Vivun – Enterprise AI Architecture
SRM
Enterprise AI Architecture

The discipline
behind intelligent
enterprise.

Domain reasoning models that enable AI to operate with structured knowledge, transparent logic, and real-world context.

Layer 01 — Foundation
Structured Knowledge
Organizational truth ingested into a persistent, queryable, auditable knowledge layer.
Layer 02 — Reasoning
Sales Reasoning Engine
The SRM applies constrained logic to real selling situations — answers verifiable against source material.
Layer 03 — Execution
Live Delivery
The reasoning layer surfaces inside live selling moments — before, during, and after every conversation.
SOC 2 Type II Certified
Enterprise Market Focus
Inspectable by Design
Research
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. With it, models 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 require chaining 8–12 inferential steps. 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. You're not paying for raw model capability — you're paying for the right architecture. A significant and durable cost advantage.