Why choose Level AI over Observe AI?

Level AI has an in-house AI trained on noisy contact center data transcription engine with over 85% accuracy and comes with advanced features like automated redaction, speaker separation and entity boosting for brand and product names.

When it comes to automating contact center tasks, quality transcription is crucial. Observe AI benchmark with off-the-shelf transcription tools is at 72% accuracy and come up short in delivering accurate transcriptions for noisy contact center data.

Level AI comes with a truly revolutionary Auto-QA solution that can evaluate all of your conversations on nearly every assessment criteria and standard on your scorecard with near-human accuracy. With QA-GPT, setting up Auto-QA is as simple as typing in your existing scorecard Questions, and QA-GPT would understand just like a QA manager and evaluate with near-human accuracy.

Observe AI comes with a handy no-code tool for you to build complex scenarios and leaves you the task of imagining every possible phrase and keyword the customer may use with no way of knowing what you’ve missed rather than letting the AI do the job. It also doesn’t score conversations using all of your custom evaluation criteria and standards.

Level AI’s underlying technology leverages advanced semantic intelligence and Generative AI, surfacing insights on critical conversations. It can take entire conversations into context rather than relying on matching keywords and phrases, allowing it to reason and understand like a human. The AI provides reasoning for auto scores with contextual quotes, resulting in an Auto-QA that’s 10x more accurate and trustworthy.

Selecting the right Auto-QA and conversation analytics platform hinges on one crucial factor: the quality of its underlying AI technology. Unfortunately, Observe AI relies on black box, first-generation, legacy keyword-based technology, which is known for being inherently inaccurate, untrustworthy, and lacks the ability to provide reasoning for its scores.

Level AI’s leading AI engine automatically extracts all sorts of analytics from your unstructured omnichannel customer data, from call center data and QA reports to valuable business insights like customer or product analytics. In addition to excellent out-of-the-box dashboards, QA Managers can create custom dashboards to track their most important metrics, apply filters, group data, and much more.

Observe AI doesn’t have AI-enabled analytics like the voice of the customer or other business insights from your contact center. Instead, it comes with a graph-building tool that only lets you pull data from your existing databases.

Level AI offers a comprehensive understanding of voice of the customer using generative AI. Insights are available at a granular level for every conversation and a high level with topics and subtopics trending over time.

Observe AI offers unactionable wordclouds of prelabeled topics. This is an old text-analytics solution that predates modern AI and is not a true “voice of the customer” solution. They don’t offer AI that can pick up new topics that are raised by customers or show their trend over time, or provide actionable insights.

Provides knowledge assist and real-time guidance on the next best actions to drive down AHT and solve customer problems faster. AgentGPT will answer agents’ questions in real-time with information from past conversations and knowledge bases.

Observe AI’s new generative AI learns from knowledgebases to provide prompts to agents on only pre-defined triggers. Auto coaching is limited to giving meaningless platitudes and cold robotic encouragement to agents.

Easy to use multi-screen recording that intelligently records every conversation and post-call wrap-up automatically with customizable PSI and PII redaction. Moreover, Level AI’s semantic engine leverages NLU to highlight critical moments of conversations on screen capture for QA managers.

Observe AI’s screen recording solution doesn’t sync up with and integrate into the QA interaction analysis platform leading to many frustrated minutes being spent on wrangling together the various tools for recording and redaction rather than evaluating the conversations.