AI InfraStream
Technologies

Headless Ingestion Engine

AI InfraStream Technologies provides a headless engine for GPU-optimized ingestion pathways purpose-built for Retrieval-Augmented Generation (RAG) pipelines. Our platform abstracts the complexity of document parsing, chunking, and semantic embedding into a single API surface — enabling engineering teams to move from raw data to vector-ready representations without managing intermediate infrastructure.

Designed for high-throughput environments, the system handles concurrent document streams at scale while maintaining deterministic output quality. No UI, no dashboard overhead — just a reliable, API-first data plane that integrates into existing MLOps workflows.

Technical Specifications

The InfraStream pipeline is architected for compute-intensive workloads where latency and throughput are primary constraints. Worker nodes combine CPU-bound document parsing with GPU-accelerated inference to process real-time data flows across distributed clusters.

• CPU-bound parsing layer — Multi-format document extraction (PDF, DOCX, HTML, Markdown) with structural metadata preservation. Runs on dedicated CPU pools to avoid GPU contention.
• GPU-accelerated embedding — Batched inference on NVIDIA accelerators using optimized ONNX runtimes. Supports custom embedding models via pluggable adapters.
• Distributed task orchestration — Worker pools managed via Kubernetes with autoscaling based on queue depth. Backpressure-aware scheduling prevents resource exhaustion.
• Vector output sinks — Native connectors for Pinecone, Weaviate, Qdrant, pgvector, and custom gRPC endpoints. Schema-validated writes with at-least-once delivery guarantees.

Document Ingestion

Submit documents for parsing, chunking, and semantic embedding via a single endpoint. Responses include a job ID for async status polling. Authenticate with a Bearer token issued from your project dashboard.

curl -X POST https://api.ainfrastream.buzz/v1/ingest/document \
  -H "Authorization: Bearer <API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "source_url":    "s3://your-bucket/docs/report.pdf",
    "pipeline":      "rag-default",
    "chunk_strategy": {
      "method":      "semantic",
      "max_tokens":  512,
      "overlap":     64
    },
    "embedding": {
      "model":       "infra-embed-v3",
      "dimensions":  1536
    },
    "destination": {
      "type":        "qdrant",
      "collection":  "prod-knowledge-base"
    },
    "priority":      "high"
  }'

{
  "job_id":      "job_9f2a1c4e-8b7d-4e3f-a1d6-2c8e9f4b7a3d",
  "status":      "queued",
  "created_at":  "2026-07-04T11:38:00Z",
  "estimated_ms": 4200,
  "poll_url":    "/v1/jobs/job_9f2a1c4e-8b7d-4e3f-a1d6-2c8e9f4b7a3d"
}