The Product Leader's Toolkit

The RICE scoring model helps product teams objectively rank features by multiplying Reach, Impact, and Confidence, then dividing by Effort. Product leaders use RICE when they need a data-driven prioritization approach that reduces bias and politics in roadmap planning discussions. Score multiple features side-by-side and instantly see a ranked priority list you can share with stakeholders.

ICE scoring is the fastest prioritization framework available — three simple scores multiplied together give you a ranked backlog in minutes. Product leaders use ICE for rapid triage when speed matters more than precision, such as sprint planning or hack-week idea selection. Ideal for teams that find RICE too heavyweight for their current process maturity.

WSJF (Weighted Shortest Job First) is the prioritization framework used in SAFe and Lean enterprises. It balances business value, time criticality, and risk reduction against job size to identify features that deliver the most value per unit of effort. Product leaders in scaled agile organizations use WSJF to align multiple teams on the highest-value work during PI planning.

MoSCoW is a categorical prioritization method that forces stakeholders to commit to what's truly essential versus nice-to-have. Product leaders use MoSCoW during scope negotiations and release planning when they need clear boundaries between must-ship and can-wait. Particularly effective for cross-functional alignment because the categories are intuitive for non-technical stakeholders.

The weighted scoring model lets you define your own prioritization criteria — strategic alignment, customer impact, revenue potential, technical feasibility — and assign relative weights. Product leaders use this when standard frameworks like RICE feel too rigid and they need scoring that reflects their unique strategic context. Build a repeatable, transparent scoring rubric your entire organization can adopt.

The Kano model classifies features by their relationship to customer satisfaction: must-be features prevent dissatisfaction, performance features scale linearly with investment, and attractive features create delight. Product leaders use Kano to avoid over-investing in table-stakes features and to identify the delighters that differentiate your product. Essential for customer-centric roadmap discussions.

Not sure whether to use RICE, ICE, MoSCoW, WSJF, or Kano? This diagnostic quiz asks about your team size, process maturity, data availability, and decision-making style to recommend the best-fit prioritization framework. Product leaders use this when establishing or upgrading their team prioritization process — it saves weeks of framework evaluation and pilot testing.

The value vs effort matrix is the simplest prioritization tool that every PM should know. Plot features by business value and implementation effort to instantly identify quick wins (high value, low effort), big bets (high value, high effort), fill-ins (low value, low effort), and items to avoid (low value, high effort). Drag features on the interactive chart to adjust scores in real time.

Based on Anthony Ulwick's Outcome-Driven Innovation framework, this calculator uses the formula Opportunity = Importance + max(Importance - Satisfaction, 0) to identify underserved customer needs. Plot opportunities on a landscape chart to visualize which areas are over-served versus under-served, then focus your roadmap on the highest-opportunity gaps.

Add features with value and effort scores to generate a visual 2x2 matrix. Features are automatically categorized into Quick Wins (high value, low effort), Big Bets (high value, high effort), Fill-Ins (low value, low effort), and Money Pits (low value, high effort). A faster alternative to RICE when you need a visual prioritization artifact.

Net Promoter Score is the gold standard for measuring customer loyalty and predicting growth. This calculator takes your survey responses, computes your NPS, and benchmarks it against SaaS industry averages by company stage and segment. Product leaders use NPS to track satisfaction trends, set customer health OKRs, and build the case for retention investments.

The Sean Ellis test asks users how they would feel if they could no longer use your product. If 40% or more say "very disappointed," you have product-market fit. Product leaders use this metric to determine whether to double down on growth or pivot back to discovery. This calculator gives you the PMF percentage plus actionable guidance based on where you fall on the spectrum.

Running experiments without statistical rigor wastes engineering resources and leads to false conclusions. This calculator handles both pre-test sample size planning and post-test significance analysis. Product leaders use it to ensure experiments have adequate power before launch and to make confident ship/kill decisions when results come in, reducing the risk of shipping features that don not actually move metrics.

MRR and ARR are the foundational metrics of every SaaS business. This calculator breaks down your recurring revenue into new, expansion, contraction, and churned components so you can see exactly where growth is coming from. Product leaders use MRR decomposition to identify whether growth is driven by acquisition or expansion and to spot early warning signs in contraction trends.

Customer Lifetime Value tells you how much revenue a customer generates over their entire relationship with your product. This calculator computes LTV from your ARPU, gross margin, and monthly churn rate, then benchmarks it against enterprise, mid-market, and SMB SaaS standards. Product leaders use LTV to justify retention investments, set acquisition budgets, and segment customers by value.

