Looproof

Data-Driven Reference Check SaaS

Role	Co-Founder, Product Manager (2-person team)
Context	B2B HRTech Venture (Looproof)
Stage	Validation & Enterprise Partnership
Time	2024.11 – 2025.06

Summary	Structured reference-check SaaS turning subjective peer feedback into comparable hiring signals.
Scope	Generated 2,000+ B2B leads and secured an official cooperation letter from NICE Information Service.
Outcome	2,000+ B2B leads / NICE official cooperation letter
Tags	Product, B2B SaaS, HRTech, Validation

Problem

Capability thesis	Combined B2B outbound automation, structured evaluation design, and enterprise channel strategy to validate an HRTech SaaS from zero.
TL;DR	Redesigned reference checking from subjective phone conversations into a structured, comparable data report—acquiring 2,000+ B2B leads via automated outbound and securing an official cooperation letter (LOI) from NICE Information Service.
What	A B2B reference-check platform that collects multi-rater behavioral evidence and normalizes rater leniency so hiring managers can compare candidates objectively.
Why now	As experienced hiring expanded in Korea, companies relied heavily on reference checks, yet unstructured phone calls produced biased rumors, slow turnaround, and legal/privacy risks.
Problem	Traditional reference checks suffer from three structural flaws: (1) rater bias (some referees score everyone generously while others are harsh), (2) vague open-ended comments that cannot be compared across candidates, and (3) high manual coordination cost for HR teams.

Role & ownership

Role	Co-Founder, Product Manager
Team	2-person founding team (1 Product/GTM Lead, 1 Engineer)
Ownership	Owned problem definition, reference-check rubric and report architecture, B2B outbound automation pipeline, HR buyer interviews, and enterprise partnership negotiations with NICE.
Constraints	Zero marketing budget, high switching inertia among conservative HR teams, and strict candidate consent / personal data protection requirements.

Validation loops

Supported

Loop 1 — Zero-Budget B2B Lead Engine & Problem Discovery

We can reach HR decision-makers at scale without paid ads by crawling active job postings and automating personalized cold outreach.

Discover

Why	Find companies actively hiring experienced talent who currently feel reference-check friction.
Actions	Built a Python crawler to collect companies with active mid-to-senior job openings and public HR contact channels.
Outputs	Compiled a verified database of 2,000+ B2B leads.

Run

Why	Book discovery interviews and test problem resonance with HR managers.
Actions	Executed segmented cold email sequences focused on hiring failure cost and reference bias.
Outputs	Secured direct discovery calls with startup and mid-market HR leads.

Review

Why	Synthesize what HR teams actually disliked about existing reference check tools.
Actions	Analyzed interview transcripts across HR managers and recruiters.
Outputs	Confirmed that existing tools produced uniform 'all-positive' responses that failed to predict post-hire performance risks.

Supported

Loop 2 — Structured Rubric & Discrepancy Report Prototype

Replacing open-ended praise with forced-tradeoff behavioral questions and automatic interview follow-up prompts will make reference reports actionable.

Define

Why	Prevent referees from giving generic 5/5 ratings across every dimension.
Actions	Designed behavior-based questions, rater-leniency adjustments, and cross-referee variance indicators.
Outputs	Created the Looproof Report specification.

Design & Build

Why	Give hiring managers an immediately usable decision artifact.
Actions	Built the web prototype that turns multi-rater inputs into a comparative dashboard plus 'Questions to ask in the final interview'.
Outputs	Interactive Looproof SaaS prototype.

Review

Why	Evaluate buyer willingness to adopt a new standalone tool.
Actions	Demoed the report to HR teams and enterprise stakeholders.
Outputs	Product quality was praised, but standalone startup procurement faced privacy-audit friction and episodic purchasing.

Supported

Loop 3 — Enterprise Channel Pivot (NICE Information Service)

Partnering with an established background/credit verification enterprise can solve trust and distribution simultaneously.

Discover

Why	Identify incumbent players who already have enterprise HR relationships but lack modern qualitative reference-check software.
Actions	Mapped the Korean background-verification ecosystem and approached NICE Information Service.
Outputs	Identified synergy between NICE's quantitative background checks and Looproof's structured reference analytics.

Run

Why	Validate enterprise partnership viability.
Actions	Pitched the joint product architecture and co-business model to NICE's new-business division.
Outputs	Secured an official Letter of Cooperation from NICE Information Service.

Review

Why	Decide whether to commit to a multi-year enterprise integration cycle.
Actions	Evaluated founding team alignment, enterprise sales lead times, and opportunity cost.
Outputs	Made a disciplined decision to conclude the venture after documenting the validated B2B2B model.

Design decisions

Problem zoom	Identified that HR teams do not need 'more text comments'—they need a calibrated signal that separates candidate capability from referee generosity and highlights specific verification questions for the final interview.
Options	(1) Concierge phone-call agency, (2) Generic survey form tool, (3) Structured rubric SaaS with automated follow-up interview prompts and enterprise credit-bureau distribution.
Trade-off	Selling standalone SaaS directly to SMBs had a long, seasonal sales cycle tied to hiring volume; partnering with an established verification provider (NICE) traded standalone branding for immediate enterprise trust and distribution.
Final rules	Standardize reference inputs into behavior-anchored scales, auto-generate custom follow-up interview questions wherever referees disagree, and pursue B2B2B distribution.
Tools	Python, Playwright, Notion, Figma, TypeScript
Frameworks	Behavioral Anchored Rating Scales (BARS), Outbound Funnel Automation, B2B2B Channel Validation

Outcomes

Metrics	Built a 2,000+ B2B HR lead pipeline via custom crawling and outbound automation; conducted in-depth validation with HR leads and secured an official cooperation letter from NICE Information Service.
Qualitative outcome	Validated that enterprise buyers valued structured discrepancy detection and interview-question generation far more than raw reference summaries.
Social proof	Official Letter of Intent / Cooperation from NICE Information Service (Korea's largest corporate & credit information provider).

Learnings

Learnings	In HRTech, trust and compliance infrastructure are as decisive as product UX—leveraging an incumbent's distribution channel can bypass years of cold enterprise trust-building.
If again	Test B2B2B API/channel unit economics and revenue-share terms before building a full standalone self-serve dashboard.

Evidence

Before	Unstructured phone reference checks taking days of HR coordination and yielding subjective, non-comparable notes.
After	Calibrated multi-rater reference report with discrepancy flags and tailored final-interview verification questions, backed by a NICE cooperation agreement.