OVI is an AI-native applicant tracking system (ATS) at ovi-me.com. You chat with AI agents Sora and Milo to source candidates, screen CVs, and run AI voice interviews in one workflow.

How do I sign in to OVI?

Sign in to OVI at https://ovi-me.com/auth with your work email or Google account. New teams can start a free trial from the homepage.

OVI AI refers to OVI's hiring agents — Milo for CV screening and Sora for sourcing — built into the AI-native ATS so recruiting teams chat to hire instead of clicking through traditional ATS screens.

OVI is the AI-native ATS where you chat to hire. Two AI agents — Sora for sourcing and Milo for screening — find candidates, score every CV, and run AI interviews, all controlled from a single chat.

What does "chat to hire" mean?

Chat to hire is OVI's AI-native hiring workflow: instead of clicking through a traditional ATS, you describe what you need in chat and OVI's AI agents source candidates, screen applicants, run AI interviews, and send outreach for you.

What is an AI-native ATS?

An AI-native ATS is an applicant tracking system built around AI agents rather than forms and pipelines. OVI's AI chat ATS lets recruiters source, screen, interview, and follow up through conversation — the agents do the work and surface only the candidates worth meeting.

Who are Sora and Milo?

Sora is OVI's AI sourcing agent — she scans LinkedIn, the open web, and your ATS history to find best-fit candidates and reach out in your name. Milo is OVI's AI screening agent — he scores every applicant against your rubric and surfaces a ranked shortlist.

How do OVI's AI interviews work?

OVI runs async AI interviews for every shortlisted candidate and returns scores, transcripts, and recordings — so your team only meets the people worth meeting.

Structured Interview Software 2026: How Ashby, Greenhouse, Lever, and OVI Compare on AI-Assisted Evaluation

By Tim Kreling, Co-Founder, OVI

Published 2026-07-13

Why Structured Interviews Have Become Non-Negotiable in 2026

Unstructured interviews — the classic "tell me about yourself" conversation — predict job performance at roughly the same rate as a coin flip. Decades of industrial-organisational psychology research have established this, and the 2026 regulatory environment is now turning that research into legal exposure.

The EU AI Act, which began applying to high-risk AI systems in February 2026, classifies AI-assisted recruitment tools under its high-risk tier. This creates a compliance imperative: any scoring algorithm used in hiring must be transparent, auditable, and demonstrably free from discriminatory bias. Structured interview software — where every candidate is asked the same questions scored against the same rubric — is one of the clearest ways to satisfy that requirement.

At the same time, companies scaling rapidly face a capacity problem. Interviewing 200 candidates per role with a four-person panel is not sustainable. AI-assisted structured interview platforms promise to compress evaluation time while preserving (or improving) scoring consistency.

Current date (UTC): 2026-07-13

What Structured Interview Software Actually Does

At minimum, a structured interview platform provides:

Standardised question banks — the same questions asked of every candidate for a role
Scorecards — a predefined rubric assigning numeric or qualitative ratings to each competency
Calibration tools — mechanisms to normalise scores across different interviewers
Audit trails — timestamped records of who scored what, and why

AI-native platforms add a layer on top: they can conduct interviews autonomously (via chat, voice, or video), score responses against a rubric using natural language understanding, flag responses that match "red flag" patterns, and surface ranked shortlists to human reviewers.

The Four Platforms Compared

1. Ashby — Best-in-Class Scorecards for Modern Tech Teams

Ashby has built a reputation among Series A–C tech companies for its structured interview infrastructure. Its interview kits combine question sets, suggested follow-ups, and numerical scorecards in a single panel-facing interface. Calibration sessions — where interviewers compare scores before a decision is finalised — are built into the workflow rather than bolted on as an afterthought.

