# Outcomet Outcomet helps B2B product teams bring customer feedback together, decide what to work on next, and keep the reasons linked to the work they ship. [See an example](https://outcomet.app/use-cases#prds) · [Try Outcomet](https://inside.outcomet.app/signup?ref=public_text) ## Explore - [Product](https://outcomet.app/product) - [Decisions](https://outcomet.app/product/decisions) - [How it works](https://outcomet.app/product#how-it-works) - [Docs and product model](https://outcomet.app/doc) - [Compare](https://outcomet.app/compare) - [Philosophy](https://outcomet.app/philosophy) - [About](https://outcomet.app/about) - [Examples](https://outcomet.app/use-cases) - [Pricing](https://outcomet.app/pricing) - [FAQ](https://outcomet.app/faq) - [Blog](https://outcomet.app/blog) - [Changelog](https://outcomet.app/changelog) - [AI tools & MCP](https://outcomet.app/product/mcp) - [MCP setup](https://outcomet.app/developers) Contact: andy@crew.outcomet.app # Customer feedback. Your next decision. Review customer problems with their sources. Record what to investigate, change or defer. The product overview follows a customer obstacle through its source, goal and capability to a proposed investigation. Nova prepares findings; people review the sources and make the call. [Try the workflow](https://outcomet.app/product#how-it-works) ## Feedback: find the problem in the feedback Review proposed problems beside the original customer words. Confirm the finding after checking its source. Send or forward email to the workspace feedback address, or capture feedback directly in Outcomet. Native Zendesk, HubSpot, Salesforce and Intercom connectors are planned. The source illustration explains capture, review, decision and customer follow-up; it does not connect external accounts or demonstrate automatic delivery. AI meeting-note tools such as Granola, Modjo, Fireflies.ai, Fathom, tl;dv and Otter.ai are examples of feedback sources. Relevant transcript excerpts can be captured manually; the strip does not advertise native sync. [Feedback](https://outcomet.app/product/signals) ## Decisions: make the call, keep the why Review a theme and its current learning. Choose the next step and record the rationale. The illustrative demo lets you compare Explore and Defer and edit the reason. [Decisions](https://outcomet.app/product/decisions) ## Product Discovery: test before you build Compare possible explanations for an observed obstacle. Inspect a research plan and observations before recording a limited finding. [Product Discovery](https://outcomet.app/product/discovery) ## Product Strategy: link the goal to the problem Trace an activation goal to a customer obstacle and a proposed investigation. A direction records intent; it is not evidence of improved activation. [Product Strategy](https://outcomet.app/product/strategy) ## Product Capabilities: find the earlier decisions Open a capability and follow its linked problems and recorded reasons. Missing history remains unknown. [Product Capabilities](https://outcomet.app/product/capabilities) ## Docs: how the parts fit together The original two-loop graphic explains how goals and customer feedback connect through product capabilities. The diagram and workflow explanation now live in Docs. [Product model](https://outcomet.app/doc#model) · [Workflow](https://outcomet.app/doc#workflow) The website demos use synthesized scenarios, not private customer quotations or measured customer results. They are interactive illustrations and never submit changes to the application. Playback starts only when requested and stops before the final save. Actual light/dark product-preview screenshots appear on the Decisions and Docs pages. Open a screenshot to inspect its original. Their fictional preview example ends at a saved Explore decision; investigation work and later delivery were not demonstrated in that recording. [Recorded preview](https://outcomet.app/doc#preview) · [Customer examples](https://outcomet.app/use-cases) # Product examples: prioritize requests, test a direction, recover context Prepare priorities, test a direction and recover decision context. Keep delivery in your existing tools. Illustrative scenarios drawn from product-team interviews. Details and visuals are synthesized examples, not customer case studies or measured results. ## Decide which customer problems to tackle next [Open this example](https://outcomet.app/use-cases#prds) Sales has feature requests. Customer success has call notes. Support has a queue of recurring issues. You need to take a clear recommendation into planning without rebuilding the customer story by hand. ### Review proposed problems with their sources Add the relevant call notes and tickets. Nova, Outcomet’s AI, proposes problems and cites the original wording. You confirm, reuse or reject each proposal. ### Find the problem behind the requests In the illustration, several requests point to setup friction. Review them together in a theme, with the possible causes, options and unanswered questions, before committing to a bulk-edit feature. ### Take a recommendation into planning Record what to investigate, validate, build, fix or defer. Keep the evidence and your rationale with that decision, ready for the next person who asks why. ### What you take away A recommendation you can explain with customer evidence, including what you are leaving for later. [See how decisions keep their