Churn is the silent killer of SaaS businesses. This calculator converts between monthly and annual churn rates and benchmarks your performance against SaaS companies at different stages — early-stage, growth, and mature. Product leaders use churn analysis to set retention goals, identify at-risk segments, and quantify the revenue impact of reducing churn by even a single percentage point.

LTV:CAC ratio is the single most important unit economics metric for SaaS businesses. A ratio of 3:1 or higher indicates healthy economics; below that, you are spending too much to acquire customers relative to their value. Product leaders use this metric to evaluate go-to-market efficiency, negotiate marketing budgets, and determine whether the business model is sustainable at scale.

The SaaS Quick Ratio measures the efficiency of your revenue growth by comparing incoming MRR (new + expansion) against outgoing MRR (contraction + churn). A Quick Ratio above 4 indicates highly efficient growth; below 1 means the business is shrinking. Product leaders use this metric to evaluate whether growth is sustainable or if a leaky bucket problem needs urgent attention.

The Rule of 40 states that a healthy SaaS company should have its revenue growth rate plus profit margin equal or exceed 40%. It measures the trade-off between growth and profitability that every scaling company faces. Product leaders use this benchmark in board discussions and strategic planning to evaluate whether the company is balancing investment in growth with path-to-profitability expectations.

Stop switching between individual calculators. The SaaS Unit Economics Dashboard takes your core inputs (MRR, customers, churn, ARPU, CAC, margin) and auto-calculates 12 key metrics with color-coded health indicators based on industry benchmarks. Get a single-screen view of your business health that you can screenshot for board decks or share with your finance team.

Measure how quickly users are adopting new features after launch. Input your total active users and feature users to calculate adoption rate, then track velocity over time. Compare multiple features side-by-side to identify which launches are succeeding and which need intervention. Includes SaaS benchmarks so you know if your adoption rate is typical or needs attention.

Input number of attendees, average salary, and meeting duration to see the real cost of your meetings. Shows per-meeting cost, annual projection, and fully-loaded costs. A reality check for meeting-heavy organizations that helps justify async alternatives.

Input your cost structure, target margin, and competitor pricing to generate recommended pricing tiers (freemium, pro, enterprise). The tool calculates break-even points, suggests price anchoring, and flags common pricing mistakes. Useful for new product launches and pricing reviews.

TAM, SAM, and SOM quantify your total addressable market, serviceable market, and realistic obtainable market. This calculator supports both top-down (industry data) and bottom-up (customer × ACV) methodologies. Product leaders use market sizing to validate new product bets, prepare investor materials, and set realistic revenue targets that align executive expectations with market reality.

Knowing your burn rate and remaining runway is critical for strategic decision-making at startups and growth-stage companies. This calculator computes net burn from your revenue and expenses to determine how many months before you need additional funding. Product leaders use runway calculations to scope roadmap ambition, time fundraising activities, and make hiring decisions with financial confidence.

Not every feature is worth building. This calculator compares the development cost of a feature against its projected revenue impact to determine ROI. Product leaders use feature ROI analysis in roadmap prioritization to ensure engineering investment flows toward the highest-value opportunities and to justify development resources to finance and executive leadership.

The AARRR (Pirate Metrics) framework maps your full user journey across Acquisition, Activation, Retention, Revenue, and Referral. This calculator helps you measure conversion rates at each stage and identify the biggest drop-off points. Product leaders use funnel analysis to focus team efforts on the stage with the highest leverage — fixing activation is often 10x more impactful than adding new acquisition channels.

Every product team needs a single North Star Metric that captures the core value delivered to customers. This quiz analyzes your product type, revenue model, company stage, value driver, and usage frequency to recommend the best North Star from a database of proven metrics with example companies. Product leaders use this to align their entire organization around one metric that drives both customer value and business growth.

The Founder Fit Assessment helps aspiring SaaS founders find the best product idea based on their technical skills, available time, revenue goals, risk tolerance, domain interest, and tech stack familiarity. It scores 60+ validated ideas from the SaaS Idea Lab and presents ranked matches with specific reasons each idea fits or gaps to watch out for.

The PLG Readiness Score assesses your product-led growth motion across 7 critical dimensions: Self-Serve Onboarding, Time-to-Value, Natural Virality, Usage-Based Pricing, Product Analytics, Expansion Revenue, and Product-Qualified Leads. Answer 21 questions to get an overall score out of 100, a visual breakdown by dimension, and personalized recommendations for improving your weakest areas. Ideal for founders and growth PMs evaluating whether their product is ready for a PLG motion.

