Deepnote

What Deepnote does

Deepnote is an AI workspace for data professionals, designed to simplify data exploration, accelerate analysis, and quickly deliver actionable insights for teams. It unifies data workflows through an integrated semantic layer, preparing data for advanced AI applications. Users can leverage an AI data copilot to chat with data, create charts, write code, or turn AI notebooks into data dashboards or apps. The platform combines data, SQL or Python code, and visualizations side-by-side on a flexible canvas, enhanced with cutting-edge AI reasoning models. Deepnote supports building and running deterministic or non-deterministic agents, creating and hosting interactive data apps, exploring and storing knowledge in collaborative notebooks, exploring data in data tables, running pipelines and jobs on a schedule, turning analyses into assets for teams, and training, serving, and evaluating models.

Products

• Deepnote AIAn AI data copilot that allows users to chat with data, create charts, write code, and turn AI notebooks into data dashboards or apps. It offers code generation, editing, explanation, and context-aware code suggestions. It is multi-provider, supporting models like Anthropic's Claude and OpenAI's GPT families.
• NotebooksCollaborative, AI-powered notebooks for data exploration, knowledge storage, complex calculations, and insightful visualizations, supporting SQL and Python environments.
• Data appsInteractive applications, dynamic reports, and dashboards built with a drag-and-drop interface and pre-built components, allowing real-time collaborative reporting and tailored dashboards.
• AgentsTools to build and run deterministic or non-deterministic agents for tasks like fraud detection or revenue analysis.
• PipelinesFunctionality to run pipelines and jobs on a schedule.
• ModelsEnvironment to train, serve, and evaluate machine learning models, with support for hyperparameter tuning and deployment options.
• SpreadsheetsTools to explore data in data tables.
• DashboardsFunctionality to turn analyses into assets for teams.

Key capabilities

• AI data copilot
• AI code completion
• Auto AI for code generation, execution, and debugging
• Deepnote Agent for multi-step data tasks
• Real-time collaboration
• Integrated semantic layer
• 100+ integrations with data sources and tools
• SQL and Python environment
• Interactive visualizations
• Data apps and dashboards
• Scheduled runs
• Terminal access
• Variable explorer
• Import and export as .ipynb
• Folders for project organization
• Notebook API
• White-labeled notebooks
• Commenting
• Public projects
• Revision history
• Access controls
• Shared datasets
• Sync notebooks with Git
• Scalable compute power (Basic, Plus, Performance, High memory, GPU machines)
• Shared environments
• Public and private Docker images
• Pay-as-you-go machines
• SOC 2 Type II compliance
• Encrypted data
• Static IPs
• HIPAA Compliance
• SSH Tunnels
• Audit logs
• Single sign-on (SSO)
• Directory sync
• Federated warehouse authentication
• Single-tenant deployments
• Private cloud (on-premise) options
• Live chat support
• Custom contract and invoicing
• Unified billing across multiple workspaces
• Drag-and-drop interface for app creation
• Pre-built components for data apps
• Granular access control for data apps
• ML-ready environment for model development, optimization, and deployment
• Support for training and fine-tuning models
• Deployment and monitoring capabilities for models
• Scalable hardware including GPUs
• Support for top ML frameworks
• Reusable components for ETL processes, analytics, and trusted metrics
• REST API for programmatic management of projects, notebooks, and workflows

Use cases

• Data exploration
• Accelerated data analysis
• Delivering actionable insights
• Building and running agents
• Creating and hosting interactive data apps
• Knowledge storage in collaborative notebooks
• Exploring data in data tables
• Running pipelines and jobs on a schedule
• Turning analyses into assets
• Training, serving, and evaluating models
• Quick explorations
• ETL pipelines
• Data catalog
• Knowledge base
• Sales report generation
• Customer 360 analysis
• Churn prediction model development
• Lead scoring
• Syncing Salesforce to Notion
• A/B test evaluation
• AWS cost analysis
• NPS analysis
• Fine-tuning LLaMA 7B
• Protein visualization
• Forecasting sales pipeline
• Fraud detection
• Revenue breakdown analysis
• Real-time collaborative reporting
• Model development, optimization, and deployment
• Hyperparameter tuning
• Data preprocessing
• Prototyping and experiments
• Interactive documentation
• Brainstorming sessions
• Proteomics research
• Single-cell spatial proteomics
• On-demand data applications

AI approach

Deepnote provides an AI workspace for data professionals, simplifying data exploration, accelerating analysis, and delivering actionable insights. It offers an AI data copilot to chat with data, create charts, write code, and turn AI notebooks into dashboards or apps. The platform uses AI reasoning models to enhance SQL and Python code and visualizations. Deepnote AI is multi-provider, allowing users to choose from models like Anthropic's Claude and OpenAI's GPT families, or let Deepnote pick the best model automatically. It assists with code generation, editing, explanation, and delegating multi-step data tasks to a sidebar agent.

Tech named: Deepnote AI, AI data copilot, AI reasoning models, Anthropic's Claude, OpenAI's GPT families, GPT-5.5, Sonnet 4.6, LLaMA 7B

Industries served

• Software Development
• Financial Services
• Healthcare
• Biotechnology

What it says sets it apart

• AI-powered workspace with integrated semantic layer for advanced AI applications
• AI data copilot for natural language interaction, code generation, and chart creation
• Unified data workflow combining data, SQL, Python, and visualizations on a flexible canvas
• Real-time collaboration features for teams of all sizes
• Seamless integration with 100+ data sources and tools
• Serverless GPUs with auto-shutdown for cost efficiency and zero quota delays
• Ability to transform notebooks into fully-fledged data dashboards or apps
• Robust security features including SOC 2 Type II, HIPAA compliance, encrypted data, and SSO
• ML-ready environment purpose-built for model development, optimization, and deployment
• Faster time to insight and value, improving data team productivity by 28%
• 23% less time spent on environment management due to maintenance-free cloud environment
• Plug-and-play setup for existing Jupyter notebooks with pre-installed deep-learning libraries
• Multi-provider AI supporting various LLMs like Claude and GPT families
• Ability to bring your own LLM for Enterprise users
• Customizable plans for individuals, teams, and enterprises, including an Education plan