AI Product Opportunity
Search less. Discover more.The next unicorn is hidden in today's signals.Ideas are everywhere. Opportunities are rare.The future belongs to those who build before everyone else sees it.Great products begin with evidence, not assumptions.Every breakthrough starts as an overlooked opportunity.Innovation happens where technology meets unmet demand.Don't chase trends. Discover them first.Build what the future demands — not what the market already has.Every great startup starts with one validated opportunity.
Evidence-backed opportunity engine

Stop guessing what to build.

Discover and validate AI product opportunities using real customer pain, competitive signals, and evidence you can inspect.

opportunities tracked
3,300+
opportunities tracked
evidence items
16,000+
evidence items
domains
44
domains
Idea of the DayTop riser this week
81

Shopify AI Knowledge Base & Deflection Suite

Knowledge Management#5 in domain

Shopify merchants with high support volumes lose revenue to repetitive inquiries (order status, returns) that agents answer manually. Existing knowledge base apps are static; they don't auto-answer from store data, deflect common questions, or learn from gaps. This inflates support costs and hurts CSAT for SMB e-commerce.

13 cited evidence itemsOpen the full analysis →
The difference

Stop researching. Start discovering.

What finding your next product looks like — before and after.

The old grind

  • Six weeks of tab-hoarding market research
  • A hundred open tabs and a gut feeling
  • Betting the roadmap on a hunch
  • Another “genius idea” at 2 a.m.
  • Build first, pray later
  • A PDF report that ages in a week

With AI Product Opportunity

  • Six minutes on one dashboard
  • One leaderboard, every claim cited
  • Scores built from verified evidence
  • Opportunities validated before you commit
  • Validate first, build with conviction
  • Living intelligence that updates every run
Why most products miss

Most founders fail because…

  • They build products nobody wants
  • They discover trends too late
  • They spend weeks researching
  • They copy existing startups

We solve this by…

  • Finding emerging technologies
  • Validating market demand
  • Ranking opportunities by evidence
  • Generating implementation plans
How it works

Find. Validate. Build. Monitor.

01

Find

Discover product opportunities hidden inside open-source projects and research.

02

Validate

Every opportunity is backed by research, market signals, and competitive analysis.

03

Build

Generate an AI-ready MVP build prompt in one click.

04

Monitor

Get notified when an opportunity you watch evolves.

Inside the engine

What happens on every discovery run

Signals from 15 evidence sources travel the same six stages — nothing reaches a leaderboard without surviving all of them.

  1. 1

    Discover

    Sweep 15 evidence sources for fresh signals

  2. 2

    Gate

    Keep only AI-product-shaped candidates

  3. 3

    Pain point

    Extract the underlying problem being described

  4. 4

    Ideate

    Draft a product answer to that pain

  5. 5

    Score

    Challenge it, then score: 16 dimensions, 4 pillars, evidence-capped

  6. 6

    Rank

    Compete for a top-100 leaderboard slot

The scoring formula

How we evaluate every opportunity

We don't generate random startup ideas — every score traces back to sources you can click. Each product opportunity is scored 0–100 on four weighted pillars, and each pillar is the mean of four dimensions — sixteen in all — scored against cited evidence pulled from GitHub, Hacker News, Reddit, arXiv, Product Hunt and the open web.

Demand

30%

Is the pain real, frequent and severe — with people saying so in public?

Monetization

30%

Will someone pay? Budget owners, comparable products, pricing headroom.

Buildability

20%

Can a small team ship it with today's models, data and open tooling?

Openness

20%

Is the space still open — or already locked up by entrenched incumbents?

The honesty rule: any dimension without verified evidence is capped at 6/10, and a URL only lifts that cap for the claims its kind of evidence can actually prove — so unproven claims can never carry an opportunity to the top of a leaderboard. Popularity metrics like GitHub stars never enter the score.

Compare

Put a whole domain on one chart

A ranked list tells you the order. It does not tell you which opportunity is cheap to build, which one is worth the most, or which one is defensible — and those are different questions with different answers.

Compare plots any two of 36 axes against each other across one domain or several, with bubble size and colour carrying two more. The default is the effort-versus-score quadrant every PM already reasons in — quick wins, big bets, fill-ins, money pits — split on the medians of the ideas you selected, not an arbitrary threshold.

