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Recruitment AgencyReference Solution

TalentMatch AI

AI candidate screening, automated resume parsing, and personalized outbound sequencing.

PythonClaude 3.5 SonnetPostgreSQLNext.jsFastAPI

Project Specifications

Duration

3 Months

Team Size

2 Developers

Launched

2025 Launch

🔵

Status

Reference Solution

🖥 Product Gallery
Applicant matching scorecard panel
Applicant matching scorecard panel
Screen 1 of 2

Applicant matching scorecard panel

Case Study

The Narrative

An executive deep dive tracing operational objectives, challenge landscapes, and delivery outcomes.

IndustryRecruitment Agency
Timeline3 Months
Team Size2 Developers
Launch Year2025 Launch
Status🔵 Reference Solution

01 / Project Overview

TalentMatch AI accelerates applicant screening by parsing resumes, comparing applicant skills against job criteria via semantic search, and writing tailored email sequences for recruiters.

02 / Business Challenge

Recruiting specialists spent hours manually reviewing resumes and drafting emails for developer roles, slowing down hiring pipelines.

03 / Objectives

Accelerate candidate resume screening and initial recruiter outbound scheduling, cutting profile review times by 80% and tripling sourcing pipeline capacities.

04 / Our AI Solution

We built a candidate sourcing application featuring vector databases, matching algorithms, and automated email generation tools aligned with recruiters' hiring preferences.

05 / Implementation Journey

We created a candidate database index using pgvector, enabling recruiters to query applicant resumes using semantic terms. The app leverages Claude to draft tailored outbound copy based on applicant history.

06 / Business Outcome

Reduced initial screening review times from 2 minutes to 15 seconds per profile, while doubling response rates through personalized cold outreach messaging.

Engineered Value

Core System Features

Custom engineered software nodes mapping specific operational problems to engineering resolutions.

Vector Resume Matcher

Performs semantic analysis to score resumes against requirements beyond keyword matches.

Business ValueSaves 2 minutes per profile by scoring resumes semantically against job details.

AI Outreach Writer

Drafts email copy tailored to the candidate's professional achievements and work history.

Business ValueDoubles outreach conversion rates with personalized outbound email drafts.

CRM Lead Synchronizer

Connects with ATS systems (Greenhouse, Bullhorn) to update candidate stages.

Business ValueKeeps candidate status aligned by syncing automatically with Bullhorn and Greenhouse.
System Execution

System Architecture

Data pipeline flow maps showing inputs translating down through vector stores and execution API layers.

1

Ingestion API Node

Converts PDF resumes into raw text and runs layout-aware cleanup scripts.

2

Semantic Matching Engine

Calculates similarity scores using pgvector embeddings and criteria scoring rules.

3

Recruiter Interface Dashboard

A Next.js panel where team members inspect candidate scorecards and edit emails.

Project Outcome

Results & Impact

Measurable performance metrics and operational throughput scaling indicators registered post-launch.

Reference Solution Benchmark

Reduced initial screening review times from 2 minutes to 15 seconds per profile, while doubling response rates through personalized cold outreach messaging.

OUTCOME LEDGER: PSX-TALE-2026ACTIVE SYSTEM
80%Review Time Saved
✓Saved 50 Recruiter Hours/Month
3xSourcing Pipeline Capacity
94%Placement Accuracy Score

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AI Advantage

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