Nugget AI vs Supernova: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Nugget AI and Supernova — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Nugget AI

Freemium

Real-time customer insights platform that turns discovery conversations into actionable insights for product managers.

Real-time Insight Capture: Captures and synthesizes observations from customer interviews and conversations as they happen, enabling immediate review and action by product teams.
Automated Nugget Extraction: Identifies and surfaces concise, high-value statements or 'nuggets' from raw transcripts and notes to reduce manual summarization.
Centralized Feedback Repository: Stores searchable customer feedback and discoveries in a single workspace so PMs can track themes and historical context across interviews.
Theme and Trend Detection: Aggregates and highlights recurring user problems, feature requests, and sentiment to support evidence-based prioritization.
Collaboration and Sharing: Enables teams to tag, comment on, and share extracted insights with stakeholders for faster alignment and decision-making.
Integrations and Workflow Support: Connects to common meeting, note-taking, or product tools to bring discovery data directly into product workflows (e.g., tickets, roadmaps, research docs).
Real-time processing and delivery of customer insights
Transforms customer discovery into actionable recommendations
Focus on workflows and needs of product managers

Customer Interview Synthesis: Record and automatically extract key findings from user interviews, reducing post-interview manual work for PMs and researchers.
Prioritization Evidence: Surface recurring user pain points and feature requests to inform roadmap prioritization and product decisions.
Stakeholder Reporting: Generate concise insight summaries and trend reports to communicate customer learnings to executives and cross-functional teams.
Onboarding New PMs: Provide a searchable history of customer discoveries so new team members can quickly learn validated user problems and prior research.
Continuous Discovery: Maintain an ongoing pipeline of synthesized user feedback so teams can monitor changes in needs and sentiment over time.
Research Handoff: Turn qualitative research into actionable, tagged nuggets that can be converted into experiments, tickets, or product requirements.
Synthesizing customer discovery interviews into prioritized insights for PMs
Rapidly converting user feedback into action items and product decisions
Providing an insights dashboard to inform roadmap and feature prioritization

Paid

An encrypted Iceberg data lake with a built-in engine and MCP endpoint, so Claude and Codex can query every tool your company uses.

MCP Endpoint for Claude and Codex: Point any MCP-speaking assistant at mcp.supernova.ai/mcp and every synced table becomes queryable in natural language.
Encrypted Iceberg Lake: Open Apache Iceberg tables in object storage with table-level encryption, so the data stays in a portable open format you control.
Zero-Copy Connections: Any engine that speaks Iceberg can read the lake directly, avoiding a second copy of your warehouse.
Time Travel: Every table retains version history, so you can query the state of your data as of any earlier point.
Built-In Frontier Models: Ask a question or describe a dashboard in plain language and Supernova generates the models and visualisations without a data team.
TypeSQL: Schema-aware SQL that autocompletes across joins and type-checks before execution, catching errors the way a typed language would.
Single-Binary CLI: One command-line tool connects sources, runs queries, tails live table changes and registers the MCP endpoint with Claude Desktop, from a laptop or CI.
Git-Backed Dashboards: Models and dashboards are readable and writable through Git, putting analytics artefacts under normal version control.

Conversational Revenue Analysis: Ask Claude which customers churned last quarter and why, with the answer computed over live Stripe and HubSpot tables.
Warehouse Cost Reduction: Replace a multi-vendor pipeline-plus-warehouse stack with one usage-billed platform, which the vendor illustrates as $5,640/mo dropping to $540/mo for a hardware company.
Dashboards Without a Data Team: Describe the dashboard you want in a sentence and have the models and charts generated for you.
AI-Native Data Access Layer: Give internal agents a governed, encrypted single endpoint for company data instead of per-tool API integrations.
Auditing Historical State: Use table version history to reconstruct what the numbers looked like before a pricing or schema change.
CI-Driven Data Workflows: Drive connections, queries and change tailing from pipelines using the single CLI binary.