Strengths:

Deep scorecard customisation: per-competency weighting, custom rating scales (3-point, 5-point, or custom labels)
Automatic de-biasing prompts that surface score variance between interviewers
Native analytics showing which interviewers are outliers and which competencies predict downstream retention
Clean API for integration with existing HRIS and offer tools

Limitations:

Primarily designed for knowledge-worker roles; less adapted to high-volume or frontline hiring
No autonomous AI interviewer — human interviews remain the norm; AI assists scoring, not conducting
Pricing scales with headcount, which can become expensive at mid-market scale

Best for: Tech companies running deliberate hiring processes with experienced interview panels.

2. Greenhouse — Enterprise Standard with Broad Ecosystem

Greenhouse is the incumbent structured interview platform at many enterprise organisations. Its Interview Kits have been a staple for over a decade: hiring managers build question sets per stage, interviewers complete scorecards within the ATS, and hiring decisions must be recorded with a rationale before a next step can proceed.

Strengths:

Widest ATS ecosystem — integrates with virtually every HRIS, video platform, and background check provider
Compliance-grade audit trails that satisfy EEOC, GDPR, and EU AI Act documentation requirements
Structured interview templates available for hundreds of role types via the Greenhouse marketplace
Role-based access controls ensuring score visibility is limited to appropriate stakeholders

Limitations:

The AI layer remains limited: scoring suggestions are pattern-based, not contextual
Interface is complex; onboarding new interviewers takes meaningful time investment
The platform is ATS-first; structured interview features are strong but not the sole focus

Best for: Mid-market and enterprise companies that need compliance documentation and broad integration coverage.

3. Lever — Collaborative Interviews with CRM DNA

Lever approaches structured interviews from a relationship-first angle — it began as a recruiting CRM before building ATS and interview features. Its Feedback Forms (Lever's scorecard equivalent) are designed for collaborative evaluation, with inline comment threads allowing interviewers to discuss scores in context.

Strengths:

Highly collaborative scorecard model — ideal for consensus-based hiring cultures
Strong candidate-side experience: automated scheduling, reminder flows, and interview prep materials
DEIB reporting built in: tracks pass-through rates by demographic segment across interview stages
Merged with Employ Inc. giving it a multi-product talent suite

Limitations:

Structured interview rigour is lower than Ashby or Greenhouse: question adherence is encouraged but not enforced
Analytics depth lags behind purpose-built structured interview tools
AI-assisted scoring is minimal compared to newer entrants

Best for: Companies that value collaborative hiring culture and candidate experience over rigid scoring enforcement.

4. OVI — AI-Native Screening Agent with Configurable Rubric

OVI takes a fundamentally different approach: instead of structuring a human-led interview, its Milo agent conducts the screening interview autonomously using a custom rubric configured by the hiring team. This is not a chatbot that routes candidates to a human — Milo evaluates responses, applies weighted competency scores, and produces a ranked shortlist before a human interviewer enters the picture.

Strengths:

Fully configurable rubric: hiring managers define competencies, assign weights (e.g. communication 30%, technical problem-solving 40%, culture fit 30%), set context clues (signals that a candidate is strong), and red flags (disqualifying patterns)
Consistent scoring: every candidate is evaluated against the identical rubric with no interviewer fatigue, variance, or unconscious bias from the evaluation side
Transparent output: Milo produces a structured evaluation for each candidate — not a black-box score, but a per-competency breakdown with supporting evidence from the interview transcript
High-volume efficiency: 1 credit = 1 CV screen, 5 credits = 1 interview minute — scales to hundreds of simultaneous screening interviews without additional recruiter headcount
Sora sourcing agent pairs with Milo for an end-to-end pipeline: systematic outreach, auto-follow-up, and reply-rate data feeding into Milo-scored shortlists

Limitations:

Primarily focused on the screening and first-interview stage; later-stage structured interviews still require human panels
Newer entrant with smaller ecosystem than Greenhouse or Lever; fewer pre-built integrations at launch
Best results require upfront investment in rubric design — hiring teams who lack clear competency frameworks will need to build them before configuring Milo

Best for: Teams running high-volume screening, companies building data-driven hiring infrastructure, and organisations where inter-rater reliability is a compliance priority.

