The agent that works the way product managers do

Frequently Asked Questions

What is Productboard Spark?

Productboard Spark is a specialized AI agent that knows your product, customers, and market. It's purpose-built for product makers, including product managers, product leaders, and other collaborators. And it comes built into Productboard's platform, not as a separate solution.

Spark connects to all your organization's product data in Productboard, your customer feedback, strategy documents, competitive intel, product documentation, and product codebase — then does the analytical work and information synthesis that previously consumed most of a product manager's week.

If you're just getting started with Spark, you can look forward to putting it to use surfacing your highest-impact opportunities ranked by evidence, turning them into delivery-ready specifications through guided conversation, and measuring whether what you shipped actually drove impact.

How is Spark different from ChatGPT, Claude, Gemini, or other general-purpose AI tools?

General-purpose AI tools give you general-purpose outputs. You can copy/paste context, upload files, and jump through hoops to get more tailored results, but the responses are still heavily reliant on generic patterns and publicly available information. They're not sound for guiding your product decisions, setting strategy, or formulating the solutions you'll build next.

Productboard Spark provides benefits for product teams not available in generic AI solutions:

• Dependable outputs: Every Spark output is traceable to its source, with citations that take you to related feedback and documents. Generic AI produces plausible-sounding outputs, often disconnected from underlying sources and your organization's data.
• Persistent context: Spark knows your product, customers, and market from day one, and that knowledge compounds over time. Generic AI requires re-uploading context to every new project or session.
• Deep collaboration: Spark is multiplayer by default. Specs can be co-edited in real time by PM, designer, engineer, and Spark! Generic AI remains fundamentally single-player.
• Guided workflows: Spark has PM best practices baked in — opportunity discovery, feedback analysis, spec authoring, and impact measurement. Spark is equipped to take you through guided workflows, or execute on complex product tasks, while always keeping you in the driver seat. With generic AI you always start from a blank prompt window.
• Org knowledge: Knowledge in Spark compounds at the organization level and survives every personnel change. In generic AI, it lives in individuals' chat histories.

How is Spark different from using Claude Code or Cursor for product management tasks?

If you've already used terminal or IDE-based tools like Claude Code or Cursor to carry out product management tasks, then you'll have a taste for what's possible when you have an agent that can tap into additional context to produce more tailored outputs.

Productboard Spark offers the most popular capabilities of agentic harnesses employed by product managers, but does so in a collaborative environment, and without the maintenance headaches:

• Built for collaboration: Spark is built for many colleagues and agents collaborating on the same documents and product data in real-time.
• Persistent context: Spark knows your product from day one. DIY agent context is scattered across markdown files on your harddrive, GitHub repos, and systems that drift over time.
• Guided workflows: Spark ships purpose-built workflows for product work. With a DIY agent, you develop and maintain every workflow yourself.
• Dependable outputs: Every Spark output is traceable to its source. DIY agents often lack source tracing, and hallucination risk grows at scale.
• Org knowledge: Knowledge in Spark compounds continuously, growing from one initiative to the next. For DIY agents, it departs with the PMs who built it.

Above all, Productboard Spark gets you to value in hours, not weeks or months, with a dedicated system that frees you to focus on your own product — not AI infrastructure administration.

What data sources does Spark connect to? Does Spark support MCP?

Productboard's platform supports a wide variety of integrations for centralizing feedback, segmenting customers, collaborating with colleagues, and pushing prioritized features into delivery.

Productboard's customer feedback engine categorizes trends & findings (from Zendesk, Gong, Intercom, Slack, G2...) that Spark surfaces as new opportunities, and employs when replying to queries about customer needs.

Spark is also able to enrich opportunities it identifies with customer data synced from Salesforce.

Spark's deep product knowledge is based in part on codebase analysis enabled by a GitHub integration, as well as indexing of public product documentation and help articles.

