AI Agent Builder
    Deploy AI Agents in Minutes, Not Months 

    Stop waiting 6 months for infrastructure. Your ML team has ideas today — ship them this week.

    What Real Teams Are Building

    These aren't theoretical use cases. They're live in production at companies like yours.

    Customer Support AI

    A fintech reduced ticket resolution from 4 hours to 12 minutes by building a RAG chatbot over 50,000 support docs. Deployed in 2 days.

    WeaviateQdrantMilvusEmbeddings

    Document Processing Agents

    A logistics company automated invoice extraction across 15 carrier formats. Processing 10,000 docs/day — previously needed 3 FTEs.

    LangChainLlamaIndexCustom APIs

    Natural Language BI

    'Show me Q4 revenue by region' → instant chart. An e-commerce company gave their ops team self-serve analytics without SQL.

    SupersetClickHouseAirflow
    Production ML Without the PhD

    Skip the 6-Month Infrastructure Project

    Your ML engineers should be shipping models, not debugging Kubernetes manifests at 2am.

    Vector Databases

    Weaviate, Qdrant, Milvus — deployed with mTLS and backups. No YAML required.

    Weaviate •Qdrant •Milvus •Chroma

    Experiment Tracking

    MLflow with S3 artifact storage. Compare 100 model runs without infrastructure tickets.

    MLflow •Weights & Biases

    Model Serving

    KServe with autoscaling. Handle 10 RPS or 10,000 — pay only for what you use.

    KServe •Ray Serve •Triton

    GPU Clusters

    Ray clusters for distributed training. Spin up 8 GPUs for fine-tuning, tear down when done.

    Ray Clusters •GPU Pods •Spark

    From 'We Need a Chatbot' to Production in 15 Minutes

    Your CEO asked for an internal knowledge bot. Here's what it actually takes:

    Step 1

    Deploy Vector DB

    Click 'Deploy' in catalog. 45 seconds later: production Weaviate cluster with mTLS, backups, and monitoring ready.

    Step 2

    Attach Embeddings

    One toggle to attach HuggingFace sentence-transformers. No Python dependencies to manage, no Docker containers to debug.

    Step 3

    Ingest Company Docs

    Point to your Confluence, or S3 bucket. Our pipeline handles OCR, chunking, and parallel ingestion automatically.

    Step 4

    Connect Your LLM

    Securely connect OpenAI or a self-hosted Llama 3. The gateway handles API keys, rate limiting, and cost tracking.

    rag_app.py
    1# Build a production RAG app in < 15 minutes
    2from aivena import Catalog, Agent
    3
    4# 1. Deploy Production Vector DB
    5# mTLS, backups, and monitoring included
    6weaviate = Catalog.deploy(
    7 "weaviate-cluster",
    8 replicas=3,
    9 region="eu-west-1"
    10)
    11
    12# 2. Attach Embeddings Model
    13# No pip install, no Docker management
    14embeddings = Catalog.attach(
    15 "sentence-transformers/all-MiniLM-L6-v2",
    16 gpu=True
    17)
    18
    19# 3. Ingest & Chunk Documents
    20# Auto-ocr, cleaning, and vectorization
    21dataset = Catalog.source("s3://company-docs/policy")
    22weaviate.ingest(
    23 dataset,
    24 embeddings=embeddings,
    25 chunk_size=512,
    26 overlap=50
    27)
    28
    29# 4. Connect LLM Gateway
    30# Unified API for OpenAI, Anthropic, or Llama
    31agent = Agent(llm="gpt-4", knowledge_base=weaviate)
    32
    33response = agent.ask(
    34 "What is our remote work policy?",
    35 citations=True
    36)
    37print(response)
    Initializing environment...
    Python 3.11 • Aivena SDK v2.4.0

    Your ML Team Is Waiting

    Every week you spend on infrastructure is a week not shipping AI features. Start today — no credit card required.