---
title: "NOVA: Building Agentic Workflows for Structured Data Intelligence at Nexla"
---

**Date**: *April 10, 2025*

<div class="blog-authors">
  <p class="authors">Speaker:</p>
  <div class="card-group">
    <div class="card">
      <div class="col card">
          <div class="img-placeholder"> <img nozoom="" src="https://github.com/Noel1997.png" /> </div>
          <div>
            <p class="name">Noel Nebu Panicker</p>
            <p>AI Engineer at Nexla</p>
          </div>
      </div>
    </div>
  </div>
</div>

## Watch the Talk

<div class="user-story-youtube-container">
  <iframe
    class="w-full aspect-video rounded-md"
    src="https://www.youtube.com/embed/djUFEs3fM5s?si=3q9WOZZH---IBLMu"
    title="YouTube video player"
    frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
    allowfullscreen
  ></iframe>
</div>

## Talk Recap

In this talk, Noel introduced **NOVA**, Nexla's AI-powered co-pilot designed to address one of the toughest challenges in enterprise environments: **integrating scattered data across diverse tools, systems, and formats**.

Built on an **agentic foundation powered by AG2**, NOVA simplifies how both technical and non-technical users can build and manage complex data workflows.

## Solving Enterprise Data Complexity with NOVA
A central concept behind NOVA is the need to **ground AI outputs in real, production-grade data**. Noel explained how Nexla’s **metadata intelligence layer** powers NOVA with rich contextual awareness — including field types, sample values, and data relationships — reducing hallucinations and enabling **safe, data-aware code generation** and seamless API integrations.

## Intelligent Data Mapping & Extraction
NOVA also functions as an **intelligent translator**, capable of:

- Mapping messy, inconsistent inputs into structured, enterprise-standard models.
- Accelerating **data onboarding and transformation processes**.
- Extracting structured information from unstructured sources like **emails and scanned documents** using models such as **Pixtral** and **GPT-4**.

## How NOVA Operates
Noel shared a breakdown of NOVA’s **agentic workflow architecture**:

- **Initializer Agent**: Sets up the transformation requests.
- **Planner Agent**: Converts user intent into a series of actionable tasks.
- **Query Interpreter Agent**: Maps these tasks to specific data transformations.
- **Structured Extraction Workflows**: Extract structured data from unstructured inputs.

The system is **metadata-driven**, supports **real-time updates through Server-Sent Events (SSE)**, and is designed for **explainability, control, and traceability**.

## Real-World Impact
NOVA has already demonstrated significant value:

- 📈 **10% increase** in pipeline creation rates since launch.
- ⏳ **Reduction in partner onboarding time** from 6 weeks to 2 days.
- 🔒 Enabled the creation of **secure, governance-friendly private data marketplaces** for non-technical users.

These outcomes point toward a promising future for **AI-native, data-driven enterprise operations**.

## Discover More

Curious about how all of this comes together? The full user story is captured in the [Unlocking the Power of Agentic Workflows at Nexla with AG2](/docs/user-stories/2025-02-11-NOVA/nova) section of this document. Be sure to check it out.