The Startup Idea Validator helps founders and product managers assess whether a business idea is worth pursuing. Answer 10 structured questions about your target market, problem severity, competition, differentiation, and revenue model. Get a validation score out of 100 with a letter grade, plus a breakdown across five dimensions: Market Demand, Competitive Position, Feasibility, Revenue Potential, and Timing & Risk. Each score comes with specific, actionable recommendations.

The AI Startup Idea Generator matches you with validated SaaS ideas from a database of 90+ opportunities. Answer five questions about your technical skills, time commitment, budget, domain interests, and revenue goals. The algorithm scores every idea against your profile and presents your top matches with fit percentages, match reasons, key stats, and links to full playbooks with MVP specs, tech stacks, and go-to-market strategies.

Before building anything, smart product teams identify and prioritize their riskiest assumptions. This tool maps each assumption on a 2x2 matrix of risk vs uncertainty, then generates a prioritized validation test plan. Product leaders use assumption mapping during discovery to focus experimentation resources on the bets that could most damage the product if wrong.

Jobs-to-be-Done statements capture why customers hire your product in a structured format: situation, motivation, and desired outcome. This guided wizard walks you through each element to produce clear, actionable JTBD statements. Product leaders use JTBD to reframe product decisions around customer needs rather than feature requests, improving discovery quality and stakeholder alignment.

Not all roadmap items carry the same level of certainty. This scorer rates each item across value confidence, effort confidence, and strategic alignment to separate solid bets from speculative ones. Product leaders use confidence scoring during roadmap reviews to flag items that need more validation before committing resources, and to communicate risk levels to executives transparently.

Describe your product, select your company stage, team size, and goals. Compass generates a structured, context-aware roadmap with phased initiatives, milestones, risks, and success metrics. View your roadmap as a timeline, Now/Next/Later board, or detailed list. Refine with chat, score your roadmap, and export to share with stakeholders. Solo founders get lean two-phase plans. Enterprise teams get multi-track roadmaps with dependencies.

Build, edit, and share product roadmaps visually. Drag initiatives between phases, switch between timeline, Now/Next/Later kanban, and swimlane views. Import AI-generated roadmaps from Compass and refine them with full editing controls. Set impact, effort, confidence, status, and assignee on every initiative. Export as markdown or share via URL. Free tier includes 2 saved roadmaps and timeline view. Pro unlocks all views, unlimited saves, and shareable links.

Upload a PDF or PPTX presentation and get structured feedback from a CPO perspective. Scores across five dimensions: narrative, structure, data use, clarity, and audience alignment. Get actionable feedback items sorted by severity with "Fix This" buttons. Refine your deck with natural language chat or quick actions like sharpen narrative, add data points, simplify slides, or executive tone. Generate a polished PPTX from the improved outline in dark, light, or corporate themes.

Paste customer feedback from any source (NPS comments, support tickets, survey responses, app reviews) and get instant AI analysis. The synthesizer clusters feedback into weighted themes, scores sentiment at the theme level, identifies urgency, and generates prioritized action items. Handles up to 500 items per analysis. Supports CSV upload with automatic column detection.

Story points measure relative effort, not time. But sometimes you need a rough hours estimate for stakeholder discussions or capacity planning. This converter uses your team's historical velocity to calculate the relationship between story points and hours. Includes a Fibonacci reference table and a clear disclaimer that this is for rough planning only.

Overcommitting in sprint planning leads to missed deadlines and burned-out teams. This calculator accounts for real-world factors: individual PTO, team holidays, focus factor, and meeting overhead. Add each team member with their specific availability to get an accurate capacity number. Use historical velocity to translate capacity hours into a suggested story point commitment.

Technical debt is invisible until it cripples your delivery speed. This calculator quantifies the annual cost of tech debt in dollars based on team size, hourly cost, and the percentage of engineering time consumed by debt-related work. See the break-even point for remediation investment and get stakeholder-ready talking points that translate technical concepts into business impact.

How many PMs do you need? The answer depends on company stage, product complexity, and engineering team size. This calculator provides data-driven recommendations for PM-to-engineer ratios, designer ratios, and squad structure. It flags when your team is understaffed or overstaffed compared to industry benchmarks and suggests optimal team organization.

