An agent that knows your product, customers, and market from day one, and gets smarter every day after.
Spark’s specialized agentic workflows get you to clarity on the right solutions to build, in a fraction of the time.
AI-powered opportunity discovery
Review newly surfaced opportunities when you arrive back in Productboard, refreshed every week. Use Spark to explore the signals and initiate next steps.
Market signals enriched by strategic context
Customer feedback enriched by segment sizing
Competitive intel enriched by roadmap analysis
AI customer feedback analysis
Productboard performs statistically accurate analysis of your organization’s entire body of feedback — citations included.
Monitor trending feedback findings
Dive deeper into a customer need
Generate voice-of-customer reports
AI product specification
As delivery accelerates, stay ahead of the curve with an agent that employs deep understanding of your product to craft delivery-ready specs in less time.
Synthesize large amounts of context in minutes
Surface technical requirements using codebase analysis
Pull specs into Claude Code, Codex, or Cursor via MCP
AI product launch analytics
Spark connects to your data sources to evaluate product analytics and new customer feedback in relation to the original objectives & success metrics.
Tap into quantitative product usage data
Incorporate post-launch customer feedback
Plan fast-follows and future investments
AI for product release notes
Write it once. Then instantly adapt for many purposes, from team updates to external comms.
Prepare status updates
Generate release notes
Draft enablement assets
When Spark makes an update, you can easily review and accept its changes inline. Maintain full control with document version history.
The product brief was an instant hit. My boss took it to our board and we got a full green light.
I saved 1 week of work in just 90 minutes using Spark and successfully delivered the output to my executive team.
The benefit of Spark is context-aware AI that has continuity. In other LLMs you have more of a siloed experience.
I'm already super happy that this whole discovery stage is so much smoother and faster and more efficient, and also pretty accurate.
As time has gone by and there's more and more content in these documents, it's able to provide better and better quality critiques and feedback and suggestions and ideas. I've been really genuinely impressed at how useful it is.
Spark sees around corners that I'm not really paying as much attention to.
The product brief was an instant hit. My boss took it to our board and we got a full green light.
I saved 1 week of work in just 90 minutes using Spark and successfully delivered the output to my executive team.
Specialized agentic workflows with embedded best practices allow Spark to surface things you might have missed and provide guidance tailored for PMs.
Spark knows your product, customers, and market. It connects to your data, your documents, even your codebase, to provide the most relevant outputs. Above all, this knowledge compounds over time.
Every insight, every recommendation links back to real pieces of feedback, a real source document, so you can stand by decisions made with Spark.
Unlike single-player generic AI tools, Spark is built for collaboration, shared knowledge, and saved skills that allow everyone to gain maximum benefit from AI.
Maintaining DIY agent stacks soon becomes a full-time job. Productboard’s agentic system provides the specialized agents, orchestration and harness, model evals, token expenditure optimization, and more.
Centralize customer feedback, tap into external docs, analyze your codebase, send specs to delivery agents, evaluate product analytics, and more.
Maximize productivity in your existing product workflows with easy-to-understand tutorials.
Spark is a specialized product management agent that accelerates some of the most time-consuming aspects of taking an initial idea to a well-defined specification.
Product managers and other collaborators can interact with Spark through a conversational interface to explore product ideas, inquire into customer needs (based on actual customer requests and feedback), and generate documents like product briefs, product requirement documents (PRDs), user research reports, competitive intelligence, product launch materials, and more.
As an AI agent, Spark can create new documents and edit existing documents. These documents can then be collaborated over with colleagues and further refined with help from AI. Moving forward, they can then be used as context in future AI prompts. For example, you could prompt Spark to generate a product specification based on context drawn from your Product strategy doc, User personas doc, and PRD template.
Unlike generic AI tools:
Jobs are guided, step-by-step workflows in Spark that help you complete complex PM tasks with expert guidance. Instead of starting with a blank prompt, Jobs lead you through proven methodologies - asking the right questions at each stage to produce high-quality, strategy-aligned deliverables.
Examples of jobs available within Spark:
Two key benefits:
Each Job walks you through multiple steps, connects to your product context and customer feedback, and creates polished documents you can share with stakeholders immediately.
Spark connects to external tools in two ways:
Integrations let you tap into context residing in platforms like Confluence, Google Drive, and Notion, right from within a Spark chat.
Connectors (powered by the Model Context Protocol) let you interact with tools like Amplitude, Hex, Pendo, and Linear within Spark chats using natural language — querying data, searching docs, or creating tasks without switching apps. You can also set up custom connectors for other tools.
Spark respects your existing permissions in connected platforms and only accesses what your account is authorized for.
More info: Connect your existing tools to Spark
Spark is powered by large language models by Anthropic and OpenAI. If you choose to use Productboard’s AI capabilities, you agree to Anthropic, OpenAI, and Amazon Bedrock being subprocessors of your data. For more information, please review Productboard’s list of subprocessors.
Spark can access any documents you provide in your workspace in addition to customer feedback notes and associated data. In the near future, Spark will be able to access additional product data in your workspace to carry out more actions and provide new types of responses.
Spark follows Productboard's standard security and privacy practices. Your data remains within your workspace and is not used to train external AI models.
For more information, see Productboard's AI Terms.
Have additional questions? Send us an email at hello@productboard.com
