CallMiner, Level.AI, Observe.AI, and MiaRec [Comparisons, Pricing, Features, & Reviews]

Standard CX Metrics: CallMiner does NOT provide AI-derived any CX metrics. Instead, its analytics infers customer satisfaction and effort levels from conversation data by filling "in the gaps and making up for the shortcomings of CSAT surveys." In addition, CallMiner advertises the ability to “predict NPS scores by analyzing the sentiment of each conversation.”​ Additionally, CallMiner tracks sentiment and emotion trends that correlate with CX outcomes like churn or loyalty.  

Business Intelligence (BI): CallMiner has traditionally excelled in the breadth of analytics, but its BI capabilities are more traditional (analyst-driven) than generative AI-based. CallMiner offers a module called Eureka Analyze/Visualize, which allows slicing and dicing interaction data and creating custom categories and metrics. Users can define their KPIs by writing queries (for instance, a “Compliance Risk Index” counting certain infractions). The platform can certainly track custom metrics over time and deliver deep insights – e.g., using unsupervised ML, it can surface patterns that predict NPS or churn. However, the process of extracting those insights often requires expert configuration or data analyst involvement. CallMiner has recently added semantic search (natural language querying of calls). Still, it does not yet have a conversational BI tool (you can’t just ask, “Which issue caused CSAT to drop last month?” and get a narrative answer from the AI).

Customizable Reporting & Dashboarding: CallMiner's reporting capabilities are highly customizable but complex. CallMiner has a rich reporting tool (“Visualize”) for the creation of detailed dashboards and reports. Users can technically customize nearly anything – filters, charts, custom metrics – but the interface and approach often require technical data analytics skills or technical expertise. Many CallMiner users rely on data analysts or the vendor’s professional services to build out advanced reports. A critique is that while it’s “custom,” it’s not easily self-service for a typical contact center manager. The power is there, but due to the steep learning curve we mark it ◯.

Auto QA: Observe.ai offers a contact center LLM- and ML-based Auto QA solution, enabling contact centers to score 100% of their calls automatically. Observe.ai has a different way of scoring calls for QA than MiaRec. It utilizes Gen AI to identify QA “Moments” that are either out-of-the-box “Moments” (pre-built call analytics for greetings, compliance phrases, etc.) for the most common prompts or custom QA criteria or insights created by typing natural language prompts. In addition to Auto QA, Observe.ai offers support for Manual QA, Agent Performance and Coaching,  and Screen Recording.

Sentiment Analysis: Observe.ai provides sentiment detection that incorporates both textual, acoustic, and vocal cues, such as the customer’s tone, speech rate, volume, periods of silence, and overtalk.

Topic Analysis: Observe.ai does not offer a flexible topical analytics module where users define arbitrary topics or see an unsupervised topic clustering. It supports topic analysis mainly through predefined dashboards to help uncover call reasons by providing use-case-specific dashboards. Essentially, topic insights are available, but the capability is somewhat siloed​. Observe.ai’s focus is more on targeted “Moments” (specific events) and less on broad topic discovery, so customization is limited in this area.

Standard CX Metrics: Observe.ai does not currently offer any standard AI-predicted CX metrics such as CSAT or NES out-of-the-box. Observe.AI’s marketing often mentions boosting CSAT and first call resolution as outcomes​, and it can track provided metrics (like if you import post-call survey results or QA scores). For example, its sentiment analysis is used as a proxy to gauge caller satisfaction in the absence of surveys, e.g., a very negative sentiment call could be treated as a low CSAT interaction. 

Business Intelligence (BI): Observe.ai currently has limited BI and custom reporting beyond its core use cases. The platform includes standard dashboards (for QA, agent performance, etc.) and some Business Insights section. This suggests Observe.AI does not yet empower users to create rich custom BI visuals or define new KPIs on the fly. Nor does it provide generative narratives about data trends. Its development has prioritized real-time guidance and QA automation, so BI analytics is a weaker spot (confirmed by “no trend analysis” feedback)​. They are likely improving this (especially with their LLM – one could imagine future auto-generated insights). Thus, Observe.AI’s BI capability is partial, mostly confined to pre-built insights (like which agents need coaching or which calls failed QA) rather than an open BI toolkit.

