Sybill

Captures and transcribes live or recorded sales calls with automatic speaker identification, converting audio streams into timestamped, speaker-labeled text. The system integrates with common conferencing platforms (Zoom, Teams, Google Meet) via API webhooks or browser extensions to intercept audio feeds, then processes them through a speech-to-text engine with speaker separation models to distinguish between sales rep and prospect voices throughout the conversation.

Analyzes the emotional tone, sentiment, and engagement levels of both sales rep and prospect throughout the call by processing audio features (prosody, pitch, pace, volume) and linguistic patterns. Uses a combination of acoustic feature extraction and NLP sentiment models trained on sales conversations to detect emotional shifts, frustration, enthusiasm, and agreement signals, producing a timeline of emotional states correlated with specific discussion topics.

Generates concise, structured summaries of sales calls by combining transcript analysis with emotion insights, extracting key information into predefined fields (next steps, pain points, areas of interest, decision timeline, stakeholders involved). Uses a multi-stage NLP pipeline: first identifies key topics and segments from the transcript, then applies entity recognition to extract specific pain points and interests, then synthesizes emotion data to weight importance, and finally generates natural language summaries organized by category with confidence scores.

Maintains coherent understanding of conversation flow across the entire call by tracking topic shifts, building context windows that preserve relevant prior discussion, and linking current statements back to earlier context. Uses a topic segmentation model to identify when the conversation shifts between discovery, objection handling, pricing discussion, etc., and maintains a context graph that links mentions of pain points or interests back to the original context in which they were introduced, enabling accurate extraction even when topics are revisited or discussed non-linearly.

Automatically logs call summaries, transcripts, and extracted insights into CRM systems (Salesforce, HubSpot, Pipedrive, etc.) by mapping Sybill's structured output fields to CRM contact/opportunity records. Implements bidirectional sync: reads prospect context from CRM before the call (company, prior interactions, deal stage) to improve extraction accuracy, then writes call summaries, next steps, and updated deal information back to CRM after the call, with conflict resolution for concurrent edits and audit logging for compliance.

Generates objective performance metrics for individual sales reps by analyzing call patterns across multiple calls, including talk-time ratio, question-asking frequency, objection handling effectiveness, and emotional engagement matching. Compares individual rep performance against team benchmarks and best performers, identifies coaching opportunities (e.g., 'you're talking 70% of the time vs. team average 50%'), and surfaces call examples for training. Uses statistical aggregation across a rep's call history to identify trends and patterns rather than single-call judgments.

Identifies buying signals and engagement indicators throughout the call by analyzing both linguistic patterns (e.g., 'when can we start', 'how much does it cost', 'can you send me a proposal') and emotional signals (e.g., increased enthusiasm, agreement tone, reduced objections). Correlates these signals with conversation topics to determine which aspects of the pitch resonated most, and assigns confidence scores to buying readiness based on signal strength and consistency. Produces a buying signal timeline that shows when engagement peaked and what triggered it.