The Monetization Layer for AI Agents.
Monetize AI agents with contextual product recommendations. Secure, compliant infrastructure for AI-native commerce powered by RAG and vector search.
Contextual Commerce in Action
Sony WH-1000XM6
Premium noise cancelling headphones
Enterprise-Grade Infrastructure
Built for scale, designed for developers, optimized for AI agent monetization. Ultra-low latency vector search API for contextual product recommendations.
Ultra-Low Latency
Lightning-fast product recommendations powered by optimized vector search.
LanceDB on S3
Compliance Engine
Built-in regulatory compliance with automated disclosure management.
GDPR-Ready Privacy Wrappers
Developer-First SDK
Seamless integration with your AI agents in minutes, not days.
Official MCP Server support
Race the Bot
Integration so simple, your AI agent could do it. (Seriously.)
from vkra_protocol import LLMAOrchestrator
from vkra_protocol.schemas import SearchRequest, UserContext
from uuid import uuid4
# Initialize orchestrator
orchestrator = LLMAOrchestrator(
vector_db=my_vector_db,
profile_store=my_profile_store,
embedding_service=embedding_service,
llm_service=llm_service,
)
# Search for contextual products
request = SearchRequest(
query="wireless headphones for running",
context=UserContext(
user_id=uuid4(),
session_id=uuid4(),
privacy_level="standard",
),
limit=3,
)
# Execute search
response, _, _, _ = await orchestrator.execute_search(request)
# Integrate recommendations
for result in response.results:
print(f"{result['title']}: {result['price']}")
Trusted Infrastructure
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