Examples by Segment
Concrete seller and buyer scenarios for solo developers, indie teams, and B2B companies.
These examples show how different types of developers use Kate, with a seller and buyer scenario for each segment.
Solo Developer
Seller: Tax Advisory Agent
Who: Maria, a freelance tax consultant who built an AI agent to help her clients with tax planning. The agent has handled 300+ client consultations over 2 years.
What she sells: She extracts her agent's knowledge into a KH-Agent artifact. The extracted artifact contains:
- Tax optimization decision trees (entity structure selection, deduction maximization)
- Income timing strategies for freelancers and small businesses
- State-by-state tax comparison frameworks
- Common audit triggers and prevention strategies
Pricing: 200 tokens subscription + 10 tokens per query.
Why it works: Maria spent years developing these strategies through real client work. Other developers building financial agents need this exact expertise but don't have the tax background. Her artifact gets queried 50+ times per week across different buyer agents, earning her steady token revenue.
Buyer: Financial Advisory Agent
Who: James, a solo developer building a personal finance agent for freelancers. His agent handles budgeting and investment advice well, but gives generic tax advice.
The gap: Kate's evaluation identifies that James's agent "lacks specific tax optimization strategies for self-employed individuals" - it recommends standard deductions when entity structure optimization could save clients thousands.
Discovery finds: Maria's tax advisory artifact. The compatibility score is high because James's agent operates in the financial advisory domain and specifically lacks tax optimization knowledge.
After subscribing: James's agent now queries the artifact when a user asks about tax planning. Instead of "consider maximizing your deductions," it responds with "at your income level of $150K, an S-Corp election typically saves $8-12K annually in self-employment tax - here's what to consider." The agent's evaluation scores in the tax domain improve significantly.
Indie Developer
Seller: E-commerce Pricing Intelligence
Who: A 3-person team running an e-commerce optimization platform. Their pricing agent has been deployed across 50+ Shopify stores for 18 months.
What they sell: They extract pricing patterns from their agent into a KH-Agent artifact:
- Competitive pricing decision trees (price matching, undercutting, premium positioning)
- Seasonal adjustment formulas by product category
- Inventory-aware markdown timing rules
- Discount strategy frameworks (flash sales, bundles, loyalty pricing)
Pricing: 500 tokens subscription + 15 tokens per query.
Why it works: The pricing patterns are derived from real data across 50+ stores - this isn't theoretical advice, it's battle-tested intelligence. An indie team building a new e-commerce tool gets 18 months of pricing experience on day one.
Buyer: Content Marketing Agent
Who: A 2-person team building an AI content marketing platform. Their agent generates content strategies, but it doesn't understand how content drives e-commerce revenue.
The gap: Kate identifies that the agent "produces content strategies disconnected from business metrics - recommends blog topics without understanding which topics drive purchasing decisions."
Discovery finds: The e-commerce pricing artifact scores highly because the content agent operates in marketing but lacks the e-commerce commerce context that would make its strategies revenue-aware.
After subscribing: The content marketing agent now queries pricing intelligence when generating strategies for e-commerce clients. Instead of generic "write about industry trends," it produces "create comparison content for your highest-margin category - seasonal pricing data shows customers research alternatives most in Q1 and Q3." Content strategies are now grounded in actual purchasing patterns.
B2B Company
Seller: Sales Objection Handling Playbook
Who: An enterprise sales enablement company that uploaded their proven objection handling framework as a KH-Upload artifact.
What they sell: A comprehensive playbook built from 5,000+ enterprise deal conversations:
- Objection classification by type (budget, authority, need, timing, competition)
- Response frameworks organized by deal stage and objection type
- Competitive positioning templates for the top 20 enterprise software categories
- Escalation criteria and handoff protocols
- Win/loss pattern analysis
Pricing: 1,000 tokens subscription + 20 tokens per query.
Why it works: Enterprise sales knowledge is expensive to develop - it takes years of deal experience to build reliable objection handling frameworks. A B2B company building a sales agent gets their sales team's objection handling up to senior rep level immediately. The per-query model means the artifact earns revenue every time an agent handles an objection, which happens dozens of times per day across all subscribers.
Buyer: HR Compliance Agent
Who: An HR tech company building an agent that helps HR teams navigate employment law compliance. The agent handles general questions well but struggles with edge cases.
The gap: Kate identifies that the agent "provides legally correct but generic compliance guidance - misses jurisdiction-specific requirements and fails to flag emerging regulatory changes that affect the specific business context."
Discovery finds: A labor law compliance artifact maintained by a legal services firm, built from a continuously updated database of federal, state, and local employment regulations.
After subscribing: The HR agent queries the compliance artifact when handling jurisdiction-specific questions. Instead of "check your local labor laws for minimum wage requirements," it responds with "as of this month, New York City's minimum wage for employers with 11+ employees is $16/hour, with scheduled increases. Your company size triggers additional requirements under the NY WARN Act." Answers are current, specific, and jurisdiction-aware.
Patterns Across Segments
| Aspect | Solo Developer | Indie Developer | B2B Company |
|---|---|---|---|
| Typical artifact | Personal expertise extracted from agent | Team's accumulated intelligence | Enterprise-grade frameworks |
| Pricing range | Lower subscription, moderate per-query | Moderate subscription, moderate per-query | Higher subscription, higher per-query |
| Buyer motivation | Fill expertise gaps the developer doesn't have | Accelerate product development | Scale domain knowledge across agents |
| Query frequency | Moderate (10-50/week) | High (50-200/week) | Very high (200+/week) |
Next Steps
- Adoption Patterns - common buyer agent integration patterns
- Pricing Strategy - optimize your pricing for your segment