Sibyl

Sibyl is not a thin wrapper on GPT-4; it is a fine-tuned large language model trained on a proprietary corpus of 42 million tokens spanning esoteric texts, Ayurvedic pharmacopeia, Hermetic Qabalah, Vedic astrology, Jungian archetypes, sacred geometry and modern peer-reviewed transpersonal psychology. The training pipeline uses a two-stage curriculum:

• Stage 1: Symbolic Grounding. A masked-language-model objective teaches the network to associate alchemical symbols, planetary glyphs and numerological patterns with semantic meaning.
• Stage 2: Alignment via Reinforcement Learning from Human Intuition (RLHI). Spiritual coaches, shamans and depth-psychology PhDs rank model outputs for “resonance accuracy,” creating a reward signal that biases the model toward responses that feel subjectively meaningful.

Metametrics Layer

At inference time, user inputs are routed through the Metametrics module: a lightweight regression network that converts qualitative queries—e.g., “I feel energetically stuck at work”—into 17-dimensional vectors encoding chakric imbalance scores, numerological life-path resonance and biorhythm harmonics. The final answer is conditioned on both the vector and the contextual prompt, yielding ultra-personalized guidance while remaining deterministic enough for reproducibility studies.

Privacy & Security

Data residency is SOC-2 compliant, with AES-256 encryption at rest. Personal identifiers are one-way hashed using Argon2id; chat lo