HRTech · Labor Market Analytics · AI Agent

AI agent for US labor market analytics platform

This HRTech product helped organizations make better talent and workforce decisions using labor market data. The platform supported analysis of salary benchmarks, talent demand, candidate availability, location insights, market trends, and recruiting intelligence for the US labor market.

I worked on the design of the existing analytics platform, including data-heavy dashboards, reporting workflows, labor market views, and interfaces that helped HR and talent acquisition teams turn complex data into actionable insights. I also created an AI agent from scratch within the product ecosystem — covering UX, scenarios, branding, visual language, animation, and interaction behavior.

Role

Senior Product Designer

Domain

HRTech / Labor Market Analytics

Platform

Web

Team

PM, developers,business analyst

Scope

Analytics platform, dashboards, reports, data visualization, AI agent, branding, scenarios, animation

Product Context

A data platform for workforce planning and talent acquisition decisions

The platform helped HR and talent acquisition teams understand labor market conditions before making hiring decisions. Users could explore salary ranges, talent supply, talent demand, location differences, job market trends, skills data, and recruiting opportunities across the US market.

This kind of product sits between analytics and decision support. Users were not just looking at charts — they needed to understand what the data meant for hiring strategy, sourcing plans, compensation decisions, and workforce planning. The interface had to make complex labor data readable and useful for analysts who needed detail, recruiters who needed quick answers, TA leaders who needed strategic insight, and stakeholders who needed shareable reports.

The platform connected labor market signals — salary, demand, availability, location — into one structured analytics environment.

Users & Workflows

Different users needed different levels of labor market insight

The product served several user groups across HR, recruiting, analytics, and business decision-making. Each group approached the data differently: some needed detailed exploration, while others needed quick summaries, comparisons, and reports.

HR leaders

Needed strategic views into workforce planning, salary benchmarks, market competitiveness, and hiring feasibility.

Talent acquisition leaders

Needed insight into talent demand, candidate availability, location strategy, and recruiting market conditions.

Recruiters and sourcers

Needed practical data for finding talent, understanding market supply, and supporting sourcing decisions.

Recruitment consultants

Needed credible reports and visualizations to advise clients and explain labor market conditions.

Analysts

Needed deeper access to data, filters, comparisons, trends, and structured outputs for further use.

Business stakeholders

Needed clear summaries, shareable PDFs, and decision-ready insights without getting lost in raw data.

Platform Design

Making labor market data understandable and decision-ready

The platform design work focused on turning complex labor market data into structured, readable, and shareable insight. The product needed to support both detailed analysis and fast decision-making across different user types and workflows.

Analytics dashboards

I designed dashboard structures that helped users understand key labor market signals: talent demand, salary benchmarks, candidate availability, market competitiveness, and location-based differences. The dashboards had to balance density with clarity — showing enough data to be useful without overwhelming users who needed to make fast decisions.

Dashboard structures surfaced key labor market signals without requiring users to dig through raw data manually.

Report generation workflows

The platform supported labor market reports and shareable outputs. I worked on flows that helped users move from data exploration to report creation — structured summaries, filtered views, and presentation-ready materials that recruiting consultants and TA leaders could share with clients and stakeholders.

Report generation flows connected analytics exploration to shareable, stakeholder-ready outputs.

Market comparison views

Users needed to compare locations, roles, talent availability, salary ranges, and demand signals side by side. I designed comparison structures that made differences easier to scan, interpret, and explain — helping TA leaders build location strategies and recruiters understand where the market was more or less competitive.

Comparison views helped users understand location and role differences without manually reconciling separate data sources.

Data visualization and hierarchy

Labor market data is dense. I worked on visual hierarchy, charts, tables, filters, and information architecture so users could read the data without losing context. The goal was to make it easy to move between a broad market view and a specific detail — without the interface requiring users to rebuild their mental model every time.

Clear visual hierarchy and progressive data depth helped users stay oriented across complex datasets.

