification is essential, but the process behind it often feels mechanical. Checking candidate records, tracking order statuses, and validating reports across multiple systems demands a lot of manual navigation.

The team saw a clear gap: users (especially HR and operations teams) spend time performing repetitive tasks like logging into portals, finding specific orders, or checking the status of a background check. These are structured activities that still depend on human clicks and database lookups.

The challenge was simple to state but complex to execute: How can GenAI understand what users want in plain language and orchestrate the backend actions required to deliver those answers, instantly?

That question became the foundation for their definition. And cheers to the amazing team that made this happen.

The Idea: AI Agent and Conversational Analytics for Background Checks

The team broke their idea into two connected parts:

How the Team Built an AI Agent for Background Checks?

The team began by defining the backend architecture that could support such natural interactions without compromising data security or scalability.

Initially, they connected a .NET API directly with the database through a Large Language Model (LLM). After testing and feedback, they refined the approach using Tool Calling with MCP (Model Context Protocol), which makes the entire process modular and controlled.

Here’s how the system flows:

1️⃣ User Query: The user types a natural-language question in the chat interface.

2️⃣ LLM Interpretation: Using GPT-4o-mini, the model identifies intent and determines which “tool” should handle the query (e.g., Order Status or Candidate Info).

3️⃣ Tool Execution: The LLM extracts required parameters and routes them to the .NET API.

4️⃣ Business Layer Processing: The API connects with the business service layer, which interacts with the PostgreSQL database.

5️⃣ Data Return and Transformation: Results come back as structured JSON.

6️⃣ Response Generation: The LLM converts the data into a human-readable, contextual answer for the user.

This architecture ensures that the LLM never directly interacts with the database, which helps maintain data control while enabling flexibility and scalability.

A live demo during the workshop made the concept tangible – the team asked, “Which orders are in status review needed?” and within seconds, the agent produced a clean, human-like response, displaying the relevant orders seamlessly.

The Importance of Agentic AI in Background Checks

This prototype shows how Agentic AI can elevate traditional background screening systems. By integrating conversational intelligence within enterprise data workflows, teams and clients can:

✔️ Access information instantly through natural language

✔️ Scale to new data models without rewriting queries

✔️ Enable non-technical users to gain insights directly

✔️ Extend capabilities with prompt engineering as “new code”

In an industry where precision, compliance, and efficiency define trust, such intelligent automation has the potential to simplify how background screening products operate.