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Notionalize

Quantum Club case study

How Quantum Club cut recruitment cycle time in half with one connected workflow.

Notionalize built a recruitment application connecting Airtable, Slack, email automation, AI-powered candidate matching, and automated follow-ups. The delivery record reports 50% faster hiring, 45% higher candidate satisfaction, and 18 hours of manual work eliminated each week.

AI implementation

50%

Faster hiring

+45%

Candidate satisfaction

18 hrs/wk

Manual work eliminated

Evidence boundary

What this engagement proves—and what it does not

A useful case study should make the evidence easier to evaluate, not stretch one implementation into every adjacent claim.

Supported by this record

  • A manual recruitment process can be connected across Airtable, Slack, and email instead of leaving candidate data in separate threads and spreadsheets.
  • AI was applied to one bounded candidate-matching workflow inside a broader recruitment application.
  • Candidate follow-ups and pipeline visibility were included in the delivered system.
  • The recorded engagement includes hiring-speed, candidate-satisfaction, and manual-work metrics.

Not claimed from this record

  • A prior formal Fractional AI Officer, Chief AI Officer, or Head of AI employment title.
  • Model-fairness, bias, legal-compliance, or human-oversight outcomes that are not documented in the delivery record.
  • A general-purpose AI platform, enterprise AI-governance program, or guarantee about hiring quality.
  • Independently audited results or a universal outcome another recruitment team should expect.

Evidence source: the named client, implementation scope, and figures above come from Notionalize's first-party delivery record. The metrics have not been represented as independently audited benchmarks.

Implementation record

The challenge, system, and confirmed change

The challenge

Recruitment was entirely manual with candidate data spread across spreadsheets and email threads. No tracking, no analytics.

The system delivered

A custom recruitment app connecting Airtable, Slack, and email automation — with AI-powered candidate matching and automated follow-ups.

The confirmed result

Cut recruitment cycle time in half, improved candidate experience scores by 45%, and unlocked full pipeline visibility.

What was built

One recruitment workflow across data, communication, matching, and follow-up

The delivery record is strongest as bounded AI implementation proof: matching, follow-up, data, and communication were connected inside one recruitment workflow rather than presented as a general AI transformation.

AirtableSlackEmail automationAI candidate matching
01

Recruitment workflow application

02

Candidate pipeline

03

AI-powered matching flow

04

Automated follow-up flow

Before and after

The observable operating change

Candidate data was spread across spreadsheets and email threads.

One recruitment application connected Airtable, Slack, and email.

The team had no recruitment tracking or pipeline analytics.

The delivered workflow made the full candidate pipeline visible.

Recruitment and candidate follow-up were entirely manual.

The delivery record reports 18 hours of manual work eliminated each week.

Choose the right scope

A similar symptom can require a different kind of partner

Use the underlying decision—not the most fashionable title—to choose the engagement.

Bounded AI implementation

Use this path when one workflow has a named owner, inputs, outputs, users, acceptance criteria, and a handoff boundary.

Explore AI implementation

Ongoing business-systems ownership

Use this path when the recruitment workflow is one part of a larger SaaS, data, integration, vendor, and documentation environment.

Explore strategic IT partnership

Executive technology decisions

Use this path when the primary need is a technology roadmap, architecture choice, vendor boundary, or delivery-risk decision across the organization.

Explore Fractional CTO support

Have one AI workflow that needs to become an operating system?

Bring the workflow, current tools, data constraints, and accountable owner. The first call will identify whether the next step is feasibility, implementation, leadership, specialist review, or no build yet.

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