Level AI

What it is

Level AI is contact-centre quality assurance and conversation intelligence built to score every call, chat and email instead of the small sample a human QA team has time to listen to. It sits in the same seat as the quality-management modules of NICE and Verint, but it was built generative-first: custom scorecards are evaluated by its QA-GPT engine across 100% of interactions, and the same conversation data feeds agent coaching, real-time agent assist, voice-of-customer analytics, screen recording and a customer-facing virtual agent. Founder and CEO Ashish Nagar previously worked on conversational AI for Amazon Alexa. The company closed a $39.4M Series C in August 2024, led by Adams Street Partners with Battery Ventures and Eniac Ventures returning, for $73.1M raised in total. Named customers include Smartsheet, VistaPrint, Ollie, Extra Space Storage and Purple.

On 14 May 2026 it launched AI Workers — role-scoped agents that build a coaching plan with the specific calls and talking points attached, run thematic research across transcripts, or produce an executive investigation of why a metric moved. Level AI reports nearly 100 enterprise contact centres running them and more than 25,000 Worker runs at launch.

Why it shows up in CX and support stacks

• It answers the sampling problem directly. A 10-person QA team reviewing five interactions per agent per month covers a rounding error of a 200-seat floor. Level AI scores every interaction against your own rubric, so a compliance miss on a Tuesday night call is found rather than hoped against.
• It covers voice, not only tickets. Integrations span the CCaaS layer — Five9, Genesys, Talkdesk, Amazon Connect, Twilio, Vonage, Dialpad, UJET, LivePerson — as well as helpdesks: Zendesk, Salesforce, Intercom, Freshdesk, Kustomer, Gorgias and Front. Most AI-native QA upstarts start from the helpdesk and treat phone as an afterthought; Level AI’s integration list starts at the phone layer.
• One data set, several jobs. QA, coaching, VoC and agent assist read the same scored transcripts, which removes the usual argument between the QA dashboard and the analytics dashboard about whose numbers are right.
• Regulated-industry paperwork exists. The vendor lists ISO 27001, SOC 2, HIPAA, PCI and GDPR, and its named verticals are financial services, insurance and healthcare — the buyers for whom 100% compliance scoring is a requirement, not a nice-to-have.
• Users rate it well. 4.7 out of 5 across 201 G2 reviews, with 59% of reviewers from mid-market companies; reporting and ease of rollout are the most-cited strengths.

Pricing reality

No published price. Everything is quote-based, sold per agent seat on annual contracts, with implementation charged on top. Third-party estimates disagree: buyer guides cite a range of roughly $80-125 per agent per month, and others put it near $185. Plan on that band, which makes a 50-agent floor about $48,000-111,000 a year and a 200-agent floor about $190,000-445,000 — consistent with the $150,000-500,000 annual budgets third-party guides describe for the typical buyer, a VP of Customer Service at a 200+ seat operation.

For scale: the Zendesk QA add-on lists at about $35 per agent per month, and Rippit’s legacy MaestroQA contracts band at $18,000-60,000 a year for 25-100 agents on Vendr’s data. Level AI is the more expensive line on almost any quote; the case for it rests on voice coverage and the modules bundled around QA, not on the QA scorecard alone.

Best for

Heads of support operations and QA leaders running contact centres of roughly 100 seats and up, where voice is a large share of volume and a regulator or a compliance team cares what agents said on every call — fintech, insurance, healthcare, consumer services. It earns its price when you would otherwise buy QA, speech analytics and agent assist as three contracts.

Skip it under about 50 agents, where the per-seat math and the services-led rollout outweigh the coverage gain. Skip it if you are chat- and email-only on Zendesk — Zendesk QA scores the same tickets inside the tool you already pay for. And skip it if your procurement process requires a self-serve trial; there isn’t one.

Versus the alternatives

The installed-base incumbents in contact-centre quality management are the NICE and Verint workforce-engagement suites. Pick them when you already run their recording and WFM stack and the renewal will bundle QA for less than a new vendor would charge; Level AI wins when their scoring still relies on keyword rules and a sample. Observe.AI ($214M raised) is the closest like-for-like — voice-first, auto-QA plus real-time assist — and deserves a head-to-head bake-off on your own calls. Cresta ($276M raised, $125M Series D in November 2024) is the better-funded rival and the pick when real-time agent guidance, not after-the-fact scoring, is the budget line. The fastest-moving entrants are AI-native support QA tools such as Solidroad and Intryc, which are cheaper, ticket-first and faster to trial; choose them for a digital-only queue under 100 agents. Rippit is the pick for Zendesk or Intercom shops whose real question is conversation analytics rather than agent scoring.

If none fit, run a calibrated LLM rubric over a weekly export of transcripts for one queue before buying anything. It will tell you whether full coverage changes your coaching decisions — which is the only reason to pay for it.

Watch-outs

• Out-of-the-box scores need tuning. G2 reviewers report that AI scores are not always accurate before calibration. Guard: before retiring any human QA, double-score 200 interactions by hand and in Level AI, and write an agreement threshold per scorecard question — 90% is a reasonable bar — into the go-live criteria.
• The rollout is services-led. Third-party guides describe roughly four weeks to first value and about 16 weeks to full deployment. Guard: scope phase one to a single queue and scorecard, and tie part of the implementation fee to that queue running live on 100% coverage.
• Platform breadth turns into bundle creep. QA, assist, VoC, screen recording, virtual agent and AI Workers are separate buying conversations with one vendor. Guard: get a per-module line-item quote, buy only the modules with a named owner, and cap renewal uplift in the order form.
• Its last disclosed round was August 2024, in a segment where Cresta and Observe.AI have raised roughly three to four times as much. Guard: contract for a full export of transcripts, scores and scorecard definitions in a documented format, and test that export during the pilot rather than at exit.
• The AI Workers numbers are vendor-reported. Nearly 100 centres and 25,000 runs come from the launch release, not an audit. Guard: ask for one Worker output on your own data during the evaluation — a coaching plan for a named agent — and have a team lead judge it against what they would have written.