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: April 4, 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 using a 5-level sentiment taxonomy: Thrilled (81-100), Excited (61-80), Mixed (41-60), Frustrated (21-40), Upset (0-20). The score combines star ratings and volume with AI analysis of review text (theme extraction, evidence quoting). This captures both the rating and the reasoning behind it.
Competitive Landscape Analysis
Each app's competitive environment is mapped using a 4-tier taxonomy: Nemesis (closest rival), Contenders (strong competitors with different angles), Same Space (broader ecosystem peers), and New Kids on the Block (emerging threats). We prioritize same sub-genre over broad category. A surf game gets compared to other surf games first, not all sports games.
Intelligence Synthesis
All collected signals are cross-referenced and synthesized into a structured intelligence report. Each app is compared against category peers and direct competitors to identify competitive advantages and gaps. The output includes SWOT analysis, market outlook, strengths and weaknesses derived from real user feedback, and actionable insights.
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.
Corrections Policy
Every intelligence report on Marlvel.ai carries a Report an issue link. If you spot a factual error — wrong rating, wrong publisher, misattributed feature, outdated pricing, incorrect version history — submit it via that link or the support page with a short description and, when possible, the source that contradicts what we show.
We review reported issues within a few hours during business days. When a correction is warranted, we update the underlying data and regenerate the affected report from scratch so the intelligence reflects the corrected inputs end-to-end. We do not silently patch individual sentences: if the source changes, the analysis is rerun.
If a report is withdrawn pending correction, we return a noindex placeholder rather than a stale version, and we publish the corrected report once the inputs are verified. We do not maintain a public changelog of individual corrections, but the dateModified field in the report's metadata reflects the most recent meaningful update.
AI Usage & Editorial Policy
Marlvel.ai intelligence reports are produced by a pipeline that combines publicly available data (App Store and Google Play metadata, user reviews, developer websites, release notes, chart rankings) with large language models that structure, synthesize, and write the report sections. The AI does not invent data: it only organises and summarises what the input signals contain.
To keep outputs grounded, every generation prompt enforces an explicit discipline: no LLM-jargon filler (a blacklist of filler words is injected into every prompt), no speculation when the input does not support a claim, and no verbatim quoting of user reviews (we paraphrase recurring patterns to comply with store terms of service and copyright). When a section does not have enough input data, we display a transparent fallback saying so rather than padding with generic prose.
A separate AI judge scores every report along ten axes (factual grounding, analytical depth, editorial clarity, originality, actionability, and structural coverage of overview, features, sentiment, competitive position, and outlook). Reports that score poorly on grounding or originality are flagged for regeneration before they are published. The Reliability Index exposes a public-facing composite score (0-100) summarising the trust we place in each report based on data solidity, freshness, and completeness.
AI generation does not replace judgement. Our team reviews prompts, taxonomy, and scoring calibration on a rolling basis, and we retire or rewrite any prompt that produces consistently low-quality output. We explicitly do not make medical, financial, legal, or political recommendations — those categories receive hedged phrasing and skip the FAQ module entirely.
Content Compliance & Takedown
Before publishing, content is filtered with awareness of the laws and regulations applicable to the subjects we cover, to the best of our knowledge, so that what we publish stays in line with legal norms and platform policies. Sensitive categories — health, finance, politics, minors, hate, weapons, gambling, adult content — are treated with additional guardrails (hedged phrasing, disclaimers, exclusion from AI retrieval for kids content).
If a concern is raised — factual error, privacy issue, sensitive topic, rights complaint, or suspected non-compliance — we review it at our discretion and, where warranted, remove or amend the content within a few hours. Use the support page or the “Report an issue” link on any report.
We operate under a best-effort obligation (obligation de moyens), not a guarantee of result. Reports are strictly informational: they aggregate publicly available information, we are not accountable for the underlying public sources themselves, and we do not provide professional advice (medical, financial, legal, or otherwise). Always verify critical information independently.
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.