Live · Robotics & Industrial Automation

Effort (proxy) — lower is better × Score 1/3

Quick winsBig betsFill-insMoney pits2550751000.02.55.07.510.0AI Safety Drill Simulator for Maintenance and Recovery Procedures — Effort (proxy): 2.5 · Score: 40CoordiBot: Distributed Planning Middleware for Heterogeneous Robotic Systems — Effort (proxy): 4.0 · Score: 39PathPlanner AI: Dynamic Multi-Robot Task and Motion Coordination — Effort (proxy): 4.0 · Score: 38SafePath AI: Safety-Critical Maintenance Guidance System — Effort (proxy): 4.3 · Score: 38RAG-Plan: Shared Memory for Multi-Robot Task Planning — Effort (proxy): 4.0 · Score: 37Fleet Memory for Multi-Robot Exception Handling — Effort (proxy): 3.0 · Score: 37Perception Model Cloud-Testing for Industrial Robots — Effort (proxy): 2.5 · Score: 37Spatial Data Engine for Robot Fleet Mapping and Localization — Effort (proxy): 3.0 · Score: 36Adaptive LLM Scheduler for Safety-Critical Robot Tasks — Effort (proxy): 4.0 · Score: 35FineTuneHub: Domain-Adapted Task Planning Models for Industrial Robot Fleets — Effort (proxy): 4.0 · Score: 35Robot Fleet Health: Predictive Maintenance for Industrial Robots — Effort (proxy): 4.0 · Score: 35Simulation Scene Generator with AI — Effort (proxy): 4.0 · Score: 34RobotSOP: RAG-based Knowledge Service for Industrial Robots — Effort (proxy): 4.3 · Score: 33Dexterity Pilot: AI Imitation for Precision Assembly — Effort (proxy): 3.0 · Score: 33Ergonomic Risk Monitoring for Manual Assembly Stations — Effort (proxy): 4.3 · Score: 32WeldOptics AI: Real-Time Seam Tracking and Defect Detection for Robotic Welding — Effort (proxy): 3.3 · Score: 32RoboCalibrate: AI-Powered Camera-to-Robot Calibration Service — Effort (proxy): 4.0 · Score: 31SiteScan AI — Effort (proxy): 3.5 · Score: 29VoiceLog for Maintenance — Effort (proxy): 4.0 · Score: 28Vision Retrofit QA for Manual Assembly Stations — Effort (proxy): 3.5 · Score: 28FleetMemory: Shared Operational Memory for Multi-Robot Systems — Effort (proxy): 4.0 · Score: 27CellTwin AI for Robotic Workcell Commissioning — Effort (proxy): 2.5 · Score: 27Sim2Deploy: Simulation-to-Real Deployment Validator — Effort (proxy): 4.0 · Score: 27VocalCobot: Multilingual Voice Interface for Industrial Robots — Effort (proxy): 3.0 · Score: 26ShopFloorQA: AI-Powered Work Instruction and Troubleshooting Chatbot — Effort (proxy): 4.0 · Score: 26Effort (proxy) — lower is betterScore

Filled = has a PM one-pager · hollow = none yet · triangle = marketplace plugin · bubble area = Year-1 ARR (mid).

Live data, not a screenshot: 25 real Robotics & Industrial Automation opportunities from the current board, redrawn every discovery run. 25 plotted. Cycling through 3 axis pairs. Plot any two of 36 axes yourself → (free account — 2 domains)

Quality

Score, the four pillars, all 16 scored dimensions, and evidence integrity — how much of a score is actually proven rather than assumed.

Economics

Weeks to MVP, build cost, year-one ARR, ROI, payback months, LTV/CAC, market size, moat strength, competitor count.

Momentum

Evidence count, times rediscovered, and 7- or 30-day score movement.

Missing estimates are left unplotted rather than drawn as zero — an idea with no revenue estimate is not an idea worth nothing, and a chart that quietly conflates the two is worse than no chart. Every value is also in a data table beneath, so nothing is locked behind the picture.

Open the compare view → (free account — 2 domains)

3,300+
product opportunities tracked
16,000+
evidence items analyzed
44
innovation domains
29
signal sources monitored
Coverage

44 business domains, continuously refreshed

Software DevelopmentDevOps & Platform EngineeringCloud & InfrastructureCybersecurityObservability & SREIT Operations (AIOps)Data EngineeringAI/ML & MLOpsTesting & QAProduct ManagementSalesMarketingCustomer SupportHuman ResourcesFinance & AccountingLegal & ComplianceHealthcarePharmaceuticals & Life SciencesInsuranceBanking & FinTechRetail & E-commerceSupply Chain & LogisticsManufacturingReal EstateConstructionEducationGovernment & Public SectorEnergy & UtilitiesTelecommunicationsAgricultureMedia & EntertainmentTravel & HospitalityAutomotive & MobilityRobotics & Industrial AutomationSmart Cities & IoTClimate & SustainabilityProcurement & Vendor ManagementKnowledge ManagementCollaboration & ProductivityCreator EconomyAtlassian Marketplace AppsCursor Ecosystem AppsClaude & MCP Ecosystem AppsGaming
Pricing

Simple plans

Compare plans in detail →

Who uses this

Curious how this works and who builds it? About AI Product Opportunity

Ideas are everywhere. Opportunities are rare.

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Curious how the engine works? About AI Product Opportunity