Easy Data Source Integration: Simplifies this process by providing foundations for easily integrating disparate data sources into a unified Data Lake for Real-time analytics.
Simplified AI Development: Leverage pre-built Vertex AI integrations and out-of-the-box AI functionalities to build AI models without extensive talent or tooling requirements.
Faster Time-to-Value: Our solution accelerates the path to data-driven insights with real-time analytics capabilities and a foundation for rapid AI development.


Transform Your Data. Empower AI-Driven Decisions.

Leverage pre-built Vertex AI platform integrations and a comprehensive 3-in-1 database for transactional, analytics, and AI vector data management. Achieve faster time-to-value and empower informed decision-making through real-time insights and next-generation AI capabilities.

The Main Problem

Looking For A Way To Extract Value From Your Data?

Many businesses struggle to integrate data from disparate sources, which is a significant challenge to real-time analytics and AI adoption. Building and maintaining these capabilities often requires significant investments in talent, technology, and infrastructure, leading to slow time-to-value.

Data Integration Challenges
Slow Time-to-Value
Resource Constraints & High Costs

leveraging the power of Google Cloud

Your premium consulting partner for data and AI

Request the 1-Pager

Pain Point #1

Performance Degradation

Disparate data sources create information silos, hindering the ability to gain a holistic view of the business. Legacy databases often operate in isolation, preventing valuable data from different departments from being easily combined and analyzed. This fragmented data landscape makes it difficult to:

  • Identify trends: Analyze data across departments to uncover patterns and opportunities for improvement.
  • Optimize operations: Gain insights to streamline processes and workflows.
  • Understand the customer base: Develop a complete customer profile by integrating data from various touchpoints.
Checklist header

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique.

Pain Point #2

Taking Too Long to See Results from AI

Even with the potential of AI to unlock valuable insights from data, legacy databases can create a significant bottleneck. Integrating legacy databases with modern AI tools can be a complex and time-consuming process due to compatibility issues and differing data formats. Legacy databases often store data in formats that are incompatible with modern AI tools.  

This incompatibility creates the need for data transformation before it can actually be used to train AI models, while transforming the data to meet the required format can be a time-consuming and resource-intensive process, significantly delaying the deployment of the model and its impact.

Pain Point #3

Limited Resources for AI Development

The high cost associated with traditional AI development can be a significant barrier for many businesses:

  • High Talent Cost:  The expertise required to build and maintain AI models can be expensive, particularly for smaller businesses with limited resources.  This can limit their ability to leverage AI for competitive advantage.
  • Technology and Infrastructure Costs:  The cost of acquiring the necessary hardware, software, and cloud infrastructure for AI development can be prohibitive for some businesses.  Smaller businesses might struggle to invest in the necessary resources to get started with AI initiatives.
  • Lengthy Development Cycles:   Traditional AI development processes can be time-consuming, requiring specialized skills and significant upfront investment. This can delay the time it takes to see a return on investment (ROI) from AI initiatives.  A growing startup looking to leverage AI for product recommendation might find the process of hiring data scientists and building the necessary infrastructure to be too slow and expensive, challenging their ability to innovate and compete.

Solutions tailored to you

Here's how we can help you solve all that

No items found.

Don't just hear it from us

JP Grace
Chief Technology Officer

As our customer base grew, we ran into PostgreSQL vertical scalability limits and problems like CPU, memory and connection exhaustion. We were thrilled the solution gave us a drop-in PostgreSQL replacement with much more efficient reads and writes. The solution requires less CPUs to hit our throughput and latency goals, lowering our cost by 40-50% and preparing us for the next phase of customer growth.

No items found.

See How It Worked For Other Businesses

No items found.