How Artico uses Ema to reduce hiring cost by 30% and increase hiring efficiency by 67%

Introduction

Artico Search is a retained executive search firm that partners with CEOs, boards, and investors to hire C-level and VP scaleup talent. The firm serves high-growth, private equity and enterprise clients across technology and adjacent sectors, with specialty practices spanning product, engineering, go-to-market, through to CEO and board.Artico sought to shorten leadership hiring cycles while maintaining their rigorous standards of judgment. By adopting Ema’s Recruiter, the firm re-architected its sourcing and evaluation workflow expanding candidate coverage, standardizing assessments, and freeing recruiters from manual tasks so they could focus on high-value conversations. The result: faster and more precise hiring at significantly lower cost.

Problem

Executive searches require both scale and judgment. Artico’s team faced four structural constraints:

1. Fragmented data and shallow profiles

Valuable leadership profiles were spread across internal systems and public sources. Pulling them together was expensive, was fraught with human error and was largely manual. Even once assembled, profiles often lacked critical insights such as social media presence, recent news coverage, industry context, and company data, limiting recruiters’ ability to accurately and assess candidates.

2. Insufficient lead volume via market tools

Market tools like LinkedIn Recruiter produced far more leads, but quality was low. Recruiters spent far too much time filtering mismatched irrelevant profiles rather than engaging top talent.

3. Slow, inconsistent evaluations

Long-form scorecards took hours to generate for each candidate, let alone the volume needed to create a full slate, and were hard to standardize. This further stretched already limited recruiter capacity.

4. Lack of Targeted Search

Recruiters couldn’t easily define target companies using critical filters such as last funding round, revenue, or employee size making it difficult to zero in on the right-fit candidates. Popular market tools lacked these capabilities, forcing recruiters to rely on broad, unfocused searches that missed many qualified prospects and miss categorized others.

All these resulted in extended search cycles, higher cost-per-hire, and frustration for recruiters, clients, and leadership candidates alike.

Solution

Artico implemented Ema Recruiter end-to-end rebuilding sourcing, evaluation, and ranking around four pillars:

• Broadened sourcing that preserves precision
  1. Natural-language search sits alongside structured filters, so recruiters can think in concepts (e.g., “find CFOs from fintech companies with headquarters in the U.S. with revenue over $300M”) and let the system extract filters automatically.
  2. Title expansion covers synonyms, vertical step-ups (e.g., Sr. Director → VP), and lateral equivalents (e.g., GM, Chief Product & Technology Officer for CTO searches) to avoid missed talent.
  3. Dual data lanes are needed for a private leadership dataset and public profiles, presented in two tabs. Shortlisted candidates in one tab are hidden in the other to avoid noise and duplication.
• Rich, dynamic candidate profiles at scale
  1. Ema leverages proprietary and third-party data sources to build a dataset of ~700M profiles worldwide covering broader professional and demographic attributes than LinkedIn and giving recruiters unmatched reach.
  2. Each profile combines multiple signals: social presence (Twitter/X, GitHub, blogs, academic pages), real-time news on career moves, restructurings, funding rounds, or growth announcements, and deep company data such as domain, size, industry, revenue, growth rate, and funding history. Inferred attributes like seniority level and years of experience further accelerate fit assessment.
  3. Updates flow continuously from public data refreshes and verified company sources, often making Ema more current and complementary to LinkedIn. The result is a holistic, always-fresh view of candidates that surfaces top talent the moment they are most open to opportunities.
• Lightweight, role-specific candidate scorecards in seconds
  1. Candidate pools can scale into the thousands, but aggregate scorecards quickly narrow them into a focused working set (e.g., ~200 candidates for rapid triage), reducing cognitive load while keeping full coverage accessible.
  2. For each role, a lightweight scorecard is automatically generated from the job description. Candidates are scored on a 0–5 scale against defined criteria, with clear explanations for no match, partial fit, and strong match. Results are available in seconds, with candidates highlighted through progress indicators and sorted by aggregate score.
  3. This design enables recruiters to evaluate well beyond the top 500 candidates, ensuring broader coverage without sacrificing speed or judgment.
• Continuous learning and feedback loops
  1. Recruiter actions, from profile edits to shortlist decisions, feed back into the system. These feedback loops continuously refine future searches, aligning results with Artico’s definition of excellence and steadily improving slate quality over time.

Results

The transformation was immediate and measurable:

• 67% reduction in time-to-hire: Faster candidate discovery and automated evaluation collapsed sourcing timelines.
• 30% lower cost-per-hire: Expanded sourcing via cost-efficient data providers reduced overall search expenses.
• Improved candidate quality: Recruiters presented higher-fit candidates earlier in the process, validated by a 30% rise in hiring manager satisfaction.
• Scalable advantage: With AI handling sourcing, enrichment, and initial assessments, recruiters shifted focus to high-value work with relationship building, candidate calibration, and client advisory.

Conclusion

Ema helped Artico do more with stronger, evidence-based assessment. It widened the search, scored candidates clearly, and learned from recruiter feedback. The result was faster placements and better-fit leaders.

For enterprises, the lesson is to use AI to move from reactive recruiting to true agentic AI talent management, and a steady, data-driven system that gets stronger over time.