Data Methodology
Last updated: 2026-05-18
Data Methodology
HokAI is built on accurate, honest tool information. The founder has over a decade of experience in regulated industries including fintech, banking, and compliance, across Europe and Southeast Asia. That background shapes how we think about data integrity. We built HokAI because we were frustrated with affiliate-driven directories where whoever pays the most gets the top spot. Our methodology is designed to resist that.
How Tools Are Discovered and Added
Tools enter the directory through team research, user suggestions, and monitoring of the AI tool landscape. Each tool is evaluated for fit and quality before being added. We don't accept payment to include tools. If you want to suggest one, email info@hokai.io with the tool name, URL, and a brief description of what it does.
What Data We Collect Per Tool
For each tool we collect: name, vendor, description, pricing (min, max, notes, free tier), key features, pros and cons, target audience, platforms (web, desktop, mobile), architecture, capabilities, compliance certifications, and integration information. We also store technical audit data where available, including API robustness and latency, to support health scores and recommendations.
How Data Is Kept Current
We use automated scanning to monitor the AI tool landscape and keep information current. Our systems run regularly to verify pricing, features, and links. Manual review supplements automation. User reports help us catch issues faster. If you spot something that's outdated or wrong, email info@hokai.io.
Quality Assurance
Accuracy is verified through automated checks, manual review, and user feedback. Tool profiles show the good and the bad. Rankings and leaderboards are based on real signals: download numbers, team usage, and community trends. We don't accept payment to rank tools higher.