The enterprise friction point
Strata Talent received over 8,000 applicant resumes monthly for specialized engineering positions. Recruiters spent 30+ hours weekly manually sifting CVs, conducting repetitive initial screening phone calls, and calibrating compensation benchmarks. Qualified candidates frequently accepted competing offers while waiting up to 14 days for initial interview scheduling.
Transformation objectives
- Automate semantic resume parsing and candidate-job calibration scoring.
- Conduct conversational asynchronous preliminary interviews 24/7.
- Eliminate demographic bias through blind skill-based scoring rubrics.
- Reduce time-from-application-to-offer from 38 days to under 10 days.
Discovery & architectural audit
Audit of candidate drop-off metrics indicated that 60% of top-tier talent exited the hiring pipeline within the first 5 days due to slow recruiter outreach. Standard keyword filters were also falsely disqualifying non-traditional yet highly qualified candidates.
The Fortiv AI architecture
- 01
Semantic Skill Graph Parser: Contextual resume parser evaluating actual project contributions, GitHub/code artifacts, and domain competencies.
- 02
Autonomous Voice & Chat Screener: Conversational agent conducting preliminary 15-minute qualification interviews covering availability, compensation, and technical scenarios.
- 03
Blind Calibration Rubric: Bias-free candidate dossier generation, scoring candidates against standardized competencies with identity anonymization.
- 04
ATS Integration & Scheduling: Instant synchronization with Greenhouse and Lever, with automated calendar booking for calibrated top-tier finalists.
Verified audit outcomes
Post-deployment impact
- 74% Reduction
- In overall time-to-hire — from 38 days down to 9.5 days
- $320,000 Saved
- In annual recruiting overhead and external contractor spend
- 4.8 / 5.0
- Candidate experience rating across 3,500+ screened applicants
- 92% Offer Acceptance
- Driven by sub-24-hour interview feedback loops
Architectural lessons learned
Providing candidates with immediate self-scheduling options reduces interview no-show rates to under 2%.
Anonymized evaluation rubrics increase qualified diverse candidate progression by 38%.
Future scale roadmap
Deploying predictive workforce analytics to forecast engineering team retention and compensation drift ahead of market shifts.
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