evidence](https://outcomet.app/product/decisions) ## Turn an activation goal into a testable plan [Open this example](https://outcomet.app/use-cases#discovery) Leadership wants more new users to get started. A guided first-run experience sounds promising, but you still need to establish where users get stuck and whether guidance would help. ### Make the goal concrete Define the first useful action you want users to complete. Bring in relevant feedback and research so the goal connects to a customer problem you can investigate. ### Test the assumption before committing Explore where users stop. If unclear guidance is a plausible cause, record that hypothesis and plan a test with a solution concept or prototype. ### Bring the findings back to the decision Keep observations separate from interpretation. Use the updated learning to choose a build direction, try another test or defer the idea, with the goal still visible. ### What you take away A specific research question and a next decision connected to the activation goal. [See how research informs the next step](https://outcomet.app/product/discovery) ## Understand why a product area changed [Open this example](https://outcomet.app/use-cases#fix) You take over a product area. The tickets are closed and the old whiteboard is out of date. Before changing the same behaviour again, you need to know what shipped and which decisions led to it. ### Start from the product area Outcomet groups connected delivery changes around the parts of your product they affect. Open the area’s history and follow a change back to its delivery source. ### Read the decision in its original context Where a decision was recorded and linked, review its rationale, scoped problems and the learning available at the time. Missing reasoning stays a gap to investigate. ### Make the next call with that context Compare the earlier assumptions with today’s customer evidence. Decide what to keep, revisit or investigate, and record your reasons for the team that follows. ### What you take away A handover grounded in product history and recorded decisions, with open questions made explicit. [Explore product areas and their history](https://outcomet.app/product/capabilities) # Plans for your team and AI usage Choose a plan by the number of people working in Outcomet and the AI usage you need. | Plan | Introductory EUR / month | Regular EUR / month | Admin/Editor seats | Workspaces | Agent Work / month | |---|---:|---:|---:|---:|---:| | Starter | €89 | €99 | 2 | 1 | 500 | | Team | €289 | €339 | 5 | 3 | 2,000 | | Organization | From €599 | From €749 | 25 | 10 | 5,000 | Approximately 10% off Starter, 15% off Team and 20% off Organization. Percentages are rounded. Unlimited Viewers. Introductory monthly prices are shown alongside regular monthly prices. Organization is available by inquiry, not direct purchase; [contact Andy for a quote](mailto:andy@crew.outcomet.app). Contact Andy to confirm applicable taxes, annual billing, trial terms, and additional usage before subscribing. ## What is Agent Work? Agent Work is the name for AI usage included in your plan. It is used for tasks such as grouping feedback or preparing a draft. One request is not a promise of one complete workflow. ## What happens when I need more AI usage? Review usage in your account’s billing view. Contact Andy to confirm additional capacity and its price for your account before purchasing. ## Can I control usage? Admins can review the account’s remaining Agent Work in the billing view. Check your current allowance before starting more AI work. ## What counts as a seat? The included seats are for people who administer or edit the account. Viewer access does not use an Admin/Editor seat. Usage and workspace limits apply to the account’s plan. ## Can I try Outcomet before subscribing? Create an account to get started, or ask Andy about the trial terms available to you. The plan prices above are introductory monthly prices, shown alongside regular monthly prices. Organization is available by inquiry; contact Andy for a quote. [Current pricing](https://outcomet.app/pricing) · [Talk to Andy](mailto:andy@crew.outcomet.app) # Compare Outcomet with the tools you already use. Start with the decision you need to make. Compare how each tool handles customer feedback, the reasons behind a priority, and the connection to delivery. ## Jira and Jira Product Discovery See how each setup connects customer evidence, priorities, and delivery. [Learn more](https://outcomet.app/compare/jira) ## Linear Compare customer requests, product planning, and the context behind shipped work. [Learn more](https://outcomet.app/compare/linear) ## Productboard Compare feedback analysis, prioritization, and the record you keep after a change ships. [Learn more](https://outcomet.app/compare/productboard) ## Already have a process that works? Check the actual gap before adding another tool. Documents and your own AI setup may already handle what you need. [See an example](https://outcomet.app/use-cases#prds) # Outcomet and Jira Product Discovery: follow one decision through both. Jira Product Discovery connects customer insights to ideas and delivery in Jira. Outcomet connects customer feedback, decisions, and changes to the areas of your product they concern. Compare the work you need to do in each setup. ## What your Atlassian setup already does. Customer insights, prioritization, roadmaps, and links to delivery may already cover your needs. ## What