Generate production-ready PM documents in 30 seconds. 15 document types organized by weekly habit-builders (stakeholder updates, sprint reviews, meeting recaps), planning (PRDs, one-pagers, user stories, OKRs), launch (release notes, GTM briefs, experiment plans), and strategy (competitive analysis, exec updates). Fill in 3 fields and get a structured document ready to share.

Identify who matters most to your product decisions. Add stakeholders with their influence level, interest level, and stance (supporter/neutral/blocker). The tool renders a 2x2 influence-interest grid and suggests engagement strategies per quadrant: manage closely, keep satisfied, keep informed, or monitor.

Input your company mission, team name, and key focus areas. The tool generates structured Objectives and Key Results with measurable targets and suggested metrics. Produces OKRs you can bring directly to your quarterly planning session.

Add competitors and evaluation criteria with custom weights. The tool creates a weighted scoring matrix comparing features across competitors with totals, rankings, and percentage scores. Export the matrix to share in competitive analysis presentations.

Guided persona builder with fields for demographics, goals, frustrations, tools used, and day-in-the-life narrative. Generates a formatted persona card you can screenshot or copy. Replaces the typical scattered Google Doc persona with a structured, consistent format.

Input your feature changes (name, type, description) and generate release notes in three formats: developer changelog, customer-facing announcement, and internal stakeholder table. Copy to clipboard and paste into your preferred communication channel.

Enter your product name, problem statement, target users, and key features. The AI generates a complete PRD with overview, problem definition, goals, user stories, prioritized requirements, success metrics, and timeline. Copy the output as markdown and paste it into Notion, Google Docs, or Confluence. For custom templates, review, and refinement, upgrade to Forge.

Describe your feature and user personas, and the AI generates formatted user stories in "As a [persona], I want [action], so that [benefit]" format. Each story includes priority labels, acceptance criteria in Given/When/Then format, and implementation notes. Copy directly into Jira, Linear, or any backlog tool.

Enter your product, target market, pricing model, competitive advantage, and launch timeline. The AI generates a GTM brief with launch overview, target audience, value proposition, key messages, a launch plan table with owners and dates, and success metrics. Use it to align product, marketing, and sales before any launch.

Enter your sprint name, what went well, what did not, and goal status. The AI generates a structured retrospective with themed analysis, prioritized action items with owners and due dates, a two-sprint improvement plan, and team health indicators. Replaces the manual work of synthesizing retro discussions into actionable documents.

Create visual Opportunity Solution Trees following Teresa Torres' Continuous Discovery Habits framework. Start with a desired outcome, map customer opportunities, brainstorm solutions, and design validation experiments. Each level is color-coded (cyan for outcomes, blue for opportunities, purple for solutions, amber for experiments). Add, edit, delete, and collapse nodes. Save multiple trees to localStorage and export as markdown.

Create visual customer journey maps with stages, touchpoints, actions, thoughts, emotions, pain points, and opportunities. Choose from 4 pre-built templates (SaaS Onboarding, E-commerce Purchase, Mobile App, B2B Sales) or start from scratch. Includes an emotion curve visualization that plots customer sentiment across stages. Save multiple maps to localStorage and export as markdown.

Choose from three popular strategy canvases and fill in each block with guided prompts. Save multiple canvases to localStorage, load previous work, and export as text or markdown. Ideal for workshops, product discovery sessions, and business model validation.

Add objectives with descriptions and time periods, then track key results with target values, current progress, and status indicators. Progress bars show real-time completion. The alignment view groups objectives by company, team, and individual levels. Save multiple OKR sets for different quarters and export to markdown.

Paste your raw notes about what shipped, what is in progress, and what is blocked. The AI generates a polished, audience-specific status update formatted for executives, teams, board members, or all-hands presentations. Choose professional, casual, or data-heavy tone. Saves 3 to 5 hours per week of manual report writing.

This diagnostic assessment evaluates your product management practice across discovery, prioritization, roadmapping, metrics, strategy, and stakeholder management. Each dimension is scored on a 4-level maturity scale from Emerging to Advanced. Product leaders use this to identify specific capability gaps in their organization, build a PM transformation roadmap, and benchmark progress over time against industry-standard maturity frameworks.

Launching a product without checking all the boxes is a recipe for a messy rollout. This scorecard evaluates readiness across 10 critical dimensions: product quality, documentation, marketing, sales enablement, support, analytics, pricing, legal, infrastructure, and go-to-market. Rate 40 checklist items, get an overall readiness score with a radar chart, and see exactly which blockers need resolution before you can ship with confidence.