Customizable Reporting & Dashboarding: Basic pre-built dashboards with limited customization., e.g., a trend of sentiment or a pie chart of call dispositions – and can filter by date, team, etc. Observe.AI provides out-of-the-box dashboards for QA scores, agent performance, and compliance. However, the ability to significantly customize these or build new dashboards is minimal. Users note that the reporting UI only shows simple single-metric widgets and lacks comparative or drill-down views​. There isn’t a drag-and-drop report builder to add new charts easily. You would need to export data into BI tools for more complex analysis. Given that “reporting leaves a lot to be desired” by user accounts​, Observe.AI falls in the lower end here. We rate it ◯ (partial) because basic tracking is there, but true self-service dashboarding is weak.

Auto QA: Also Level.ai offers fully automated Gen AI-based QA to auto-score up to ~100% of interactions on every channel (call, chat, or email) with its proprietary “QA-GPT” model against custom scorecards​. The company stresses the point that (like Observe.ai), its LLM is trained on contact center data. QA-GPT will also provide evidence and reasoning for each call, even on subjective metrics​, in addition to a QA score.

Sentiment Analysis: Uses a Generative AI-based sentiment scoring, focusing on granular emotion levels. Level AI computes a “multi-emotion” customer sentiment score on a 0–10 scale. Level AI only scores customer sentiment, not agent sentiment​. The solution also tags moments in a conversation where a specific emotion is detected. 

Topic Analysis: Level AI’s platform mines all customer interactions to identify key themes using its proprietary Generative AI-powered topical analysis. It can surface trending issues or root causes. However, there seems to be no way to customize the topic categorization. 

Auto-Redaction: Level AI provides automatic redaction of sensitive customer data as part of its platform’s security features. All personally identifiable info like names, addresses, credit card numbers, etc., are automatically removed from call recordings and transcripts​. Level AI likely uses an ML-based NER approach under the hood (similar to peers), though it’s less publicly detailed. 

Custom AI Prompts: There is no dedicated prompt designer in the platform. Level AI’s generative capabilities (QA-GPT, AgentGPT, etc.) are largely behind-the-scenes, and users interact with them through productized features rather than by writing custom prompts. For example, in QA you input questions, but you don’t directly craft the underlying LLM prompt beyond the QA form itself. 

Standard CX Metrics: Level AI offers an Inferred CSAT (iCSAT) score, which is a composite AI-driven customer satisfaction metric derived from factors like sentiment, issue resolution, and customer effort (e.g., repetition, time, transfers, customer actions) during the call​. This provides a quantifiable measure of how satisfied the customer likely was, even if no survey was sent. The company does not explicitly measure a predicted NPS or NES score. 

Business Intelligence (BI):  Level AI introduced a Contact Center and Business Analytics component with promises of comprehensive reporting, but in practice it appears somewhat limited. Level AI currently lacks a generative BI assistant – it doesn’t automatically generate written insights or allow natural language questions on data (its generative tech is focused on QA and agent assist). Custom KPIs seem to be limited to what can be calculated from the existing data fields (there’s no mention of a full custom formula builder). Overall, Level’s BI/reporting is user-friendlier than CallMiner’s (no coding required for basic custom charts​), but it’s not as powerful in handling truly custom or complex analytics. We rate it partial – it covers common needs well (trending dashboards, filtering, some custom views)​, yet doesn’t reach the “analyst-free full BI” ideal for all scenarios.

Customizable Reporting & Dashboarding: Modern, moderately flexible dashboards. Level AI’s platform includes a user-friendly reporting interface. As mentioned, users can personalize dashboards with graphs and info relevant to them​. The system can analyze data over custom time ranges quickly and is noted for being “very user-friendly” in this regard​. Creating a custom view (for example, a dashboard of key QA metrics for Team A vs Team B) is feasible in-app. While not as deep as a dedicated BI tool, this covers most day-to-day needs without requiring an analyst. On the flip side, extremely advanced or completely novel reports might not be possible (the customization is within a defined template). But for an “analyst-free” experience, Level AI does well – a supervisor can get useful dashboards on their own. t’s a notch below MiaRec in flexibility, but clearly more usable than legacy systems that need expert help.

Auto QA: MiaRec offers Automated Quality Management (Auto QA) as part of its GenAI-powered platform​, allowing companies to automatically score 100% of their calls. Create custom scorecards using simple natural language prompts, test it on real calls in a sandbox, and tweak it to provide you with the most accurate results at scale. In addition, supervisors can easily and quickly manually review calls flagged by the AI for further intervention.

Sentiment Analysis: MiaRec's LLM-powered sentiment analysis provides deep insight into how a customer or agent felt during a conversation. MiaRec analyzes every call transcript and produces a detailed sentiment score between –100 (extremely negative) to +100 (extremely positive) for the customer and the agent​. It also gives a textual explanation and evidence of why the sentiment was assessed that way. MiaRec’s approach goes beyond simple polarity; it captures the trajectory of sentiment across the call and highlights specific positive or negative phrases. Historical sentiment trends can be reviewed via heatmaps and filters​. 

