Eloquent AI’s Tugce Bulut on Probabilistic Architecture for Deterministic Business Outcomes

When traditional chatbots fail to answer basic questions, frustration turns to entertainment — a problem Tugce Bulut, Co-founder & CEO witnessed firsthand before founding Eloquent AI. In this episode of Chief AI Officer, she deconstructs how her team is solving the stochastic challenges of enterprise LLM deployments through a novel probabilistic architecture that achieves what traditional systems cannot. Moving beyond simple RAG implementations, she also walks through their approach to achieving deterministic outcomes in regulated environments while maintaining the benefits of generative AI's flexibility. 

The conversation explores the technical infrastructure enabling real-time parallel agent orchestration with up to 11 specialized agents working in conjunction, their innovative system for teaching AI agents to say "I don't know" when confidence thresholds aren't met, and their unique approach to knowledge transformation that converts human-optimized content into agent-optimized knowledge structures.

Topics discussed:

• The technical architecture behind orchestrating deterministic outcomes from stochastic LLM systems, including how their parallel verification system maintains sub-2 second response times while running up to 11 specialized agents through sophisticated token optimization.
• Implementation details of their domain-specific model "Oratio," including how they achieved 4x cost reduction by embedding enterprise-specific reasoning patterns directly in the model rather than relying on prompt engineering.
• Technical approach to the cold-start problem in enterprise deployments, demonstrating progression from 60% to 95% resolution rates through automated knowledge graph enrichment and continuous learning without customer data usage.
• Novel implementation of success-based pricing ($0.70 vs $4+ per resolution) through sophisticated real-time validation layers that maintain deterministic accuracy while allowing for generative responses.
• Architecture of their proprietary agent "Clara" that automatically transforms human-optimized content into agent-optimized knowledge structures, including handling of unstructured data from multiple sources.
• Development of simulation-based testing frameworks that revealed fundamental limitations in traditional chatbot architectures (15-20% resolution rates), leading to new evaluation standards for enterprise deployments.
• Technical strategy for maintaining compliance in regulated industries through built-in verification protocols and audit trails while enabling continuous model improvement.
• Implementation of context-aware interfaces that maintain deterministic outcomes while allowing for natural language interaction, demonstrated through their work with financial services clients.
• System architecture enabling complex sales processes without technical integration, including real-time product knowledge graph generation and compliance verification for regulated products.
• Engineering approach to FAQ transformation, detailing how they restructure content for optimal agent consumption while maintaining human readability.