to check in Outcomet. Open a product area and follow the feedback, decision, and shipped change. Check whether you can answer the same question as easily in your current setup. ## Compare a real example. Use the last priority you changed. Find the original customer evidence, the reason for the choice, and the work that followed. Compare the effort to keep that record current. [See an example](https://outcomet.app/use-cases#prds) Provider reference, reviewed 2026-09-07: https://www.atlassian.com/software/jira/product-discovery/features # Using Linear? Check where your decision context lives. Linear can connect customer requests to issues and projects. Outcomet brings customer feedback, product decisions, and related changes together. The useful comparison is what your team still has to collect and maintain outside its current setup. ## Start with what Linear already gives you. Review your customer requests, source links, projects, and planning setup. ## Find the missing work. Where do you compare the feedback, record the trade-off, and revisit the decision after release? ## Try the same decision in Outcomet. Compare the result and the upkeep, including any work needed to connect your tools. Check how you would link your existing work before adopting another tool. [See an example](https://outcomet.app/use-cases#prds) Provider reference, reviewed 2026-09-07: https://linear.app/docs/customer-requests # Outcomet and Productboard: compare the path from feedback to delivery. Productboard helps teams analyze feedback, prioritize features, and communicate plans. Spark also works with customer feedback and product context. In Outcomet, follow the feedback, decision, and changes linked to a product area, then compare that experience with your current setup. ## Use the same customer problem. Start with the original comments rather than a prepared feature list. ## Find the reasons behind the priority. Check the source material, the goal, and the trade-off your team made. ## Look again after the change ships. Can a teammate find what changed, why, and what was learned next? How much work does keeping that record take? [See an example](https://outcomet.app/use-cases#prds) Provider reference, reviewed 2026-09-07: https://www.productboard.com/product/spark/ # Connect your AI tool to Outcomet Connect a compatible AI tool to read recent customer feedback and look up problems and initiatives in your Outcomet workspace. Ask for available customer wording and source references; missing evidence should remain explicit. Feedback reads support a recent sample; problem and initiative searches support topic queries. The MCP connection can also create feedback and, with contact write access, create or update contacts and companies. Available actions depend on your role and approved access. People review evidence and make product decisions. [Explore Outcomet MCP](https://outcomet.app/product/mcp), then [connect your client](https://outcomet.app/developers#connect). Use https://mcp.outcomet.app/mcp with browser sign-in through OAuth. The setup guide covers Claude, native HTTP and a stdio fallback for Codex, and the Antigravity proxy path. You need an Outcomet account with workspace access and permission to connect an external client. Start with the read-only first prompt in the guide. Do not assume every app feature is exposed through MCP. No general-purpose REST API or A2A endpoint is advertised by these website files. # Questions about using Outcomet ## What does Outcomet do? Outcomet helps product teams bring customer feedback together, decide what to work on next, and keep the reasons linked to the work they ship. ## Who is it for? Product teams that already have customer feedback and need to bring it together when choosing priorities. The useful starting point is a real decision your team is working on. ## What can I start with? Start with customer comments, support feedback, or interview notes relevant to that decision. Review related feedback together before expanding to more sources. ## Does AI make the decision? No. AI helps organize feedback and prepare drafts. Your team checks the sources, considers the trade-offs, and decides what to do. ## Do we need to replace our delivery tools? No. You can keep planning and delivering work in your existing tools. Check which connections are available for your setup before getting started. ## How is it different from Productboard or Jira Product Discovery? Both can help teams use customer feedback to set priorities. In Outcomet, you can follow a customer problem through the decision and the changes linked to it. Compare that workflow with what your team can already do today. ## Could we do this with documents and our own AI setup? You may already have a setup that works well. The question is whether collecting the feedback, keeping sources attached, and updating the shared record still takes work you want to reduce. ## What is a product area? A part of your product with its related feedback, decisions, and changes. For example, open sign-in to see what customers reported and what the team changed. The interface may call these areas ‘Capabilities’ or ‘Features.’ ## Do we need a new process? Start with one existing decision and the material you already use. Check whether Outcomet makes that work easier before expanding your setup. ## How much does it cost? See the pricing page for current plans, included AI usage, and additional charges.