This simulation puts you in the hot seat with realistic AI feature requests that need immediate Ship, Defer, or Kill decisions under time pressure. It tests your ability to evaluate AI features by weighing technical feasibility, user value, ethical risk, and resource requirements simultaneously. Product leaders use this to sharpen AI product judgment and train their teams on the unique decision-making challenges of AI feature development.

Choosing from 100+ PM tools on the market is paralyzing. This diagnostic asks about your role, primary needs, team size, budget, maturity, integrations, and priorities to recommend the best-fit tools from a curated database of 40+ products. Product leaders use this to cut through vendor noise, build their ideal PM tech stack, and make informed tooling decisions that match their team context and budget.

How well do you know the numbers that drive products and startups? This daily estimation game tests your knowledge of SaaS benchmarks, user behavior data, startup economics, AI metrics, and PM career stats. Each answer reveals the real number with its source, teaches you the benchmark, and links to relevant IdeaPlan tools. Track your calibration accuracy over time, compete on daily streaks, and share Wordle-style results with your team.

A 28-question strengths assessment spanning product management and design. It measures Strategic Vision, User Empathy, Design Craft, Analytical Rigor, Execution, Influence, Systems Thinking, and Innovation to determine your primary and secondary archetype from 8 profiles. Product leaders use this to understand their own strengths, build balanced teams, and identify high-leverage growth areas for their reports. Results include a radar chart, archetype narrative, team pairing recommendations, and curated IdeaPlan resources.

A startup survival card game where you play as a founder making monthly decisions about hiring, product, fundraising, customers, and more. Each choice affects four stats (morale, product quality, traction, technical debt) plus your cash and burn rate. Some decisions trigger delayed consequences that surface months later. Choose from 4 founder archetypes with different starting conditions. Achieve product-market fit before your runway hits zero — then share your results.

Master 200+ product management terms across 8 categories with three study modes: flip-card study, multiple-choice quiz, and timed speed rounds. Spaced repetition surfaces terms you struggle with more often. Track mastery progress over time, filter by category, and share your results. Every card links to the full glossary definition for deeper reading.

Input your past sprint data (committed vs completed story points) to calculate average velocity, identify trends, and predict how many sprints you need to complete a target amount of work. Essential for realistic roadmap commitments and sprint planning.

Score your team on 8 dimensions: autonomy, psychological safety, clarity, velocity, collaboration, technical health, customer connection, and growth. Generates a radar chart visualization and targeted recommendations for your weakest and strongest areas.

Shipping AI features without ethical review is a reputational and regulatory risk. This scanner evaluates your AI feature across five ethical dimensions — bias, privacy, transparency, safety, and accountability — with weighted scoring and actionable mitigation recommendations. Product leaders use this to embed responsible AI practices into their development process and ensure features pass ethical review before reaching users.

AI product management requires a distinct skill set spanning ML literacy, data strategy, ethical AI, prompt engineering, evaluation methods, and more. This assessment scores you across 8 AI PM competencies and identifies your specific skill gaps with targeted learning recommendations. Product leaders use this to plan their own AI upskilling journey and to design training programs for their PM teams entering the AI space.

Not every problem needs an LLM — and using one when rules suffice wastes money and adds latency. This decision tool asks 8 questions about your use case to recommend whether you should use an LLM, traditional ML model, or rules-based logic. Product leaders use this to make informed build decisions, avoid over-engineering with AI hype, and choose the approach that balances cost, accuracy, and maintainability for each specific feature.

Pricing AI features is notoriously difficult because costs are usage-based and value is hard to quantify. This simulation lets you set pricing for a fictional AI SaaS product and watch 12 months of market response play out based on your decisions. Product leaders use this game to develop intuition for AI pricing models, understand the trade-offs between per-seat and usage-based pricing, and test strategies without real-world consequences.

AI features have unique cost structures — development, inference APIs, and ongoing model costs. This calculator models the full financial picture including time savings, revenue uplift, API costs, and development investment to produce ROI projections and break-even timelines. Product leaders use this to build business cases for AI investments, set realistic expectations with leadership, and compare AI features against non-AI alternatives.

LLM API costs vary dramatically across providers and models, and small differences in token usage multiply into significant monthly expenses at scale. This estimator compares costs across GPT-4o, Claude, Gemini, and open-source models based on your expected usage patterns. Product leaders use this to forecast AI infrastructure costs, choose providers for new features, and negotiate enterprise API agreements with data-driven volume estimates.

