MeetraAI

Automatically converts audio from sales calls, customer success interactions, and support conversations into timestamped transcripts while identifying and labeling individual speakers. Uses speech-to-text processing with speaker separation algorithms to distinguish between multiple participants, enabling downstream analysis to attribute statements to specific roles (e.g., sales rep vs. prospect). Integrates with common communication platforms and recording systems to capture audio streams in real-time or batch mode.

Analyzes transcript segments and audio tone to classify emotional states and sentiment polarity (positive, negative, neutral) at the speaker level and conversation-phase level. Uses a combination of NLP-based text sentiment analysis and acoustic feature extraction (pitch, pace, energy) to detect emotional shifts. Produces segment-level sentiment scores with temporal visualization, enabling identification of conversation turning points and emotional escalations or de-escalations.

Enables customers to fine-tune sentiment, intent, and objection classification models on their own conversation data to improve accuracy for domain-specific language and sales methodologies. Provides a training interface where customers can label conversation segments and trigger model retraining. Supports transfer learning to leverage pre-trained models while adapting to customer-specific patterns. Produces model performance metrics (precision, recall, F1) to validate improvements before deployment.

Monitors ongoing calls in real-time and surfaces alerts or coaching prompts to reps or managers when specific conversation patterns are detected (e.g., 'customer expressed budget concern — suggest trial offer', 'rep has talked for 3+ minutes without customer response — prompt to ask question'). Uses low-latency intent and sentiment detection to identify intervention opportunities within 5-10 seconds of occurrence. Supports configurable alert rules and delivery channels (in-app notification, SMS, Slack).

Provides customizable dashboards and reports aggregating conversation metrics across teams, time periods, and customer segments. Includes pre-built reports (team sentiment trends, objection frequency, rep performance rankings, customer health) and custom report builder for ad-hoc analysis. Supports drill-down from aggregate metrics to individual calls and segments. Produces trend analysis showing metric changes over time and correlation analysis (e.g., 'calls with high discovery quality have 40% higher close rates').

Automatically identifies customer intents (e.g., 'pricing inquiry', 'technical support', 'renewal discussion') and sales rep intents (e.g., 'discovery', 'objection handling', 'closing attempt') throughout the conversation. Uses intent classification models trained on sales conversation patterns to tag conversation phases and extract key topics discussed. Produces a conversation flow diagram showing intent transitions and topic sequences, enabling analysis of conversation structure and effectiveness.

Identifies mentions of competitors, pricing discussions, and customer objections within conversations, then aggregates patterns across calls to surface recurring themes. Uses named entity recognition (NER) to detect competitor names and product mentions, combined with intent classification to identify objection contexts. Produces reports showing which competitors are mentioned most, what objections are most common, and how reps handle them, enabling sales leadership to identify coaching gaps and competitive positioning weaknesses.

Automatically flags conversation segments where coaching opportunities exist (e.g., rep missed discovery question, failed to handle objection, talked too much without listening). Uses behavioral pattern matching against sales methodology frameworks to identify deviations from best practices. Scores individual reps on dimensions like discovery quality, objection handling, talk-to-listen ratio, and closing effectiveness. Produces rep performance dashboards with trend analysis and peer benchmarking.

Analyzes customer success and support calls to extract health signals (sentiment trends, feature adoption mentions, renewal readiness indicators) and produces a churn risk score. Uses historical conversation data to identify patterns associated with churned customers, then applies those patterns to current customers. Integrates with CRM to surface at-risk accounts and trigger intervention workflows. Produces customer health dashboards with leading indicators derived from conversation content.

Integrates conversation analysis results (sentiment, intents, coaching moments, objections) directly into CRM systems (Salesforce, HubSpot, Pipedrive) and triggers automated workflows based on conversation outcomes. Pushes key insights as call summaries, next steps, and coaching flags into deal records and contact profiles. Supports webhook-based integrations and native CRM connectors to enable real-time insight delivery and automation trigger conditions (e.g., 'if objection detected, create coaching task').

Enables full-text and semantic search across the conversation corpus to find calls matching specific criteria (e.g., 'calls where customer mentioned budget concerns', 'calls where rep used closing technique X', 'calls with high sentiment drop'). Uses vector embeddings of conversation segments to enable semantic similarity search beyond keyword matching. Supports filtered search by rep, customer, deal stage, date range, and conversation outcome. Returns ranked results with highlighted relevant segments and context.

Automatically detects the language spoken in conversations and applies language-specific NLP models for transcription, sentiment analysis, and intent extraction. Supports 15+ languages including Spanish, French, German, Mandarin, Japanese, and others. Produces multilingual transcripts and analysis results, enabling global sales and CS teams to analyze conversations in their native languages. Handles code-switching (mixing multiple languages in a single call) with language-aware segmentation.

Manages secure storage of call recordings with configurable retention policies, encryption, and access controls. Implements compliance features including GDPR consent tracking, CCPA data deletion workflows, and audit logging for who accessed which recordings. Supports on-premise or cloud storage options with SOC 2 Type II certification. Provides granular permission controls (e.g., reps can only access their own calls, managers can access team calls, compliance can access all).