AI Baseline
    The Problem

    Current RAG is broken

    Retrieval systems weren't built for AI agents that need to make real decisions.

    Before

    • Context gets lost

      Vector search treats documents as isolated chunks, missing crucial relationships between facts.

    • No source verification

      Without provenance trails, AI confidently returns wrong answers—a dealbreaker for high-stakes work.

    • Same broken paradigm

      Everyone builds on the same flawed retrieval patterns. Better models can't fix bad knowledge architecture.

    With Baseline

    • Graph-native structure

      Preserves document relationships and cross-references—context stays intact.

    • Traceable reasoning

      Every answer includes the full path: sources, timestamps, and confidence scores.

    • Sub-second retrieval

      Research-grade depth in milliseconds. Deep research in ~10 seconds.

    <1s
    Retrieval
    100%
    Traceable
    ~10s
    Deep research
    The Team

    Founders

    For over a decade, we've built AI systems for humans in media, life sciences, and regulated industries. Now we're engineering that same trust for AI.

    Xenia G.

    Xenia G.

    CEO

    Vienna, Austria

    Second-time founder. Previously led AI product teams in life sciences, robotics, and healthcare — shipping complex technical products from zero to market.

    LinkedIn →
    Alex C.

    Alex C.

    CTO

    Vienna, Austria

    8 years building data architecture and ML systems. Built RAG infrastructure serving 4,000+ users in regulated sectors like finance and healthcare.

    LinkedIn →
    Daria M.

    Daria M.

    Partnerships Director

    New York, NY

    Serial media entrepreneur — launched 3 news outlets. Advises global media orgs on AI strategy. Teaches AI & Media at Bard College.

    LinkedIn →