Tugce Bulut, Co-founder and CEO of Eloquent AI, dives into the fascinating world of probabilistic architecture aimed at achieving deterministic business outcomes. She discusses the innovative methods her team employs, like using up to 11 specialized agents for real-time responses and teaching AI when to admit uncertainty. Tugce shares insights on optimizing AI for customer service, addressing the importance of regulations, and transforming knowledge structures for efficiency. Get ready to explore the cutting-edge of conversational AI!
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question_answer ANECDOTE
Parallel Multi-Agent Architecture
Eloquent AI runs 7 to 11 agents in parallel to improve response accuracy and speed compared to single agent chatbots.
One agent clarifies ambiguous questions, another verifies the answers before response, and the system can escalate if confidence is low.
volunteer_activism ADVICE
Reduce Costs with Domain Models
Optimize AI agent costs by training domain-specific models embedding enterprise rules and reasoning.
This reduces token usage and cost, allowing outcome-based pricing with significantly cheaper resolution costs.
insights INSIGHT
Proof in Side-by-Side Testing
Customers understand AI agent differences best through side-by-side testing on their hardest questions.
Success is measured by accuracy, resolution rate, and proportion escalated to humans, not by technical explanations.
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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.
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