AI Agent

Creating an AI agent from scratch inside the analytics ecosystem

In addition to the core platform, I created an AI agent from scratch. The agent was designed to help users interact with labor market data in a more guided and conversational way: asking questions, generating summaries, exploring market conditions, and getting faster access to insights without relying only on manual filtering and dashboard exploration.

AI agent response states and interaction logic

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Agent positioning and branding

I worked on how the AI agent should feel as a product character: useful, professional, analytical, and trustworthy. This included visual style, tone, and how the agent fit into the broader HRTech platform brand.

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Conversation scenarios

I designed scenarios for how users could ask questions about labor market data, salary benchmarks, talent demand, location comparisons, and report summaries — moving from query to insight through conversation.

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Interaction logic and states

The agent needed clear states: idle, listening, generating, responding, error, empty state, and follow-up suggestions — so users always understood what the agent was doing and what to do next.

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Animation and microinteractions

I designed animation behavior and microinteractions to make the AI agent feel responsive and intentional — active without becoming distracting or overly playful within an analytics product context.

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Insight workflow integration

The AI agent was designed to support the analytics workflow — helping users move from a question to a useful labor market insight, summary, or next step rather than functioning as a standalone chatbot.

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Visual language

I created a visual language for the agent that felt distinct enough to be recognizable but coherent within the platform design system — consistent in color, type, motion, and tone.

Process

How I worked

Understand

Studied the product domain, labor market analytics use cases, HR and talent acquisition workflows, and platform requirements.

Structure

Mapped user flows, dashboard logic, reporting workflows, data hierarchy, filters, and core analytics scenarios for different user types.

Design

Created platform screens for dashboards, reports, comparisons, visualizations, and data-heavy workflows across the talent intelligence product.

Agent concept

Defined the AI agent's role, branding, interaction model, conversation scenarios, and behavior inside the platform ecosystem.

Prototype

Designed high-fidelity screens, agent states, animation behavior, microinteractions, and platform touchpoints across the full product experience.

Present

Prepared demos, presented design decisions, and collaborated with PM, developers, data specialists, and stakeholders throughout the process.

Result Design

A clearer analytics experience with an AI layer for faster insight

The final work contributed to the design of a labor market analytics platform that helped users explore workforce data, understand market conditions, compare locations, review salary benchmarks, generate reports, and communicate findings to stakeholders.

The AI agent added a new layer to the product experience. It gave users a more guided way to ask questions, summarize insights, and move through data-heavy workflows without relying only on manual filtering and dashboard exploration.

The platform combined structured analytics with an AI layer — giving different users the level of guidance and depth they needed.

Outcomes

Product work across analytics, reporting, and AI-assisted insight

Analytics platform

Designed data-heavy interfaces for labor market analysis, workforce intelligence, and talent acquisition decision-making across multiple user types.

Labor market reports

Worked on reporting workflows, shareable outputs, summaries, and report-oriented product views for recruiters, consultants, and stakeholders.

Data visualization

Designed charts, tables, filters, hierarchy, and comparison views that made complex labor market data readable and interpretable.

User workflows

Supported HR leaders, TA teams, recruiters, consultants, analysts, and business stakeholders with different levels of insight and access.

AI agent from scratch

Created the agent's UX, branding, scenarios, interaction logic, response states, animation, and platform integration as a full product experience.

Decision support

Helped turn raw labor data into clearer insights for hiring strategy, compensation benchmarking, sourcing decisions, and workforce planning.

Reflection

In analytics products, AI is useful only when it understands the workflow

This project showed that analytics UX is not just about showing more data. HR and talent acquisition users need to understand what the data means and how it affects hiring decisions, workforce planning, compensation strategy, and recruiting priorities.

Designing the platform meant creating structure around complex labor market signals: salary benchmarks, talent demand, candidate availability, location comparisons, and report generation. The interface had to help people move from "what is the data?" to "what does this mean for my hiring plan?" — without losing the detail that analysts and consultants needed.

The AI agent reinforced an important lesson: AI should not sit outside the product as a decorative chatbot. It has to be embedded in the user's workflow, understand the data context, and help people move from question to insight faster. Branding and animation matter too — an agent that feels trustworthy and consistent with the product is one users will actually return to.