DeepVista Blog

We're excited to share our vision for DeepVista - a platform that's reimagining how AI agents can transform the way we work and interact with technology.

Our Vision

At DeepVista, we believe AI agents should be more than just tools - they should be intelligent partners that understand context, learn from interactions, and seamlessly integrate into your workflow. Our platform is designed to bridge the gap between human creativity and AI capability.

What Makes DeepVista Different

• Context-aware AI agents that understand your specific needs
• Seamless integration with existing workflows and tools
• Continuous learning and adaptation to user preferences
• Privacy-first approach to data handling

Watch Our Vision

Looking Ahead

This is just the beginning of our journey. We're committed to building AI agents that don't just execute tasks, but truly understand and enhance human potential. Stay tuned for more updates as we continue to develop and refine our platform.

We're embracing the power of building in public and co-creating with our community. This week, we want to share our philosophy on transparent development and collaborative innovation.

#BuildingInPublic

Building in public isn't just about transparency - it's about creating a feedback loop that helps us build better products. By sharing our journey, challenges, and learnings openly, we invite our community to be part of the solution.

Co-creation with AI Agents

Our vision extends beyond just using AI agents as tools. We're exploring how humans and AI can truly co-create, combining human intuition and creativity with AI's analytical power and scalability.

Community-Driven Innovation

• Open feedback channels for feature requests and improvements
• Regular community updates and progress sharing
• Collaborative problem-solving sessions
• Beta testing programs for early adopters

Join Our Journey

We believe the best products are built together. Whether you're a developer, designer, entrepreneur, or someone passionate about AI, we invite you to join our community and help shape the future of AI agents.

Watch Our Progress

Have you been using ChatGPT to write emails and need to copy the same context over and over again? Do you have files and conversations scattered across 50 ChatGPT conversations that you cannot find?

If you have such problems, DeepVista is for you. DeepVista helps you track all the context across all conversations you have. Remembers them when you need them, and gives you on-point messages.

The Limitations of Current RAG Systems

Today's Retrieval-Augmented Generation (RAG) systems follow a relatively straightforward paradigm. They retrieve relevant information using rule-based systems—typically employing cosine similarity to find the top-k most relevant results—and then present this context to a large language model for processing.

From Engineering to Modeling: A Paradigm Shift

The conventional approach of context engineering focuses on creating more sophisticated rules and algorithms to manage context retrieval. However, this misses a crucial opportunity. Instead of simply engineering better rules, we need to move toward context modeling—a dynamic, adaptive system that generates specialized context based on the current situation.

Learning from Recommendation Systems

The architecture for context modeling draws inspiration from the well-established two-stage recommendation systems that power many of today's most successful platforms.

The Context Modeling Solution

• Adaptability: Unlike rule-based systems, context models can learn and adapt to new patterns and user behaviors over time.
• Personalization: These models can be trained on user-specific data, creating truly personalized AI experiences.
• Efficiency: By using smaller, specialized models for context generation, the system maintains efficiency.
• Developer Control: Context modeling provides agent developers with a trainable component they can influence and improve.

Watch: Deep Dive into Context Modeling

The Infrastructure Opportunity

Context modeling represents a common infrastructure need across the AI industry. As more organizations deploy RAG systems and AI agents, the demand for sophisticated context management will only grow.

Looking Forward

The future of personalized AI lies not in building ever-larger language models, but in creating intelligent systems that can effectively collaborate with these powerful but inflexible models. Context modeling represents a crucial step toward this future.