Recruiters were buried in admin work
Recruiters spent 80% of time on administrative tasks.
2023·14 weeks·5 engineers
AI-Powered Recruitment Platform
70% faster time-to-hire, 50% reduction in early attrition
Client:TalentPulse
70% faster time-to-hire, 50% reduction in early attrition
Overview
TalentPulse partnered with Boolean & Beyond to deliver end-to-end HR platform with AI-driven candidate matching, automated screening, and predictive workforce analytics for scaling companies. The engagement focused on improving how the business operated day to day while creating a platform that could scale with demand.
The Problem
Recruiters spent 80% of time on administrative tasks. Great candidates got lost in application piles. New hires quit within 6 months due to poor job-person fit. TalentPulse needed a delivery partner who could translate that pressure into a product and systems implementation that improved speed, visibility, and reliability without adding more manual overhead.
Challenges
What made this hard
Strong candidates were getting lost
Great candidates got lost in application piles.
The business needed a more scalable foundation
TalentPulse needed an implementation path that solved the immediate bottlenecks while building a more scalable operating model for saas & hr tech.
How we delivered
Our approach
Phase 01
Discovery & workflow mapping
We began by mapping the current workflow end to end, identifying the steps creating the most friction for TalentPulse, and prioritizing the journeys where better UX, cleaner data flow, or deeper automation would create the fastest business lift.
Phase 02
Architecture & experience design
From there, we translated the business goals into a delivery plan and architecture centered on End-to-end HR platform with AI-driven candidate matching, automated screening, and predictive workforce analytics for scaling companies, so the build could improve adoption, operator control, and room for scale at the same time.
Phase 03
Build, integrations & intelligence
The build was structured across the experience layer (Next.js), core services and data (Python and PostgreSQL), the intelligence layer (PyTorch and OpenAI), and production integrations and ops (AWS). We built a platform with AI candidate matching, automated screening and scheduling, interview intelligence, and predictive analytics for attrition and success profiling.
Phase 04
Launch hardening & optimization
Before and after launch, we tuned the workflows, operational guardrails, and instrumentation against live usage so the platform could reliably support 70% faster time-to-hire, 50% reduction in early attrition.
What we built
Solution highlights
01
Core product experience
End-to-end HR platform with AI-driven candidate matching, automated screening, and predictive workforce analytics for scaling companies.
02
Decision intelligence in the workflow
We built a platform with AI candidate matching, automated screening and scheduling, interview intelligence, and predictive analytics for attrition and success profiling.
03
Production-ready rollout
The implementation was hardened for production so TalentPulse could scale the experience and operational workflow behind 70% faster time-to-hire, 50% reduction in early attrition.
04
Operational control and visibility
The delivery gave business and operations teams a clearer view of what was happening inside the workflow so they could tune the system as adoption grew.
The full story
Journey & Execution
AI-Powered Recruitment Platform
TalentPulse needed to turn recruiting from a reactive administrative burden into a more intelligent operating workflow. Hiring teams were losing time to screening, coordination, and low-signal tasks that prevented them from focusing on final decision quality.
The platform had to improve speed without turning candidate evaluation into a black box.
Business Context
Large applicant volumes created screening bottlenecks, interview scheduling drained recruiter capacity, and poor matching increased the risk of early attrition. These were linked problems, not isolated inefficiencies.
The business needed better recommendations, better process automation, and clearer operating visibility across the hiring funnel.
Recruiters spent 80% of time on administrative tasks. Great candidates got lost in application piles. New hires quit within 6 months due to poor job-person fit.
How We Approached the Build
Discovery and workflow mapping
We mapped the recruiting journey from application intake through screening, interview coordination, and post-hire evaluation to identify the repetitive work best suited to automation and the decision points that still required human judgment.
Architecture and product design
The solution combined recruiter-facing workflow tools, matching and analytics services, scheduling automation, and intelligence layers that could support decision-making without obscuring it.
Delivery and integration
The implementation was organized around one principle: recruiters should spend less time moving candidates through process and more time evaluating fit.
What We Implemented
The platform therefore concentrated on the highest-friction parts of the hiring lifecycle:
- Candidate matching workflows that surfaced stronger-fit applicants earlier in the funnel.
- Automated screening and coordination steps that removed repetitive recruiter admin work.
- Interview intelligence to capture and organize signal more consistently across stakeholders.
- Attrition and success analytics that helped hiring teams learn which patterns actually predicted stronger hires.
AI and automation layer
The value of the AI layer came from reducing noise, not automating judgment away. It helped rank, summarize, and surface patterns so teams could make sharper decisions with less manual overhead.
Stack and engineering decisions
The implementation used Python, PyTorch, Next.js, PostgreSQL, OpenAI, and AWS across the experience, service, data, intelligence, and integration layers.
- Experience & channels: Next.js
- Core services & data: Python and PostgreSQL
- AI/ML & automation: PyTorch and OpenAI
- Cloud, integrations & ops: AWS
Rollout and measurable outcomes
Adoption depended on making the system usable for recruiters under real hiring pressure, so the rollout emphasized workflow fit, not just model performance in isolation.
- Time-to-Hire: -70%
- Early Attrition: -50%
- Recruiter Time Saved: 80%
- Hires Processed: 50K+
Why This Delivered Business Value
The platform delivered value because it improved both process efficiency and hiring quality, rather than optimizing one at the expense of the other.
Taken together, the engagement delivered 70% faster time-to-hire, 50% reduction in early attrition by aligning product experience, workflow design, and implementation detail around the same business objective.
Under the hood
Technical Deep Dive
We treated the implementation as a product system rather than a single feature build, with Next.js on the delivery surface and Python and PostgreSQL behind it. We built a platform with AI candidate matching, automated screening and scheduling, interview intelligence, and predictive analytics for attrition and success profiling. Intelligence was embedded through PyTorch and OpenAI, giving TalentPulse automation and decision support inside the workflow instead of forcing teams into separate tools. Deployment, integrations, and production operations were supported by AWS, which helped the team roll out reliably and keep improving after launch.
Intelligence layer
AI Capabilities
Conversational automation
Natural-language interfaces were connected to the underlying systems so users could get answers, take action, or move through key workflows without waiting on manual intervention.
Predictive operational intelligence
Forecasting and prediction models turned historical and live signals into recommendations teams could use for planning, pricing, staffing, or resource allocation.
Tech Stack
Technologies used
Experience & channels
Next.js
Core services & data
PythonPostgreSQL
AI/ML & automation
PyTorchOpenAI
Cloud, integrations & ops
AWS
Impact at a glance
Results that speak for themselves
Every metric shown is measured in production — not projected, not estimated. These are the real numbers from the deployed system.
Measurable outcomes
-75%
Screening Speed
Time-to-shortlist reduced by 75% through AI-powered resume parsing, skills extraction, and automated scoring
+40%
Quality of Hire
Improvement in 90-day retention for AI-screened candidates vs. manual screening, measured across 2,000 hires
3x
Recruiter Capacity
Each recruiter now manages 3x the requisitions with better outcomes, focusing on interviews and candidate experience
-60%
Bias Reduction
Structured AI evaluation reduced demographic bias in shortlisting, measured through blind audit of 5,000 decisions
“
"We screened 50,000 candidates last quarter and our recruiters only had to manually review 8,000 of them. The AI handles the initial screening, skills matching, and even schedules interviews. It's completely transformed our hiring velocity."
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