Automate Enterprise Workflows with Context-Aware AI

CoreFlow provides real-time data orchestration and unstructured context routing for enterprise operations using scalable LLM pipelines.

SOC2 Type II and GDPR Compliant

Engineered for Scale and Precision

Stop writing boilerplate data pipelines. Let our intelligent routers delegate context across model families based on complexity.

Automated Extraction

High-throughput ingestion of unstructured documents and API streams into structured, queryable vectors in real-time.

Low-Latency Routing

Intelligent context delegation across model families that optimizes for cost and latency without sacrificing reasoning capabilities.

Zero-Retention Security

Enterprise-grade data isolation. We don't train on your data. Built from the ground up to meet strict privacy standards.

Built for Modern Data Teams

At CoreFlow Labs, we realized that the biggest bottleneck in enterprise AI adoption isn't the models themselves, but the plumbing required to feed them context reliably.

We're a team of engineers who spent years building scalable data infrastructure. We created CoreFlow to eliminate the friction between your messy, unstructured company data and powerful LLM reasoning.

Independent & Bootstrapped

pipeline.py

import asyncio
import os
import coreflow

async def main():
    # Initialize the enterprise client
    client = coreflow.AsyncClient(api_key=os.environ.get("COREFLOW_API_KEY"))

    # Connect your internal database securely
    source = await client.sources.postgres(
        url=os.environ.get("DATABASE_URL"),
        sync_interval=3600
    )

    # Create a context-aware extraction pipeline
    pipeline = await client.pipelines.create(
        source=source,
        models=["claude-5-5-opus", "claude-5-5-sonnet"],
        routing_strategy="cost-optimized"
    )

    result = await pipeline.run()
    print(f"Processed {result.tokens} tokens across {result.documents} docs.")

if __name__ == "__main__":
    asyncio.run(main())