1
Website
Kanshi uses this layer to decide whether the lead should proceed.
Industries
AI validation for every workflow that accepts user content
Every industry has different submission quality problems. Kanshi adapts validation to the business, the page, and the intent behind the content.
Validation Pipeline
Kanshi validates the lead, not just the visitor
Traditional security verifies who submitted. Kanshi works after that layer to decide whether the lead should enter your systems.
2
Page
Kanshi uses this layer to decide whether the lead should proceed.
3
Business
Kanshi uses this layer to decide whether the lead should proceed.
4
Submission
Kanshi uses this layer to decide whether the lead should proceed.
5
Decision
Kanshi uses this layer to decide whether the lead should proceed.
Decision Engine
Allow, reject, or flag with explainable confidence
Every submission can return a decision, confidence score, category, and reason.
Incoming submission
"I need a quote today. Please call me about your service package."
Confidence
92%
Risk
Low
Category
Valid lead
Allow
Lead intent fits the business and can enter the workflow.
Reject
Submission is fake, low quality, irrelevant, or not worth routing.
Flag
Submission may be real but should be reviewed before entry.
Industries
Context-aware validation across real business workflows
Different industries receive different kinds of user content. Kanshi evaluates fit against the business and page context.
Forums and Communities
Challenge: Low-quality posts, thread hijacking, promotions, and AI-written replies.
How Kanshi helps: Kanshi checks whether the post fits the topic, community purpose, and expected intent.
eCommerce
Challenge: Fake reviews, product Q&A abuse, suspicious orders, and seller applications.
How Kanshi helps: Validate reviews, listings, seller requests, and order notes before they distort trust.
Healthcare
Challenge: Sensitive requests need careful routing and irrelevant submissions waste staff time.
How Kanshi helps: Screen intake and contact requests for relevance while preserving legitimate patient communication.
Finance and Insurance
Challenge: Loan applications, claims, and support messages can attract fraud and fake data.
How Kanshi helps: Use contextual review to flag submissions that do not fit the business or requested workflow.
Education and Government
Challenge: Public forms receive off-topic, duplicate, and low-quality submissions.
How Kanshi helps: Route valid requests and reduce manual triage for forms, applications, and public intake.
Real Estate and Recruitment
Challenge: Fake enquiries, irrelevant applications, and low-intent outreach drain follow-up time.
How Kanshi helps: Evaluate whether each enquiry or application matches the listing, role, and organization.
Legal and SaaS
Challenge: Support, consultation, and feature request channels attract noisy or misleading entries.
How Kanshi helps: Classify requests by legitimacy, relevance, and review priority before work begins.
Customer Support
Challenge: Fake tickets and off-topic requests pollute queues and reporting.
How Kanshi helps: Detect whether the ticket belongs to the product, account flow, or support category.