It's like having a brilliant colleague who sits in your meeting, reads the room perfectly, and whispers exactly the right insight at exactly the right moment.

Fern's core meeting copilot mode uses a multimodal deep learning pipeline that fuses real-time audio transcription, speaker diarization, and conversational context modeling to understand not just what is being said, but the social dynamics of how it is being said. A proprietary "Conscious Intelligence" layer—built on transformer-based LLMs augmented with turn-taking prediction and social timing models—determines the optimal moment to surface information, suggest actions, or remain silent. The system operates in "Shadow Mode" (private feedback visible only to the user) or "Full Presence" (active participant), allowing flexible deployment. Low-latency inference pipelines ensure responses arrive within the natural cadence of conversation, making the AI feel like a seamless collaborator rather than a disruptive tool. This is fundamentally different from post-meeting summarization tools because it operates in real time and adapts its behavior to social context.

It's like having a world-class executive assistant who can read the room better than most humans but never steals the spotlight.

It's like having your company's top sales closer whispering winning tactics in your ear during every deal call.

Fern's sales coaching capability extends its socially aware AI framework into the high-stakes domain of live sales conversations. The system ingests real-time audio and applies sentiment analysis, objection detection, and conversational momentum scoring to build a dynamic model of how the call is progressing. Using fine-tuned LLMs trained on successful sales interaction patterns, Fern identifies critical moments—such as when a prospect raises a pricing objection, shows buying signals, or begins to disengage—and surfaces tactical recommendations to the rep in real time via Shadow Mode. Unlike post-call analytics tools like Gong or Chorus that provide retrospective insights, Fern intervenes in the moment, enabling reps to adjust their approach mid-conversation. The system can also delegate tasks in real time, such as pulling up competitive battle cards, pricing calculators, or case studies relevant to the prospect's specific objections, dramatically reducing the cognitive load on the sales rep.

It's like having a GPS for sales calls—instead of telling you where you went wrong after you're lost, it reroutes you in real time before you miss the turn.

It's like building a massive library of "how humans actually talk to each other" so the AI can learn social skills the way people do—by watching and listening.

Ishiki Labs' IshikiLabs Capture application represents a novel approach to solving one of the hardest problems in socially aware AI: obtaining high-quality, structured training data that captures the nuances of human social interaction. The app enables controlled collection of audio and visual data from real-world professional interactions, applying real-time quality checks including audio clarity validation, speaker balance verification, and metadata tagging for conversational context (e.g., meeting type, participant roles, emotional tone). This structured data pipeline feeds directly into the training of Ishiki's proprietary social timing models, turn-taking predictors, and multimodal fusion layers. By building a proprietary dataset rather than relying on public corpora, Ishiki Labs creates a compounding data moat—each interaction collected improves model performance, which improves user experience, which drives more usage and data collection. The approach draws on the founders' research background in disentangled representation learning and variational autoencoders, enabling the extraction of separable social features (timing, tone, intent, emotion) from raw multimodal data for more efficient and interpretable model training.

It's like building a flight simulator for social skills—you need thousands of hours of real flight data before the simulator can teach pilots anything useful.