Case study | Technology
Turning Recruiting Data Into Compensation Intelligence
Overview
A go-to-market recruitment firm had built up years of compensation and hiring data through candidate engagement, recruiter activity, and placements. Much of that knowledge still required a recruiter to interpret it.
10Pearls built a compensation engine that lets employers and candidates explore market data by role, experience, location, industry, and company stage. Instead of relying on a broad salary average, users can see a benchmark tailored to the hiring decision or career question in front of them.
recorded by the engine
represented in usage
per employer user on average
Challenge
The firm’s clients and candidates needed more than a static salary guide. Pay varies with experience, geography, industry, and company stage; employers also need to understand how a target salary relates to the available talent pool.
Making the data self-service brought another challenge. A narrow search might have too few records for a useful benchmark, while a broad one could mix recent activity with older or unusual values. The firm needed results users could understand and trust, delivered quickly without slowing its core recruiting platform.
Solution
10Pearls designed and delivered a cloud-based compensation intelligence platform centered on Amazon Elasticsearch Service, now Amazon OpenSearch Service. The platform’s architecture used a dedicated search and analytics layer on AWS, separate from the firm’s core transactional workloads, which allowed it to filter and aggregate compensation data across job function, experience, location, industry, and company stage. Users can combine those factors in one search to get a more relevant market view.
To keep that view current, Amazon SQS carries data updates to downstream processing, while AWS Lambda helps prepare and refresh the searchable data. The engine applies rules that favor recent, relevant records, limit the effect of outliers, and expand the eligible data set when a highly specific search needs more coverage.
Users can explore compensation at the 10th, 25th, 50th, 75th, and 90th percentiles. Depending on the view, they can also see base pay, variable compensation, on-target earnings, equity guidance, and candidate availability. The experience shows the type and volume of data behind a result, while Amazon Cognito supports secure, role-aware access for employers and candidates.
Results & benefits
The engine has recorded 74,900+ requests, including 17,800+ from client users and 57,000+ from candidates. Candidate requests account for about 76% of the total, showing use on both sides of the talent market.
The client requests came from 1,255 users across 1,128 employer accounts—an average of 14 requests per client user. Searches covered 179 role and experience combinations. Together, those figures show broad reach and use across varied compensation questions, though they do not measure time saved or hiring outcomes.
Employers can explore pay alongside talent availability in a single workflow. Candidates can compare compensation against a market view tailored to their circumstances. For the firm, recruiting data has become a customer-facing product rather than information available only through manual research.
About us
Customer
- Data engineering
- Data analytics
- Product development
- Cloud engineering
- Amazon OpenSearch Service (formerly Amazon Elasticsearch Service)
- AWS Lambda
- Amazon SQS
- Amazon Cognito
Related case studies
Energy
AI-Powered Transformer Inventory Recommendations
Case study | Energy Accelerating Inventory Decisions with AI-Powered Recommendations Improving inventory matching with intelligent recommendations that evaluate inventory, apply […]
Energy
Enterprise Knowledge & AI Search Solution
Case study | Energy Centralising Enterprise Knowledge with AI Search Streamlining access to critical enterprise information by unifying fragmented information into […]
Energy
AI-Assisted Transformer Inspection Solution
Case study | Energy Modernising Transformer Inspections with AI-Assisted Data Capture Replacing manual evaluations with a digital inspection platform that […]
Energy
LiDAR Transformer Assessment Solution
Case study | Energy LiDAR and Digital Imaging for Transformer Assessments Digitising transformer assessments to accelerate field inspections, improve measurement consistency, […]
Energy
Automated Transformer Drawing Generation
Case study | Energy Accelerating Asset Readiness with Automated Drawing Generation Automating transformer drawing generation to reduce manual effort and deliver […]
Energy
AI-Powered Transformer Operations Transformation
Case study | Energy Building a Digital Foundation for AI-Powered Operations Delivering a connected set of custom AI solutions across the […]
Energy
Transforming RFQ Configuration & Quoting with AI
Case study | Energy Transforming RFQ Configuration and Quoting with AI Automating complex RFQs to accelerate quoting, reduce configuration errors, and […]
Energy
AI-Driven Inventory Matching for Smarter Operations
A leading electrical power and distribution provider faced inefficiencies in manually matching transformer orders to available inventory. 10Pearls developed an AI-powered application that automated order matching.
Energy
EV Charging Management Cloud Platform
A cloud-native EV charging platform scaled faster, improved reliability, and delivered the real-time performance needed to support growing demand.
Energy
Unifying Energy Data With AWS Analytics
Unified AWS analytics gave a renewable energy leader real-time portfolio insights, automated data pipelines, and faster decision-making at scale.