Editorial Policy & Testing Methodology
Last updated & verified: October 2026 • Version 2.6 Editorial Standards Edition
1. Our Core Principle: Authenticity Above All
At AI Launch Watch, we believe that AI technology is too consequential to be covered with superficial summaries. Our mission is to provide accurate, honest, and technically grounded coverage of artificial intelligence products.
We never fabricate personal experiences, testing results, or performance benchmarks. We never invent fake user reviews, simulated search volumes, or artificial laboratory claims. When a tool or robot has not undergone direct physical bench testing, we explicitly state that our analysis is based on published developer specifications and verified community data.
2. Our 4-Pillar Scorecard System
Eligible editorial reviews may receive a 1.0–5.0 score when sufficient evidence and a documented scoring methodology are available. Research-only pages may remain Not Yet Rated.
Features & Capabilities
Prompt fidelity, multimodal support, custom fine-tuning, context window limits, and ecosystem integrations.
Ease of Use & UX
Interface ergonomics, onboarding friction, keyboard accessibility, mobile responsiveness, and documentation.
Value for Money
Free tier generosity, token pricing, transparent rate limits, and comparison with open-source alternatives.
Performance & Reliability
Generation latency, error rates, hallucination frequency, server availability, and uptime track record.
3. Clear Content Labeling & Outbound Link Standards
To ensure total transparency, every listing and review on our platform is explicitly categorized:
- Editorial Analysis: Researched and written independently by our editorial desk based on technical documentation and architecture.
- User Reviews: Authentic perspectives submitted by registered community members, subjected to strict anti-spam filtering.
- Sponsored / Featured Listings: Paid submission placements are clearly identified with a
SPONSOREDorFEATUREDbadge. All outbound commercial links strictly adhere to search engine standards withrel="sponsored nofollow". Paying for placement never guarantees positive scores or favorable editorial verdicts.
4. Testing Status Classifications
We classify each product's evaluation state with transparent badges:
- Empirical Benchmark Tested: Applied only when reproducible benchmark scripts, documented test parameters, and empirical verification logs are established.
- Research-Based Editorial Analysis: Rigorous technical assessment derived from developer documentation, API references, architecture whitepapers, and verified user consensus.
- Not Yet Rated: Applied when an evaluation is research-based but standardized empirical laboratory test logs have not yet been completed. Numerical ratings are strictly withheld until empirical benchmarks are established.
- Official Product Information: Catalog specification summaries compiled directly from primary manufacturer materials.
5. Corrections & Fact-Checking
AI software changes rapidly. If you identify an outdated pricing plan, an inaccurate specification, or wish to report a correction, please consult our Corrections Policy or contact our editorial desk directly at [email protected]. Corrections are reviewed and timestamped in our audit log.