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