The build vs. buy decision for AI is more nuanced than traditional software — with fine-tuning as a powerful middle ground. This analyzer evaluates your specific context across team capability, data assets, differentiation needs, budget, and time constraints to recommend building in-house, fine-tuning an existing model, or buying a vendor solution. Product leaders use this to make strategic AI investment decisions that match their organizational capabilities.

Before investing in AI features, you need to honestly assess whether your organization is ready. This assessment evaluates AI readiness across data infrastructure, team skills, organizational culture, technical capabilities, and governance maturity. Product leaders use this to identify readiness gaps before committing resources, build an AI enablement roadmap, and set realistic expectations with leadership about what is achievable in the near term.

Shipping AI features without proper evaluation is like deploying code without tests. This generator creates structured evaluation scorecards with appropriate metrics, pass/fail thresholds, and minimum sample sizes for your specific AI use case. Product leaders use this to establish quality gates for AI features, define acceptance criteria that engineering and QA teams can execute against, and build repeatable evaluation processes.

The AI design tool landscape changes monthly with new products and features. This picker asks about your design workflow, team size, primary tasks, and integration needs to recommend the best AI-powered design tools from a curated database. Product designers and design leaders use this to evaluate new AI tools without spending weeks on trials, build their AI-augmented design workflow, and stay current with the rapidly evolving tool landscape.

AI features fail when they ignore human-AI interaction principles — unclear confidence levels, no graceful degradation, and poor error recovery destroy user trust. This audit scores your AI feature against guidelines inspired by Microsoft HAX (Human-AI Interaction) research. Product designers use this to identify UX gaps in AI features before launch, ensure designs follow established interaction patterns, and advocate for better AI experiences in product reviews.

Design teams are increasingly expected to design for AI-powered experiences, but most lack the knowledge, processes, and patterns to do it well. This assessment evaluates design team readiness across AI literacy, design patterns, tooling, prototyping capabilities, and cross-functional collaboration. Design leaders use this to build a capability development plan, make informed hiring decisions, and set realistic timelines for AI design initiatives.

Prepare for product management interviews with timed practice questions across strategy, execution, analytical, behavioral, design, and technical categories. Each question comes with a timer and structured evaluation criteria. Product leaders use this to prepare their teams for internal promotions, upskill during career transitions, and develop structured thinking skills that apply directly to daily PM work.

The only resume scorer built specifically for product managers. It evaluates your resume across five PM-specific dimensions — metrics density, impact framing, keyword coverage, structure quality, and seniority alignment — with targeted role weighting for SaaS, Growth, Technical, B2B, and AI PM positions. Unlike generic ATS checkers, it analyzes each bullet point for outcome framing and quantified results, the two factors PM hiring managers care about most.

Weak resume bullets describe tasks; strong PM bullets quantify outcomes. This rewriter analyzes individual bullet points for weak verb usage, missing metrics, task-focused framing, and absent PM context — then shows you exactly how to fix each issue with annotated before/after comparisons. Browse 30+ example transformations organized by PM type for inspiration. All analysis runs client-side with a rules engine, no AI API required.

Product management has fragmented into distinct specializations — Growth PM, Technical PM, Platform PM, AI PM, and more — each requiring different skills and offering different career trajectories. This quiz maps your background, interests, technical comfort, industry preferences, and career goals to recommend the top PM specializations that fit you best. Each recommendation includes a role description, key skills to develop, salary context, and links to relevant IdeaPlan resources to start building expertise.

Starting a new PM role? This tool generates a structured 30-60-90 day plan customized to your seniority level. Choose IC PM, Senior PM, or VP Product to get pre-populated tasks across four categories: people, product, process, and strategy. Customize every task, add your own, track completion, and export the plan to share with your manager.

Not sure which PM certification is worth the investment? Answer 6 questions about your experience, goals, budget, and preferred learning style. The tool compares CSPO, SAFe POPM, PMI-ACP, Pragmatic Institute, and Product School PMC — showing your top 3 matches with cost, duration, difficulty, pros, and cons.

Select an interview type (Product Sense, Estimation, Behavioral, Strategy, or Technical) and a target company style (Google, Meta, Amazon, Startup). The AI conducts a realistic 4-round interview with follow-up questions, gives brief feedback after each answer, and delivers a final scorecard with scores for Structure, Depth, Creativity, Communication, and Prioritization. Use it to practice before real PM interviews and identify skill gaps.