Build with Symbl.ai
Harness the power of machine learning pre-trained to model the nuances of human conversations. Apply intelligence generated out of the box, customize the model outputs to your preferred vocabulary, and build new text and audio classifiers with just a few examples.
Speech to Text
Integrate the most accurate real-time and asynchronous speech recognition API that is built for unstructured human conversations and also enables you to add intelligence with a single API call.
Automatically detect and model conversation topics with no manual labeling or hierarchies required. View sentiments associated with topics, and view aggregate summaries of topics as “abstract topics.”
Generate talk-to-listen ratios, words per minute, talk time, overlap along sentence level and topic based sentiments to enhance user or agent conversation analytics.
Get audio and video summarization that is abstractive, descriptive and context-aware with a single API call. Built with almost 5x lesser inference time and superior understanding.
Keyword, Phrase & Intent Detection
Integrate keyword and phrase matching and intent detection powered by semantic similarity and machine learning – both in real-time in less than 400 milliseconds and via batch / asynchronous requests.
Leverage pre-built UI and components for voice, audio, and text data. Easily customize UI look and feel, and integrate into your application or workflow.
Building with Symbl.ai
The Programmable Platform for
Robust and Comprehensive APIs and SDKs
Superior Conversation Intelligence with Context
Real-time, Streaming and Async Channels
Generate Real-Time Intelligence
SOC 2 Type 2, PCI and HIPAA Compliant
Enterprise Deployments and Support
Pre-trained and Custom Models
Free Forever Plan for Real-Time and Async Use Cases
Why Build with Symbl.ai?
We are your partner in accelerating your journey to analyze large volumes of calls, recordings, meetings and to build differentiated experiences for your customers. Symbl’s conversation intelligence platform removes the pain of building, maintaining and scaling the machine learning infrastructure you need to understand conversations contextually. Scale faster and lower tech debt by eliminating the overhead of MLOps and Data Engineering from your product and engineering investments.