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World Congress 2026 North America • Sep 25, 2026 • Session details

Building AI that fits your business

Benny Chen

Is vendor lock-in draining your AI budget? Discover how transitioning to fine-tuned, open models can cut costs by 10x while maintaining strategic control.

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#1 about 5 min

The impact of specialized intelligence models

Custom models deliver higher success rates and better unit economics than general-purpose ones.

#2 about 3 min

Evaluating build versus buy for AI capabilities

Owning evaluation and improvement loops allows for objective assessment of closed-source vendors.

#3 about 3 min

Navigating model selection and fine-tuning strategies

Iterating on closed models paves the way for deploying and fine-tuning open alternatives.

#4 about 2 min

Determining model and infrastructure ownership for AI applications

Organizations must weigh strategic trade-offs between managed APIs, model ownership, and complete infrastructure control.

#5 about 3 min

Building data pipelines and evaluation layers

Curating production feedback into golden sets enables reliable model grading and training.

#6 about 1 min

Optimizing unit economics for successful tasks

Reducing operational costs ensures sustainable quality for high-volume agent and model deployments.

#7 about 2 min

Tracking performance using the specialized intelligence index

Domain-specific benchmarks help monitor model capabilities across healthcare, legal, and finance sectors.

#8 about 2 min

Designing technical architecture for continuous evaluations

Versioned repositories and explicit interfaces allow teams to rapidly adapt to evolving model capabilities.

#9 about 3 min

Structuring teams to own the improvement loop

Hybrid ownership models balance product and platform responsibilities better than isolated teams.

#10 about 3 min

Summary of AI implementation and ownership decisions

Reviewing the five core decisions ensures the creation of a sustainable AI supply chain.

#11 about 2 min

Transferring model capabilities through reinforcement learning

Capabilities transfer effectively within narrow domains when applying reinforcement learning on prior model versions.

#12 about 2 min

Leveraging open models alongside RAG systems

Continuous evaluation is necessary to capitalize on frequent model releases alongside retrieval generation.

#13 about 2 min

Controlling token usage and data quality

Increasing production volume requires transitioning from experimental token consumption to sustainable gross margins.

#14 about 2 min

Ensuring security with on-premise model deployments

Heavily regulated industries rely on on-premise and air-gapped models to maintain strict data perimeters.

Matching moments

3:20 min

Strategic advice for leveraging open models in software development

Mitesh Patel Mitesh Patel +3 · World Congress 2026 North America

1:27 min

Key takeaways and open-source resources for model evaluation

Viktoria Semaan Viktoria Semaan · World Congress 2026 North America

4:26 min

Navigating intelligence, speed, and cost trade-offs in AI models

Viktoria Semaan Viktoria Semaan · World Congress 2026 North America

6:00 min

Navigating competition and infrastructure in enterprise AI

Ash Ryan Arnwine Ash Ryan Arnwine +3 · World Congress 2024

2:53 min

The impact of open source models on industry dynamics

2:14 min

Balancing AI investment with product shipping commitments

Ajita Kanchivakam Ananth Ajita Kanchivakam Ananth · World Congress 2026 North America

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