Lack of agility
Adding new revenue generating capabilities requires coordinating across a complex web of teams/services.
Architecture Simplification
Simplify your AI architecture
Framework limitations often introduce application-level complexity and necessitate costly resources for maintenance. Simplify your architecture with Superduper by eliminating data pipelines and consolidating various functions of your AI infrastructure into your databases. Save costs while enhancing resilience, scalability, and agility.
Build, deploy and manage
AI efficiently and flexibly
Category
- Data
- Infrastructure
- Code
- Management
- Hardware
- Security
Traditional/Standard Approach
- Data duplication in multiple locations and data pipelines, leading to increased latency, costs, security vulnerabilities, and management overhead.
- Complex architecture with multiple components and deployments to be stitched together.
- Various components require glue code in different languages and environments, often combined with vendor lock-in, lacking open-source ownership.
- Plenty of work with deployment, scaling, securing, and managing data pipelines across different environments.
- Hardware and vendor lock-in resulting in low flexibility and inability to optimize costs by moving to cheaper compute.
- Scattered security and governance on the data and application layers, resulting in massive management overhead and vulnerabilities.
Superduper
- Data stays where it is, with compute running where data resides, limiting data movement and costs.
- A scalable deployment that unifies all AI and data components services in a single platform.
- A simple Python interface with reusable building blocks and complete application templates enabling developers to build custom enterprise AI applications super fast.
- No infrastructure work at the application level, allowing focus on building and improving applications.
- Full hardware flexibility with the ability to distribute workloads to any hardware easily with containerized architecture.
- Relying on existing security and rights management controls of the datastore making it easy to stay in control.
| Category | Traditional/Standard Approach | Superduper |
|---|---|---|
| Data | Data duplication in multiple locations and data pipelines, leading to increased latency, costs, security vulnerabilities, and management overhead. | Data stays where it is, with compute running where data resides, limiting data movement and costs. |
| Infrastructure | Complex architecture with multiple components and deployments to be stitched together. | A scalable deployment that unifies all AI and data components services in a single platform. |
| Code | Various components require glue code in different languages and environments, often combined with vendor lock-in, lacking open-source ownership. | A simple Python interface with reusable building blocks and complete application templates enabling developers to build custom enterprise AI applications super fast. |
| Management | Plenty of work with deployment, scaling, securing, and managing data pipelines across different environments. | No infrastructure work at the application level, allowing focus on building and improving applications. |
| Hardware | Hardware and vendor lock-in resulting in low flexibility and inability to optimize costs by moving to cheaper compute. | Full hardware flexibility with the ability to distribute workloads to any hardware easily with containerized architecture. |
| Security | Scattered security and governance on the data and application layers, resulting in massive management overhead and vulnerabilities. | Relying on existing security and rights management controls of the datastore making it easy to stay in control. |
Challenges with complex AI infrastructure:
High costs
Multiple software services waste storage, network, and compute resources as well as engineering time.
Risk of downtime
Complex runbooks for disaster recovery force ops teams to stretch their time and expertise across many transactional systems.
AI app templates ready-to-install on your database
Our highly configurable AI app templates enable enterprises to implement custom AI solutions with near zero development work. If you are looking into use-cases we have not covered yet, reach out here!
Document Key-Value Extraction + RAG
Accurately retrieve mission-critical information from your documents and store them in your database for downstream business operations like accounting, analytics, and decision-making.
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In-Database RAG multi-modal
Configure custom RAG applications, such as AI chatbots, directly on your database.
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Visual object detection
Identification and tracking of multiple objects in images or videos, enabling analytics and surveillance applications in retail, manufracturing, healthcare and more.
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Vector Embedding Generation
Integrate vector embedding models and APIs with your existing databases to generate embeddings for your data and perform vector search without moving your data.
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Custom Workflows
Schedule a meeting If you are interested in a custom AI application or workflow that we haven’t covered yet.
Start implementing AI
with your data todayStart implementing AI
with your data today
Reach out to us to learn more about Superduper, our enterprise solutions and how they help you to solve your AI challenges.