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.

Gem Built AI Into the Foundation — and Recruiters at Airbnb, DoorDash, and Zillow Are Getting 5x More Done

2026-06-19

When Zillow's talent acquisition team started using Gem's AI sourcing, something shifted. Resume screening time dropped 50–75%, and AI-sourced candidates converted at a 57% rate — compared to the 10–20% they'd been seeing with manual LinkedIn searches (Source 7). That isn't a marginal improvement. It's a different operating model.

Gem is at the center of a structural change in how enterprise recruiting teams work. The San Francisco-based company, co-founded by CEO Steve Bartel and CTO Nick Bushak, has evolved from a recruiting CRM add-on into what it calls an AI-first all-in-one recruiting platform — unifying ATS, CRM, sourcing, scheduling, and analytics under a single AI layer with access to more than 800 million candidate profiles (Source 3).

For TA leaders evaluating their tech stacks in 2026, Gem's trajectory raises a pointed question: does your AI actually see the full picture, or is it working blind?

The Problem With Siloed AI

Most recruiting teams are drowning in tools. Over 550 recruiting vendors now compete for attention, and the average recruiter juggles 10–20 tools daily (Source 3; Source 7). Recruiters handle nearly three times more applications than they did in 2021 and manage 40% more requisitions, while the interview-per-hire ratio has increased 42% in three years (Source 7).

Adding AI to each tool individually doesn't solve the core issue. A sourcing tool's AI doesn't know that a candidate was already interviewed and rejected six months ago. An outreach tool's AI can't factor in prior application history. Each tool sees only its own slice — and acts on incomplete data.

The result: duplicate outreach to recently rejected candidates, redundant re-sourcing of people already in your ATS, and AI that makes confident recommendations based on partial information.

One Platform, One AI Brain

Gem's answer is architectural. Rather than bolting AI onto separate tools, the company rebuilt its platform so that a single AI layer has unified visibility across the entire candidate lifecycle — who applied, when, how interviews went, what outreach was sent, and what happened next (Source 3).

This unified context powers a suite of AI agents that span every recruiting stage (Source 2; Source 4):

AI Sourcing. Natural language search across 800M+ profiles replaces complex boolean strings. The system identifies candidates based on funding-stage experience, career progression patterns, and specialized expertise — with full transparency into match reasoning (Source 4).

AI Application Review. For teams receiving high volumes — some Gem customers process over 250,000 applications monthly — AI ranks candidates against hiring criteria and generates transparent summaries explaining its decisions. Only about 25% of applications typically meet minimum criteria; AI surfaces them in seconds rather than the 2,000+ manual review hours that volume would otherwise require (Source 2; Source 4).

AI Outreach. By drawing on a candidate's full history — past applications, interview feedback, prior outreach — Gem's AI creates personalized messages. Gem reports that customers using AI-powered outreach see response rates increase by 30–40% (Source 2; Source 3).

AI Talent Rediscovery. The platform scans existing ATS and CRM databases to surface past candidates who match current openings — complete with interaction history. Gem's benchmark data indicates that 30–50% of hires come from existing records, and for enterprise companies, the figure jumps to 50–70% (Source 2; Source 4).

AI Scheduling. Intelligent coordination handles complex onsite panels, timezone management, interviewer load balancing, and training requirements. Gem reports organizations schedule 2–3 times more interviews using half the coordination effort (Source 2).

AI Fraud Detection. A dedicated agent flags suspicious applications before they consume team resources (Source 2).

Enterprise Proof Points

More than 1,200 TA teams use the platform, including Airbnb, DoorDash, CarMax, Zillow, Wayfair, and Cintas (Source 1). Gem reports that enterprise customers see 5x recruiter productivity gains and 30–50% tech cost reductions through stack consolidation (Source 3).

Independent review platforms reinforce the signal. Gem holds a 4.8 out of 5 rating on G2 (Source 5), and reviewers on Capterra highlight the platform's depth of integration and recruiter workflow alignment (Source 6).

At Mission Cloud, two recruiters made 43 hires in 90 days while reducing time-to-hire by 12% (Source 7). These aren't theoretical benchmarks — they're operational results from teams that replaced fragmented stacks with a unified platform.

When Does Consolidation Make Sense?

Not every organization needs to rip and replace tomorrow. But TA leaders should evaluate consolidation if:

Your AI tools can't see each other's data. If your sourcing AI doesn't know what your ATS knows, you're running blind automation.
You're paying for overlapping functionality. Multiple tools handling sourcing, CRM, and scheduling often means duplicated costs — Gem reports 30–50% savings when teams consolidate.
Recruiter capacity is the bottleneck. When each recruiter manages 40% more reqs than three years ago, tool complexity becomes a direct drag on throughput.
Your rediscovery rate is low. If most of your hires are net-new sourcing while qualified past candidates sit untouched in your ATS, unified AI rediscovery is leaving value on the table.

Note that Gem does not publish pricing, so TA leaders should request custom quotes during evaluation. It's also worth noting that Gem markets itself as "the only AI-first all-in-one" — though competitors like Ashby also position around AI-native architecture. Evaluate based on your specific workflow requirements and integration needs.

The Dividing Line

Gem CEO Steve Bartel frames the current moment bluntly: "It's disrupt or be disrupted. It's become AI-first or be left behind" (Source 7). Seventy-six percent of HR leaders believe adopting AI is imperative within 12–24 months to maintain competitive advantage (Source 4).

The distinction that matters going into 2026 isn't whether your recruiting tools have AI — nearly all of them claim to. It's whether that AI has the unified context to actually make intelligent decisions. Gem's bet is that architecture determines outcomes, and the early enterprise results suggest they may be right.