Our Methodology
How Marlvel.ai provides independent mobile app intelligence reports for the US market. We continuously improve our analysis, accuracy, and coverage.
Last updated: March 30, 2026
Our Mission
Marlvel.ai's mission is to help mobile builders improve their existing apps and create new ones, so they can unleash their creativity. We provide intelligence reports across all 44 App Store categories, covering 2,400+ top apps in the US market. Our reports offer an objective, data-driven view of the mobile app landscape. No app publisher pays for coverage, influences our analysis, or reviews reports before publication. Our goal is simple: give builders the insights they need to make better decisions.
Data Sources
Our analysis is built on publicly available signals from multiple independent sources. We cross-reference data points to reduce bias and increase confidence in our findings.
App Store Listings
Metadata, descriptions, version history, screenshots, and pricing from iOS App Store and Google Play.
User Reviews
Ratings and written reviews from both platforms, analyzed for sentiment patterns, recurring themes, and evolving user perception.
Developer Websites
Official websites, about pages, press releases, and public documentation from app publishers.
Public Market Signals
App store rankings, chart positions, category trends, and competitive landscape data from public sources.
Community Signals
Public discussions and user-generated content that reflect real-world user experience and market perception.
Store Metadata
Technical metadata including bundle identifiers, platform availability, content ratings, and update frequency.
Analysis Pipeline
Each intelligence report is produced through a multi-stage analysis pipeline that combines AI-powered processing with structured analytical frameworks.
Signal Collection & Normalization
We aggregate data from all available public sources for a given app. Raw signals are cleaned, deduplicated, and normalized into a structured dataset that can be analyzed consistently across thousands of apps.
Feature & Market Positioning Analysis
Our AI identifies the app's core features, monetization model, target audience, and competitive positioning. Each feature is classified as either a market standard or a differentiator based on category benchmarks.
User Sentiment Analysis
We analyze user reviews across platforms to extract sentiment patterns. This includes identifying recurring praise themes, pain points, and emerging trends. We require a minimum of 5 reviews to generate sentiment data. Below this threshold, we flag the confidence as low rather than guessing.
Intelligence Synthesis
All collected signals are cross-referenced and synthesized into a structured intelligence report. Each app is compared against category peers to identify competitive advantages and gaps. The output includes SWOT analysis, market outlook, pros/cons derived from real user feedback, and actionable insights. Reports follow a consistent format designed for both human readers and machine consumption.
Quality Assurance & Expert Review
Our team of experienced mobile industry professionals, with over 15 years of expertise in app development, product management, and mobile market analysis, continuously reviews the generated content to ensure quality.
Reviewers check reports for factual accuracy, analytical coherence, and relevance. They flag and correct incoherencies, outdated information, and misleading conclusions that automated analysis may produce. This ongoing human-in-the-loop approach ensures that our reports meet the standard of quality that mobile builders rely on.
Confidence Scoring
Every report includes a transparent confidence score (0.0 to 1.0) that reflects how much data was available to produce the analysis. We believe in being honest about what we know and what we don't.
| Level | Score | What it means |
|---|---|---|
| High | 0.7 to 1.0 | 100+ reviews, diverse data sources, strong sentiment signal |
| Medium | 0.4 to 0.69 | 20-99 reviews, limited source diversity, moderate signal |
| Low | 0.0 to 0.39 | Fewer than 20 reviews, limited data, or very recent launch |
Confidence is calculated from review volume, website availability, about page content, sentiment data quality, and feature documentation depth. Additionally, our team evaluates reports and may downgrade the confidence score if the generated information appears inaccurate or inconsistent.
Update Frequency
Reports are updated on a continuous basis, with refreshes running every hour. Our target is that no report should be older than 15 days. Each refresh re-collects all public signals, re-runs the analysis pipeline, and regenerates the report when new reviews, version updates, or ranking changes are detected. Market pulse data (rankings, top movers) is refreshed daily. All reports display their last audit date prominently.
Independence & Ethics
Our commitment to independence is non-negotiable:
- No pay-for-play: No app publisher can pay for a favorable report, higher ranking, or removal of negative findings.
- No editorial influence: Publishers do not see or approve reports before publication.
- Public data only: We analyze publicly available information. We never access, store, or process any private or personal user data, internal analytics, or confidential business information.
- Transparent methodology: Our confidence scoring and data sources are fully disclosed. We show our work.
- Corrections policy: Each report includes the ability to flag an error. We commit to reviewing all requests promptly and applying corrections when justified. Use our support page to report any issue.
See It in Action
Want to see what our methodology produces? Check out the Candy Crush Saga intelligence report as an example of a high-confidence report including user sentiment analysis with real quotes, key feature breakdown with competitive positioning, store rankings history, pros and cons, market outlook, pricing analysis, and related apps comparison.
Known Limitations
- Apps with fewer than 5 user reviews do not receive sentiment analysis.
- Very recent app updates or launches may not be reflected immediately.
- AI-powered analysis may occasionally produce inaccurate or incomplete conclusions. Our expert review process catches most issues, but some may persist.
Disclaimer
All intelligence reports published on Marlvel.ai are provided strictly for informational purposes, to help mobile builders improve their apps and make more informed decisions. They do not constitute guaranteed advice, recommendations, or endorsements. Marlvel.ai declines all responsibility for any decisions made based on the information contained in our reports. Use at your own discretion.
Machine-Readable Access
All intelligence reports are available in machine-readable formats for AI systems, researchers, and developers:
| Endpoint | Format | Description |
|---|---|---|
| /llms.txt | Text | Index of top apps per category with links to reports |
| /llms-full-{category}.txt | Markdown | Complete reports for a single category |
| /api/llm/apps/{cat}/{slug} | Markdown | Individual app report with YAML frontmatter |
| /api/llm/categories | Markdown | Dynamic index of all categories |
| /api/llm/pulse | Markdown | Live US App Store rankings |
| /.well-known/ai.json | JSON | AI discovery manifest |
| /about/methodology.md | Markdown | This page in markdown |
| /ai-policy.md | Markdown | AI policy in markdown |
All data is freely accessible. Only fair use is accepted, under CC-BY-NC 4.0 license.