Spark also supports MCP connectors, allowing you to connect to solutions with MCP servers — including Amplitude, Pendo, Hex, Linear, Notion and more. Product teams can query product analytics, retrieve documentation, update tasks, and sync insights across tools by way of natural language prompts.

When submitting a prompt to Spark, you can easily load in context from specific documents in connected systems like Confluence, Notion, and Google Drive.

Spark ingests additional publicly available data — such as competitor pricing pages, product reviews, and feature announcements, for competitive intelligence — with no connected data sources required.

What models are used to power Productboard Spark?

Spark is powered by large language models from Anthropic and OpenAI for natural language processing and reasoning. Productboard's AI infrastructure also includes Amazon Bedrock and Google Vertex AI.

For more information, please review Productboard’s list of subprocessors.

What data does Spark use? Is it used to train AI models?

Your data is not used to train or improve external AI models.

Spark accesses your workspace documents, customer feedback, product data, connected integrations, and associated data to generate context-aware responses.

• Workspace documents — product strategy, product briefs and specifications, product documentation, personas, pricing & packaging, competitor info, and any other documents in your workspace
• Customer feedback — all feedback in your workspace which may include feedback brought in by integrations, feedback forms, and submitted on Portals
• MCP Connectors — data from connected tools like Amplitude, Pendo, Hex, Linear, and Notion
• Context integrations — documents from Google Drive, Confluence, Notion, and uploaded PDFs, CSVs, and markdown files
• Publicly available information about your company, product, and competitors, gathered automatically when you first enable Spark in your workspace
• Product data in your workspace — including data associated with entities such as objectives and key results, initiatives, releases, products, components, features, and subfeatures

For more information, see Productboard's AI Terms.

How do I get started with Productboard Spark?

If you're new to Productboard, you can start a trial of Productboard (with Spark as built-in agent) which includes a defined number of Spark credits:

• When you first get started, Spark draws upon publicly available information about your product and business to give you a head start in defining the canonical knowledge Spark will use moving forwards. You can continue to refine this information with help from colleagues, and from Spark.
• Once you have some context in the system, some core product data added (like objectives, initiatives, or feature ideas), and feedback imported from other tools, you can expect to immediately begin getting value from Spark. No weeks of defining taxonomies or data hygiene just to get started.

Existing Productboard customers have an accelerated path. The organizational memory you've already built in Productboard is the foundation Spark builds upon:

• An admin can enable Spark for your workspace. A built-in Spark skill guides admins through workspace configuration.
• Spark also onboards makers across the workspace when they first return to Productboard after Spark has been enabled.
• Connect additional integrations like GitHub for codebase analysis or enable indexing of your public product documentation. Spark will be able to apply deeper knowledge about your product to answer queries and author high quality product docs.

How do I know I can trust Spark's outputs to inform important product decisions?

For many types of responses, Spark provides outputs that are traceable to underlying sources, such as internal documents, customer conversations, product data, or external resources. These are conveniently represented as citations for your review. That connection to the source is what gives you confidence when using Spark to power core product work.

Spark was designed to guide, inform, and support execution on core product tasks. Nonetheless, it's the product makers who wield Spark that own the judgement calls, make the final decisions, and apply their product sense to deliver the right solutions.

Is Productboard Spark a replacement for my development issue tracker like Jira or Linear?

No, Productboard (and its built-in agent, Spark) are for helping product teams decide what to build, how to build it, and whether it worked.

It's an agentic product system that supports core product management work spanning market signals, customer feedback, product strategy, product discovery, product prioritization, release planning, roadmapping, and post-launch evaluation. It also supports the product operations that underly all of these core product processes.

Prioritized product work in Productboard can be pushed into integrated delivery planning tools.

Product specifications authored with Spark represent a source of truth for the solution being delivered. They can be accessed by coding agents like Claude Code, Codex, and Cursor using Productboard's MCP server.

Have additional questions? Send us an email at hello@productboard.com