Vivun – Reasoning Fidelity
AI Architecture

Our domain-specific model outperforms
both incumbents and foundational models

Most AI models collapse under the weight of complex B2B reasoning. We built the architecture that doesn't — by combining best-in-class foundational models with structured domain knowledge.

Mean Reasoning Fidelity Score by Distance (Hops)
Revealing performance without ontology…
WITH ontology
WITHOUT ontology
Quick select:
~45 pt gap at hop 6
Hop 6

Solid lines = WITH ontology  ·  Dashed lines = WITHOUT ontology  ·  Click any toggle above to isolate. Higher score = stronger logical reasoning.

Why it works
~97%
Structured knowledge preserves fidelity at every hop
Ontologies give AI a structured map of domain knowledge — relationships, rules, and context — that prevents reasoning from degrading as complexity grows. Without this structure, models are guessing across long inference chains. With it, they stay anchored to ground truth at every step, maintaining near-perfect fidelity even across 11+ reasoning hops.
The B2B problem
~50%
Fidelity lost by hop 6 — without ontology
Meaningful B2B sales tasks — researching an account, building a business case, mapping a buying committee — require chaining 8–12 inferential steps. But without structured knowledge, even the most capable models hit a reasoning cliff around hop 3. By hop 6, performance has roughly halved. By hop 11, most foundational models are effectively guessing.
The business case
≈0 pt
Performance gap between models — with ontology
With structured knowledge in place, model tier becomes nearly irrelevant. GPT-4.1 with an ontology matches GPT-5 without one. This means Vivun delivers top-tier reasoning using smaller, faster, cheaper models. You're not paying for raw model capability — you're paying for the right architecture. That's a significant and durable cost advantage.