Head-to-Head Comparison

Dimension	Ashby	Greenhouse	Lever	OVI (Milo)
Structured scoring rigour	High	High	Medium	High
AI-assisted scoring	Partial	Minimal	Minimal	Full
Autonomous interview capability	No	No	No	Yes
Rubric customisation depth	Deep	Standard	Standard	Deep
Bias safeguards	Strong	Strong	Strong	Strong
Ecosystem integrations	Good	Excellent	Good	Growing
High-volume scalability	Moderate	Moderate	Moderate	Excellent
Pricing model	Per seat	Per seat	Per seat	Per credit

The Compliance Angle: What EU AI Act Auditors Will Look For

For companies operating in or selling into the EU, structured interview platforms must now demonstrate:

Transparency of scoring logic — candidates must be able to understand how they were evaluated
Bias testing and documentation — evidence that the scoring model was tested against protected characteristics
Human oversight — AI scoring must feed into a human decision, not replace it entirely
Data minimisation — interview data should be retained only as long as necessary

Ashby and Greenhouse have the most mature compliance documentation. OVI's per-competency breakdown with transcript evidence satisfies transparency requirements well; its rubric-based approach (rather than a black-box model) is architecturally well-positioned for the auditability requirement.

Pricing Overview (2026)

Ashby: Custom pricing; typically $3,000–$8,000/month for 50–200 person companies
Greenhouse: Custom enterprise pricing; mid-market plans start around $4,000–$6,000/year per module
Lever: $3,500–$7,000/year depending on company size
OVI: Free plan (50 credits, 10 interview minutes, one-time); Launch $29/month (500 credits, 100 interview minutes); Starter $99/month (1,000 credits, 200 minutes, 25 headhunts); Growth $450/month (5,000 credits, 1,000 minutes, LinkedIn integration + 60 ATS connections)

Choosing the Right Tool

Building a high-volume screening pipeline from scratch? OVI delivers the fastest time-to-structured-evaluation with the lowest per-assessment cost at scale.
Needing an enterprise-grade ATS with embedded structured interviews? Greenhouse remains the compliance and integration standard.
Running consensus-driven hiring at a Series B tech company? Ashby's scorecard depth and calibration tooling are best-in-class for growing teams.
Prioritising candidate experience and collaborative evaluation? Lever's CRM-first model fits companies where relationship quality drives offer acceptance.

The key question is whether structured interviews remain a human-led process or shift to AI-conducted screening at the top of the funnel. For the former, Ashby and Greenhouse lead. For the latter, OVI is the only platform in this comparison that conducts the interview autonomously against a recruiter-defined rubric — which is where the industry is heading as hiring volumes grow and recruiter bandwidth does not.

What is structured interview software?

Structured interview software standardises the hiring process by giving every candidate the same questions, scoring responses against predefined competency rubrics, and creating auditable records of hiring decisions. AI-native versions like OVI's Milo conduct the interview autonomously and produce ranked shortlists.

How does AI improve structured interview scoring?

AI removes the primary source of inconsistency in structured interviews: human interviewers. By conducting and scoring interviews autonomously against a fixed rubric, AI ensures every candidate is evaluated on identical criteria with no fatigue, variance, or in-room social bias affecting the score.

What compliance features should structured interview tools have in 2026?

Under the EU AI Act, high-risk AI in recruitment must provide transparent scoring logic, evidence of bias testing against protected characteristics, human oversight in final decisions, and data retention controls. Ashby, Greenhouse, and OVI all offer compliance-grade audit trails.

How does OVI Milo differ from traditional structured interview platforms?

Traditional platforms like Greenhouse and Ashby structure human-led interviews with scorecards. OVI's Milo conducts the screening interview autonomously using a hiring-team-configured rubric with weighted competencies, context clues, and red flags — producing ranked shortlists before human interviewers enter the process.

Which structured interview platform is best for high-volume hiring?

OVI is purpose-built for scale: 5 credits per interview minute means a company can screen hundreds of candidates simultaneously at a fraction of the cost of human-panel interviews. Greenhouse and Ashby work well at volume but still depend on human interviewer capacity at each stage.