Topic Analysis: Offers Generative AI-based Topical Analytics with high customization. In mid-2024, MiaRec released a new generative topic analysis tool, replacing its older rule-based topic engine​. The generative AI model understands conversation context and nuance, automatically categorizing calls by themes (without rigid keyword lists). Users can still define or adjust topic categories if needed (ensuring alignment with business-specific terms), but many will find the out-of-the-box generative topics comprehensive. This provides more reliable and flexible insight into why customers are calling and emerging issues.

Auto-Redaction: MiaRec offers an Enhanced ML/NER-Based Auto-Redaction feature for both audio and text. In early 2023, MiaRec launched an AI-driven redaction that leverages Named Entity Recognition (NER) and Machine Learning to identify a wide range of PII/PHI in unstructured text​. This ML model was trained on thousands of conversations and can recognize entity patterns (e.g. detecting a credit card by pattern + context, or a health insurance ID) with higher accuracy than regex alone​. 

Custom AI Prompts: MiaRec is the only vendor (to our knowledge) that provides a full-fledged AI Prompt Designer for custom generative AI prompts. The Prompt Designer allows users to create, test, and refine prompts within a secure sandbox using their own call data​. For instance, a manager could write a prompt to have the AI evaluate calls for a very specific behavior or extract a custom insight, and then trial that prompt on sample calls to see the results before deploying it live​. This capability means MiaRec customers can tailor the generative AI to their unique needs – beyond out-of-the-box functions – with confidence. The prompt designer can be used to improve call scoring accuracy, produce customized call summaries, targeted agent coaching tips, extract custom AI insights (e.g., license plate of the vehicle) and more, all aligned to the business’s terminology and goals​. 

Standard CX Metrics: MiaRec automatically measure and track standard CX metrics (e.g., CSAT, NPS, NES, and customer churn risk) from conversation data​. Net Easy Score (NES) is essentially Customer Effort Score, reflecting how easy the interaction was​. By analyzing language (e.g. expressions of frustration or smooth resolution), MiaRec infers an effort score. Similarly, it uses sentiment and outcome cues to estimate CSAT for each call, and even NPS propensity. These metrics are available in dashboards for trend analysis. For example, you might see a trend of improving CSAT week over week, or identify calls with low NES to target for process improvement. This gives contact centers a 360° view of CX without relying solely on post-call surveys. (Of course, actual survey results can be incorporated if available, but MiaRec’s value is providing these insights from 100% of calls.) The AI models behind these metrics have been trained on industry data to ensure the scores correlate well with true customer satisfaction and loyalty.

Business Intelligence (BI): In addition to measuring and tracking standard CX metrics, MiaRec customers can extract custom metrics and CX intelligence​ from call transcripts at scale, without requiring  data analysts or technical help. It delivers a full BI-like experience designed to optimize not only contact center operations but operations across the entire business. Users can define new KPIs to track marketing campaign effectiveness, drive product innovations, and much more by getting detailed customer and market insights. The platform then tracks these custom KPIs over time in its dashboards. MiaRec also emphasizes AI Insights, where the system can highlight notable trends or anomalies (e.g., “Spike in cancellation calls this week”). While not a chatbot per se, it’s moving toward providing narrative explanations for data. This feature includes advanced customizable reports and AI insights along with the prompt designer​. The goal is an “analyst-free” experience – managers can get the reports and answers they need through configuration, not coding​. For example, MiaRec can automatically correlate sentiment downturns with specific topics, flagging a emerging issue to address. 

Customizable Reporting & Dashboarding: MiaRec provides easy,-to-use, self-serve customizable reporting. The platform provides a drag-and-drop dashboard builder where non-technical users can create and modify reports. All the captured metrics (standard and custom) can be turned into charts or KPI widgets. MiaRec’s philosophy here is “analyst-free, business-friendly analytics”​. For example, a contact center leader could create a dashboard showing: average CSAT (predicted) this week vs last, top 5 call topics for complaints, sentiment trend, and agent QA scores – all without writing code or contacting IT. The UI supports filtering, grouping, and comparing metrics over time with just a few clicks. If a new KPI is needed, users can simple use the Prompt Designer to extract the data and then it becomes available to chart. Compared to others, MiaRec’s reporting is one of the most flexible and user-centric. It is built to be used directly by QA managers and CX leaders.