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World Congress 2026 Europe • Jul 9, 2026 • Session details

AI That Fits Your Business, Not the Other Way Around

Anshul Jindal , Cansu Kavili Örnek

Why force your data into rigid cloud ecosystems? Deploy specialized models directly to your infrastructure using RAG and quantization to ensure data sovereignty and maximize hardware ROI.

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#1 about 3 min

Bridging the gap between generic models and enterprise data

Delivering real business value requires bringing scattered enterprise data closer to generic models through alignment approaches.

#2 about 2 min

Structuring scattered data for effective model engagement

Disconnected organizational data like ticket systems and internal documentation must be uniformly represented for model ingestion.

#3 about 2 min

Adapting generic models through domain-specific fine-tuning

Embedding domain knowledge into generic models empowers them to behave like knowledgeable industry colleagues without training from scratch.

#4 about 1 min

Leveraging document intelligence for better data retrieval

Converting complex PDFs and tables into markdown files enables seamless contextual integration using retrieval augmented generation.

#5 about 2 min

Overcoming data scarcity with synthetic data generation

When internal data is insufficient for teaching new tricks, synthetic data generation pipelines provide the volume needed to effectively train models.

#6 about 3 min

Building and fine-tuning models with the NeMo framework

Leveraging containerized microservices operating on scalable clusters facilitates robust pre-training and custom proprietary data model fine-tuning.

#7 about 1 min

Orchestrating toolkits for agentic workflows and observability

Implementing agent-specific toolkits allows optimized routing between sequential tasks while analyzing performance timing across complex workflows.

#8 about 3 min

Implementing on-premise deep research agentic workflows

Deploying localized research agents empowers organizations to conduct variable depth data discovery without exposing sensitive intellectual property.

#9 about 2 min

Navigating data sovereignty and hardware footprint limitations

Maintaining internal data compliance mandates deploying appropriately sized models that balance necessary capabilities against expensive GPU demands.

#10 about 3 min

Reducing hardware requirements through model quantization

Compressing frontier models retains critical accuracy margins while significantly decreasing the necessary graphical processing compute required for deployment.

#11 about 3 min

Progressing gracefully from generic chatbots to agentic workflows

As automated systems transition into acting on complex reasoning, platforms must support flexible infrastructure capabilities to handle escalating compute costs safely.

#12 about 4 min

Optimizing inference costs through efficient distributed runtimes

Integrating high-performance serving systems with intelligent traffic distribution maximizes resource utilization and minimizes expensive operational inference costs.

#13 about 2 min

Scaling models consistently across diverse deployment environments

Unified enterprise platforms systematically standardize capabilities like document intelligence and distributed serving regardless of backing infrastructure setups.

#14 about 2 min

Constructing scalable AI factory stack blueprint architectures

Combining operating systems, accelerator operators, and sandbox environments delivers a robust hardware framework for secure localized model deployment.

Matching moments

1:24 min

Training small AI models on secure private data

Alexandre Guenoun Alexandre Guenoun +3 · WWC Europe 2026

1:39 min

Balancing human-centric AI collaboration with environmental sustainability practices

Madalena Costa Madalena Costa · WWC 2024

50 sec

Prioritizing enterprise data in generative AI tooling and development

David Leconte David Leconte +1 · WWC 2024

3:13 min

Embedding generative AI in enterprise software platforms

Mike Butcher Mike Butcher +3 · WWC 2024

6:00 min

Navigating competition and infrastructure in enterprise AI

Ash Ryan Arnwine Ash Ryan Arnwine +3 · WWC 2024

2:52 min

Scaling generative AI use cases across large enterprises

Mike Butcher Mike Butcher +3 · WWC 2024

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