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The first Large Language Model purpose-built for human-to-human conversational intelligence.

Nebula is a specialized Large Language Model (LLM) developed by Symbl.ai, architected specifically for the nuances of human conversation. Unlike general-purpose LLMs, Nebula is trained on massive datasets of multi-party dialogues, telephonic interactions, and video conferencing data, allowing it to excel in tasks like action item extraction, sentiment nuance, and intent detection without the 'hallucination' risks associated with standard creative models. In the 2026 market, Nebula positions itself as the core infrastructure for 'Conversation-as-a-Service,' providing low-latency inference for real-time applications. Its architecture supports RAG (Retrieval-Augmented Generation) out-of-the-box, enabling enterprises to ground model outputs in their own private knowledge bases. By focusing on the 'operational' side of conversation rather than just chat-style interaction, Nebula enables automated CRM updates, compliance monitoring, and real-time agent coaching with high precision. It is designed to be model-agnostic in its deployment, offering both cloud-based APIs and VPC deployment options for data-sensitive industries such as finance and healthcare.
Nebula is a specialized Large Language Model (LLM) developed by Symbl.
Explore all tools that specialize in extract action items. This domain focus ensures Nebula (by Symbl.ai) delivers optimized results for this specific requirement.
Explore all tools that specialize in analyze sentiment. This domain focus ensures Nebula (by Symbl.ai) delivers optimized results for this specific requirement.
Explore all tools that specialize in redact sensitive data. This domain focus ensures Nebula (by Symbl.ai) delivers optimized results for this specific requirement.
Explore all tools that specialize in sentiment analysis. This domain focus ensures Nebula (by Symbl.ai) delivers optimized results for this specific requirement.
Nebula uses a specialized decoder architecture to generate summaries without needing specific examples per industry.
Integrates diarization data directly into the LLM context to attribute actions to specific individuals.
Built-in identification and masking of sensitive entities (credit cards, SSN) before LLM processing.
Allows Nebula to query external knowledge bases while maintaining the flow of a conversation.
WebSocket-based model processing that provides partial results with sub-500ms latency.
Processes audio features (tone, pitch) alongside text transcripts to detect true sentiment.
Enterprise-level ability to adjust model weights based on industry-specific jargon.
Create a Symbl.ai developer account at console.symbl.ai
Generate your App ID and App Secret from the dashboard
Authenticate via POST request to retrieve a temporary JWT access token
Choose between the Async API for files or the Streaming API for real-time data
Define the 'Nebula' model parameter in your API request header
Configure the 'parameters' object to specify summarization or insight types
Upload a sample conversation file (MP3/WAV) to the /v1/process/audio endpoint
Poll the 'jobId' or set up a Webhook URL to receive the processed JSON response
Review the 'insights' and 'action_items' arrays in the returned JSON
Scale deployment using Docker containers or Symbl's managed cloud infrastructure
All Set
Ready to go
Verified feedback from other users.
"Highly praised for its specialized focus on conversation; developers prefer it over GPT-4 for structured data extraction from audio."
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