Building a Production-Ready AI Classification Agent: Engineering Aura's Role Mapping System

From Workflow Pain to AI Innovation

When Aura Intelligence first integrated Anthropic's Claude AI into our workforce analytics platform, we achieved remarkable results: 94% classification accuracy and reduced processing time from months to minutes. But observing our customers, particularly consulting firms across our client base, revealed an unexpected workflow challenge.

Despite our high-accuracy classification system, users were still:

Downloading data from our platform
Manually remapping job titles using external LLM tools
Performing additional verification outside our system
Re-uploading results for their analyses

This represented a clear market opportunity. Our traditional batch ML approach, while accurate on our internal validation data, wasn't meeting the dynamic classification needs that varied dramatically across organizations. One client might categorize software architects under "Engineering," while another considers them part of "Technology Leadership."

We realized we needed to evolve beyond static AI integration to build a conversational AI Assistant that could learn classification rules from natural language interactions and apply them consistently across millions of job profiles within our existing platform workflow.

Batch ML to Conversational AI

Why Traditional Approaches Fall Short

Our original system excelled at consistency but struggled with adaptability. Static classification rules couldn't capture the nuanced requirements of different organizations without manual configuration for each use case, creating bottlenecks that forced users outside our platform.

The breakthrough insight: Users already knew how they wanted their data classified, they just needed a way to communicate that knowledge to our system naturally.

The Conversational Agent Approach

We designed an AI agent that could understand instructions like "Take everything that looks and feels like an account executive in financial firms and put them in wealth management" and apply these rules consistently across entire datasets.

This wasn't just a technical improvement, but a fundamental shift from static ML prediction to dynamic, interactive classification that adapts to organizational needs in real-time.

Intelligent Context Learning

One of our most sophisticated capabilities is the automatic extraction of classification rules from natural conversation. When a user says, "In our organization, anyone with 'architect' in their title should be classified as Engineering," our system:

Identifies the rule pattern through natural language processing
Assigns confidence scores based on language specificity
Persists the context throughout the conversation
Automatically applies the rule to all subsequent classifications

This transforms static classification into an interactive learning system that builds organizational knowledge through dialogue.

Flexible Processing for Different Workflows

Our system adapts to different user needs through dual processing modes:

Synchronous Processing

Immediate results for exploratory analysis
Perfect for interactive refinement and quick iterations
Real-time feedback for smaller datasets

Asynchronous Processing

Background processing for large-scale production workflows
Roughly 50% cost savings through batch API pricing
Designed for enterprise-scale classification jobs

The system automatically routes requests to the optimal processing mode based on dataset size and user requirements.

The Draft Workflow: What Makes It Different

Side-by-Side Editor Experience

AI-GIF-Clear

Our key differentiator is the draft workflow, which is integrated directly into our existing Role Management page. We created a side-by-side editor interface where:

Left panel: Traditional role configuration controls
Right panel: Conversational AI chat interface
Real-time updates: AI predictions appear instantly as they're generated

Users can watch classifications happen in real-time, refine instructions through natural language, and maintain full control over the final results.

Real-Time Collaboration with AI

The experience feels like pair programming with an AI colleague:

Draft Generation: AI creates initial classifications in temporary storage
Interactive Refinement: Users chat with the agent to adjust rules and edge cases
Live Preview: Real-time updates via Pusher WebSocket show progress
Final Application: Users approve and apply changes to permanent storage

This keeps the entire workflow within our platform while providing the flexibility users were seeking with external tools.

Quality and Control

The draft system ensures quality through multiple validation layers:

Schema validation for data integrity
Taxonomy validation against organizational rules
Confidence scoring for classification reliability
User review before permanent application

Users maintain complete control while benefiting from AI acceleration.

Results and Impact

The new system eliminated the inefficient download → manual remap → re-upload workflow entirely. Users now perform sophisticated role classification directly within our platform, with AI assistance that learns their preferences and applies them consistently. Beyond technical metrics, we've created a data flywheel. Every classification decision enhances our understanding of organizational role structures, allowing for more accurate baseline classifications for future customers.

Our AI Role Mapping system understands user workflows and builds systems that enhance human decision-making. By combining thoughtful architecture, rigorous evaluation, and deep integration with existing user workflows, we've created a foundation for the future of AI-powered workforce analytics. The conversational agent pattern has proven so valuable that it's becoming a cornerstone of how we approach AI product development across our platform.

As AI agents become more prevalent in enterprise software, we believe the most successful AI systems will be those that feel like natural extensions of how users already work, not replacements for their expertise.

The AI Assistant for Role Mapping is currently rolled out to a limited audience and will be available for all Aura users later in June.

Try our new AI Role Mapping feature in Aura today. Or if you’re not currently a user, request a demo of Aura here.

Authored by Mike and Robert from Aura's Engineering Team

By: Robert Pare

Robert Pare is a Machine Learning Engineer at Aura Intelligence, where he applies cutting-edge AI techniques to transform raw data into strategic insights. With a master’s in computer science from Carnegie Mellon and a dual bachelor’s in math and computer science from Dartmouth, Robert brings a rigorous, analytical edge to Aura’s most complex modeling challenges. He’s previously contributed to research at MIT Lincoln Lab and the Air Force Research Lab, and has industry experience spanning data science, software, and machine learning.