Case study

Vultus Recruit

Cloud-based recruiting platform combining ATS + sourcing + job board distribution + high-scale outreach.

B2B SaaSHRTechATSScaleAI

TL;DR

What it is: A cloud-based recruiting platform combining ATS + sourcing + job board distribution + high-scale outreach.
My role: First product at Vultus - owned end-to-end product strategy, workflow design, prioritization, and delivery, and built key technical components (resume parsing + scale systems).
Outcome: Launched publicly in Q2 2015, grew rapidly; scaled outreach from ~10M to ~250M emails/year, scaled the platform to handle 40M+ resumes, achieved 95% resume parse accuracy, and sustained 96% email delivery rate.

Context

Recruiting teams were dealing with fragmented workflows: Jobs posted in one place, candidates managed in another. Too much manual movement between job boards, inboxes, spreadsheets, and ATS tools. Outreach at scale was hard to manage and even harder to measure.

We built Vultus Recruit as the core system that unifies sourcing, candidate management, and outreach - fast for daily recruiting, but structured for analytics, collaboration, and scale.

Launch

Vultus Recruit was the first product we built at Vultus. We released it publicly in Q2 2015 and iterated rapidly based on real recruiter usage patterns.

What the product does

Candidate intake + database

Centralized candidate profiles (resume, contact details, history, notes)
A structured system to build and maintain a searchable candidate pool
Scaled to handle 40M+ resumes (ingestion, storage, retrieval, and search)

Job management + job board distribution

Create and manage job openings
Post jobs to external job boards
Candidate search integration with job boards (for sourcing and matching)

Pipeline / candidate tracking (ATS)

Track candidates through stages (screening → shortlist → interview → offer)
Recruiter collaboration on candidates (notes, tags, sharing)

Outreach and mass mailing

A major adoption driver
Share job openings and candidate pools via email
Run high-volume campaigns to shortlisted pools
Track outcomes (deliveries, bounces, engagement signals)

Feature highlights

Core (what most teams use daily)

Jobs + pipeline: create jobs and move candidates through stages (screen → shortlist → interview → offer)
Job board posting: publish jobs to job boards and track applicants
Candidate database: centralized, structured candidate profiles (scaled to 40M+ resumes)
AI resume parsing: 95% accuracy, improves over time through learning/feedback
Search + filters: fast candidate search with advanced filters (skills, titles, experience, location, keywords)
Hotlists / pools: group and shortlist candidates quickly
Collaboration: notes, tags, and activity history tied to each candidate/job

Advanced (scale, reliability, and control)

Mass mailing at scale: outreach from pools/hotlists (scaled 10M → 250M emails/year)
Deliverability + tracking: bounce handling, unsubscribe compliance, engagement signals; 96% delivery rate
Recruiting analytics: dashboards for pipeline health, conversion, and recruiter activity
Access control: role-based permissions to protect sensitive candidate data
Integrations: job board sourcing + ingestion workflows into the candidate database

Core workflow

We launched with the workflows recruiters do daily:

1. Candidate tracking (ATS basics)

2. Job posting to job boards

3. Candidate search integration with job boards

4. Mass mailing to share jobs + candidate pools

This created a simple loop: Source → Store → Search → Shortlist → Outreach → Track → Improve pool

Key decisions

Build 'workflow-first', not 'feature-first'

It's tempting to expand features early. We prioritized the end-to-end workflow (job → candidate → pipeline → outreach) before breadth.

Outcome: Faster time-to-value, stronger adoption, and a clearer roadmap for expansion.

Make outreach a first-class product surface (not a bolt-on)

Outreach could have been 'just email.' We treated it as a product surface: pools/hotlists, templates, tracking, unsubscribe handling, and hygiene rules.

Outcome: It became a core usage driver and scaled into high-volume recruiting campaigns with strong delivery performance.

Treat parsing as a learning system, not a one-time extractor

Traditional parsers are brittle as formats change. I built an AI-based resume parser designed to keep improving through learning/feedback.

Outcome: High-quality structured data at scale (95% accuracy) → better searchability and recruiter outcomes, especially with 40M+ resumes.

Impact

Outreach Scale

10M → 250M emails/year

With deliverability tracking and list hygiene built into the workflow

Email Delivery Rate

96%

Sustained at scale

Resume Database

40M+ resumes

Scaled system for ingestion, storage, retrieval, and search

Parse Accuracy

95%

AI-based resume parser with continuous learning

Technical depth

Designed and scaled the candidate data layer for 40M+ resumes with reliable ingestion and fast retrieval.
Built and improved AI resume parsing (95% accuracy) and structured profile creation.
Scaled high-volume outreach while maintaining 96% delivery rate, with tracking and hygiene.
Partnered closely with engineering to turn recruiter workflows into systems that are simple to use but measurable and reliable.

What I learned

Workflow clarity beats feature count - recruiters adopt what saves time immediately.
Data quality is product quality - parsing + normalization drives search success.
Scale forces discipline - deliverability, reliability, and performance become product features.

Collaboration

Engineering (architecture, performance, delivery, scalability)
Design (workflow UX, information hierarchy, usability)
Sales & Customer Success (feedback loops, onboarding, adoption)
Operations/Deliverability stakeholders (email quality, hygiene, compliance)

Testimonial

“Rupak led the product vision and decisions that transformed our hiring workflows and materially improved onboarding.”

— CEO, Vultus, Executive

See resume →