Build an HR Team-Matching Agent Using MongoDB Vector Search & AI

Imagine instantly assembling the perfect project team, matching skills, and boosting productivity. This article shows you how to build an HR team-matching agent using MongoDB Vector Search and Vercel AI SDK, streamlining team creation for better results.

Ditch the Resume Scans: Embrace Smart Team Building

Traditional HR team building is slow and often misses hidden talent. Resumes don't always capture true skills or compatibility. Stop manually sifting through spreadsheets, and start leveraging AI.

Why Traditional AI Falls Short in HR

Workflow-based AI struggles with the nuances of team building because:

• Skills are complex: Thousands of combinations exist, each with varying experience levels.
• Team dynamics matter: Interpersonal relationships impact project success.
• Needs evolve: Project requirements and employee availability constantly change.

Agentic AI + MongoDB Vector Search: A Winning HR Combination

Imagine describing project needs in plain language and having an AI assistant that:

• Understands the meaning behind your requirements.
• Intelligently searches your talent database.
• Reasons through complex team compositions.
• Provides recommendations with justifications.
• Saves chosen teams for easy tracking.

Together, Vercel AI SDK and MongoDB Vector Search transform HR team assembly.

How This Article Will Transform Your HR Workflow

In this guide, you'll learn to:

• Represent employee skills as vector embeddings.
• Craft powerful searches using MongoDB Atlas Vector Search.
• Implement agentic patterns using Vercel AI SDK.
• Construct a database-backed system for team suggestions.
• Create a management interface for tracking and approving teams.

Time to revolutionize your team-building process.

Beyond Keywords: Understanding Skill Semantics with Vector Embeddings

Traditional keyword matching misses important nuances like:

• Synonyms: Recognizing "ML" and "Machine Learning" as the same skill.
• Context: Knowing if "Python" is for data science or web development.
• Relatedness: Understanding how React experience translates to Vue proficiency.
• Expertise: Differentiating "familiar with Python" from "Python expert."

Vector embeddings solve these problems by grouping semantically similar skills, and are revolutionizing skill assessment in HR.

Generate Skill Embeddings with Voyage AI (Now Part of MongoDB!)

Voyage AI, now part of MongoDB, excels at capturing semantic relationships between skills. This means MongoDB's AI-powered search and retrieval capabilities are built directly into the database.

MongoDB Data Models & Vector Search Setup

Before diving into implementation, let’s clarify our MongoDB schema and vector search:

Database Schemas

export interface Employee {
  _id?: ObjectId;
  name: string;
  title: string;
  department: string;
  email: string;
  hireDate: Date;
  availability: number; // percentage available for new projects
  skills: Skill [];
  performance: {
    rating: number; // 1-5 scale
    teamworkScore: number; // 1-5 scale
    lastReviewDate: Date;
  };
  pastProjects: {
    projectId: ObjectId;
    role: string;
    contribution: string;
  }[];
  teamHistory: ObjectId []; // IDs of employees they've worked with
  embedding: number []; // Combined skills embedding at the top level
}

export interface Team {
  _id?: ObjectId;
  projectTitle: string;
  projectDescription: string;
  createdAt: Date;
  members: {
    name: string;
    role: string;
    keySkills: string [];
    justification: string;
  }[];
  skillCoverage: number;
  overallRationale: string;
  risks: string [];
  mitigationStrategies: string [];
  status: 'proposed' | 'approved' | 'active' | 'completed';
}

Setting up MongoDB Atlas Vector Search

Create a vector search index in MongoDB Atlas on the "employees" collection:

{
  "fields": [
    {
      "type": "vector",
      "path": "embedding",
      "numDimensions": 1024,
      "similarity": "cosine"
    }
  ]
}

With the index set up, perform vector searches to find employees.

Agentic HR: Why Vercel AI SDK Changes the Game

Vercel AI SDK's agentic capabilities let AI break down problems and make iterative decisions. Traditional AI follows preset paths, but agentic AI adapts like an experienced HR pro.

How Vercel's Agentic Pattern Works

1. HR manager submits a project description.
2. AI analyzes the request and identifies needed info.
3. AI calls a tool (e.g., employee search).
4. AI receives results and decides next steps.
5. Steps 3-4 repeat until sufficient data is gathered.
6. AI delivers a clear recommendation.

Implementing Team Analysis Logic

Before tools, implement core logic for analyzing team compositions:

• calculateSkillCoverage
• calculateTeamDiversity
• analyzeCollaborationHistory
• calculateTeamScore

Defining Your HR Agent Tools

Create specialized tools your HR agent needs:

• analyzeProjectRequirements
• searchEmployeesBySkill
• analyzeTeamComposition
• saveTeamToDatabase
• generateTeamRecommendation