PAIN TRIGGERS
Developers frequently resort to crowdsourcing ideas because they lack mechanisms to find validated user problems.
Training large language models is too expensive for individual developers or small projects.
SaaS· Software developers (e.g., Flutter developers)Pain 7.00/10WTP 8.0/10Market 5.0/10Validation 6.0Confidence 85%Oct 10, 2026
AsphaltScan: Mobile LiDAR Pothole & Material Estimator
Estimating the exact volume of asphalt needed for irregular pothole repair is a manual, error-prone process that leads to either wasted expensive material or job-stopping material shortfalls.
automationb2bconstructioncost-reductionmobile-appniche-softwaresmall-business
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STAGE 01 · PROBLEM
Is the problem real?
CANONICAL PROBLEM
Developers struggle to discover validated, real-world problems to build solutions for, while users with highly specific, niche needs lack the technical resources to build them.
FREQUENCY
Multiple repeated complaints in the post and comments.
INTENSITY
Users explicitly describe existing tools as bloated/overkill and mention workaround behavior.
EVIDENCE
calculates its volume, asphalt needed to fill it and the price of the asphalt that's needed
commentAn app that scans the volume of a pothole on a road using LiDAR (on iPhones) and calculates its volume, asphalt needed to fill it and the price of the asphalt that's needed based on average market price. The app would have "folders" per project (addresses) where I can scan multiple potholes, the app calculates the volume for each one, estimates how much it'd cost to fill it and shows the volume needed for each individual pothole in a project + the total cost of the project.
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STAGE 02 · CUSTOMER
Who feels this pain?
TARGET USERS
Software developers (e.g., Flutter developers)Paving And Road Maintenance Contractors
Contractors who bid on and repair road damage, requiring fast and accurate material estimates to maintain margins.
Context
Developers want to build applications that solve actual user needs rather than guessing. Users want custom utility tools for their specific workflows.
Soliciting project ideas directly from Reddit users to avoid building unwanted products.
Proposing to train a smaller model to control a larger LLM to bypass the high cost of training full models.
Current Workarounds
Eyeballing pothole volume based on experience
Manual tape measure calculations that ignore depth irregularities
Over-ordering asphalt to prevent job delays, leading to wasted material
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STAGE 03 · MARKET
Where's the gap?
EXISTING SOLUTION GAPS
Developers lack platforms that provide validated, ready-to-build ideas, forcing them to randomly solicit ideas on social media.
No streamlined mobile application exists to quickly scan pothole volume via LiDAR and calculate asphalt material costs per project.
Affordable implementations of Cooperative Inverse Reinforcement Learning in LLMs are not readily accessible to researchers/hobbyists.
OPPORTUNITY & VALUE
Why Now
There is a direct request from niche industry professionals needing specialized utility tools, contrasted with developers begging for exactly this kind of validated idea.
Value Proposition
Purpose-built exclusively for paving estimators to replace manual measurements and guesswork, without requiring expensive dedicated surveying hardware.
Product Direction
An iOS mobile application that uses built-in device LiDAR to instantly 3D-scan a pothole, calculate its exact cubic volume, and output the required asphalt tonnage and cost.
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STAGE 04 · BUSINESS
How does it make money?
MONETIZATION
$49/moPer estimator license
Model
SaaS subscription
WILLINGNESS TO PAY
Asphalt is expensive and margins are tight in contracting; avoiding one over-order or eliminating the need for a second material run instantly pays for a year of the software. Users are explicitly requesting this capability.
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STAGE 05 · EXECUTION
How do you ship it?
MVP PLAN
“Scan a pothole, instantly quote the asphalt.”
An iOS mobile application that uses built-in device LiDAR to instantly 3D-scan a pothole, calculate its exact cubic volume, and output the required asphalt tonnage and cost.
Core Features
One-tap LiDAR volume scanning via ARKit
Customizable asphalt density and cost-per-ton calculator
On-site PDF estimate generation
Weekly Roadmap
1
W1-W2
Core LiDAR scan captures accurate physical volume.
- •Integrate ARKit/LiDAR depth API
- •Build mesh generation for surface depressions
- •Calculate and output raw cubic volume
2
W3-W4
Asphalt calculation engine and core UI are complete.
- •Implement material density conversion formulas
- •Add inputs for custom price-per-ton and material types
- •Build user-friendly field UI
3
W5
Field testing and quote export finalized.
- •Field test app on real potholes against manual calculations
- •Implement PDF estimate export feature
- •Recruit 3-5 local paving contractors for beta testing
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W6
App Store launch and initial customer acquisition.
- •Submit to iOS App Store
- •Configure in-app subscription billing
- •Execute direct outreach to 100 paving companies
Launch Strategy
Direct outbound sales to local paving companies, App Store SEO for 'paving calculator', and posting in construction/contracting niche communities.
RISKS & ASSUMPTIONS
Top Risks
Hardware/LiDAR precision limits
Consumer device LiDAR may struggle to accurately map highly irregular, deep, or wet potholes, leading to faulty material estimates.
Low tech adoption in target market
Traditional paving contractors may prefer existing manual methods or 'eyeballing' over adopting new mobile SaaS tools.
Single-use utility churn
Users might use the app initially to calibrate their own estimation skills and then cancel the subscription.
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STAGE 06 · DECISION
Should you build it?
NEED A CLEARER CALL?
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoWhat this score means
This idea scores in the upper-middle range of opportunities surfaced by MonetScope, with a validation sub-score of 6/10 against 1 independently sourced evidence signals. A "promising" rating usually indicates a real pain has been detected and discussed in the open, but the pipeline did not find enough signal to flag it as urgent or high-frequency. These opportunities can still produce excellent businesses — they often correspond to "boring" problems that established players have ignored — but the founder should expect a longer customer-development cycle to confirm willingness to pay.
Why this matters for SaaS founders
It sits at the intersection of "automation", "b2b", "construction", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
Frequently asked questions
Is "AsphaltScan: Mobile LiDAR Pothole & Material Estimator" a real validated startup idea or just an AI-generated suggestion?
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
How recent is the underlying data for automation?
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
What's the difference between "overall score" and "validation score"?
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.