Powerful Features for Modern AI Apps
Everything you need to build intelligent applications without the complexity of traditional RAG systems
Everything You Need for AI-Powered Apps
Transform any content into conversational AI experiences with our comprehensive API suite
Chat with Everything
Transform any content into conversational AI experiences:
- 🎥 YouTube Videos - Extract insights from any video
- 🌐 Web Pages - Understand articles, docs, blogs
- 🔍 Google Search - Analyze results intelligently
- 📰 Google News - AI-powered summaries
- 🛍️ Shopping Data - Compare products instantly
Built for Developers
Everything you need, nothing you don't:
- 📊 Pay-per-use credits
- 🔌 Zapier, n8n, Make.com ready
- 📖 Simple documentation
- 🚀 Instant API keys
- 💾 Context caching included
Enterprise-Ready
Scale confidently with:
- 🔒 SOC 2 compliant infrastructure
- ⚡ 99.9% uptime SLA
- 🌍 Global CDN
- 📞 24/7 support
- 📈 Usage analytics
1M+ Token Context
Process entire documents without chunking:
- 📚 Full books & papers
- 📄 Long documents
- 🎬 Hours of video transcripts
- 💬 Extended conversations
- 🔗 Multiple sources at once
36-Hour Sessions
Conversations that remember context:
- 🧠 Persistent memory
- 💬 Multi-turn conversations
- 📊 Context accumulation
- ⚡ Fast follow-ups
- 💰 Cost-efficient caching
5,000+ Integrations
Connect with your favorite tools:
- ⚡ Zapier workflows
- 🔧 n8n automation
- 🎯 Make.com scenarios
- 🔌 REST API
- 📱 SDKs available
Deep Dive Into Our Technology
1M+ Token Context Windows
Unlike traditional RAG systems that break documents into small chunks, we process entire documents in their full context. This means:
- Process entire books, research papers, or documentation sets
- Maintain relationships between distant parts of documents
- No loss of context from arbitrary chunking
# Traditional RAG approach
chunks = split_document(doc, chunk_size=512)
embeddings = [embed(chunk) for chunk in chunks]
relevant_chunks = vector_search(query, embeddings, k=5)
# Limited context, may miss important connections
# WLN.ai approach
response = wln.chat(
document=entire_document, # Full 500-page book
question="How does chapter 3 relate to the conclusion?"
)
# Complete understanding of the entire document
36-Hour Session Example
Monday 9 AM
"Analyze this research paper"
Monday 2 PM
"What were the main findings again?"
✓ Remembers previous analysis
Tuesday 11 AM
"Compare with this new paper"
✓ Maintains full context
Persistent Session Memory
Build real conversations that remember context across multiple interactions:
- Continue conversations hours or days later
- Build up knowledge through iterative questions
- Perfect for research, learning, and support applications
Work with Any Content Type
YouTube Videos
Extract insights from any YouTube video instantly
- • Automatic transcription
- • Multi-language support
- • Timestamp references
Web Pages
Understand any webpage or article with AI
- • Clean content extraction
- • Follow redirects
- • Handle paywalls (when possible)
Google Search
Analyze search results intelligently
- • Real-time search data
- • Multi-source synthesis
- • Fact verification
Google News
Stay updated with AI-powered summaries
- • Topic monitoring
- • Sentiment analysis
- • Trend detection
Shopping Data
Compare products and prices instantly
- • Price comparison
- • Review analysis
- • Feature extraction
Documents
Process PDFs, docs, and text files
- • Full document understanding
- • Table extraction
- • Cross-reference support
See It In Action
🐍 Python Example
import wln
# Initialize client
client = wln.Client(api_key="your_api_key")
# Chat with a YouTube video
response = client.youtube_chat(
video_url="https://youtube.com/watch?v=dQw4w9WgXcQ",
question="What are the main topics discussed?"
)
print(response.answer)
# Output: The video covers topics including...
# Continue the conversation
followup = client.continue_chat(
session_id=response.session_id,
question="Can you elaborate on the second point?"
)
# The API remembers the full context!
🟨 JavaScript Example
const wln = require('wln-ai');
// Initialize
const client = new wln.Client({
apiKey: 'your_api_key'
});
// Analyze web content
const result = await client.webChat({
url: 'https://example.com/article',
question: 'Summarize the key points'
});
console.log(result.answer);
// Chain multiple sources
const comparison = await client.continueChat({
sessionId: result.sessionId,
question: 'How does this compare to...',
additionalUrl: 'https://another-source.com'
});
Start Building Smarter Today
No credit card required. 100 free credits every month.
No Setup Required
Start making API calls immediately
No Hidden Costs
Transparent credit-based pricing
No Vendor Lock-in
Standard REST APIs, export anytime
No Learning Curve
If you can call an API, you can use WLN.ai
Questions?
Chat with our docs using our own API → docs.wln.ai