AI Wrapper Product Kit

Build viable AI wrapper products that solve real problems — covering product design, prompt engineering as product development, cost management, differentiation strategies, and common pitfalls.

When to Use

Build an AI wrapper when:

• AI (LLM) is the core value proposition of the product
• You're solving a specific workflow problem with AI
• Your differentiation comes from UX, data, or domain expertise — not the model itself
• You can add significant value on top of raw API access

Don't build a wrapper when:

• The raw API (ChatGPT, Claude) already does what you'd offer
• Your only differentiation is a nicer UI
• The target audience is technical enough to use APIs directly
• You can't articulate why someone would pay for your product over the API

Quick Start

Product Architecture

User Interface (your differentiation)
         |
    Prompt Layer (your IP)
         |
    Orchestration (workflow, context, memory)
         |
    LLM API (commodity)
         |
    Post-processing (formatting, validation, safety)

Value-Add Layers

Copy
class AIWrapperProduct:
    def __init__(self):
        self.context_manager = DomainContextManager()
        self.prompt_engine = PromptEngine()
        self.post_processor = PostProcessor()

    def process_request(self, user_input, user_context):
        # Layer 1: Domain-specific context (YOUR VALUE)
        enriched_context = self.context_manager.enrich(user_input, user_context)

        # Layer 2: Optimized prompt (YOUR IP)
        prompt = self.prompt_engine.build(user_input, enriched_context)

        # Layer 3: LLM call (COMMODITY)
        raw_response = self.llm.complete(prompt)

        # Layer 4: Post-processing (YOUR VALUE)
        processed = self.post_processor.format(raw_response)
        validated = self.post_processor.validate(processed)

        return validated

Core Concepts

Moat Building Strategies

Strategy	Defensibility	Example
Proprietary data	High	Fine-tuned on 10M domain-specific docs
Workflow integration	High	Embedded in existing tools (Slack, JIRA)
Domain expertise	Medium	Legal AI with lawyer-written prompt chains
UX innovation	Medium	Voice-first interface for hands-free use
Network effects	High	Collaborative features that improve with users
Prompt engineering	Low	Better prompts (easily replicated)

Cost Management

Strategy	Savings	Impact
Model tiering	60-80%	Route simple tasks to cheaper models
Response caching	30-50%	Cache repeated queries
Prompt optimization	20-40%	Shorter prompts, fewer tokens
Batch processing	10-20%	Aggregate similar requests
User quotas	Variable	Limit per-user API calls

Pricing Models

Model	Works When	Example
Usage-based	High-value outputs	$0.10 per document processed
Subscription	Predictable usage	$29/month for unlimited queries
Freemium	Growth-focused	Free tier (50 queries) + paid
Per-seat	B2B teams	$15/user/month

Configuration

Parameter	Description
primary_model	Main LLM for generation
routing_model	Smaller model for query classification
max_free_queries	Freemium tier limit
cost_per_query_limit	Maximum spend per query
cache_ttl	Response cache duration
domain_context_source	Where domain knowledge comes from

Best Practices

1. Solve a specific problem — "AI for X" beats "general AI assistant" every time
2. Build moats beyond prompts — data, workflow integration, and network effects defend better
3. Manage costs from day one — many wrappers fail because API costs exceed revenue
4. Test with real users early — AI demos impress, but production usage reveals real problems
5. Add value in the layers around the LLM — context enrichment, post-processing, and UX
6. Plan for model changes — your product should work when the underlying model version changes

Common Issues

"ChatGPT can do this for free": Your differentiation must be clear and concrete. Focus on workflow integration, domain-specific context, or UX that the raw API can't provide.

API costs exceed subscription revenue: Implement aggressive model tiering and caching. Set per-user rate limits. Use cheaper models for 70%+ of queries. Raise prices if the value justifies it.

Model API changes break the product: Abstract the LLM layer. Build integration tests that validate output quality. Support multiple providers so you can switch if one changes.