Informatica Delivers Blueprints to Simplify and Accelerate Generative AI Development on Leading Technology Platforms

Informatica, a leader in enterprise AI-powered cloud data management, today announced the availability of Generative AI (GenAI) Blueprints which make it faster and easier for customers to build enterprise-grade GenAI applications on six industry-leading technology platforms.

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“Informatica’s GenAI Blueprints are a development for both the industry as well as for our customers as they elevate the practice of developing GenAI applications to meet enterprise requirements for delivering responsible GenAI at scale.”

The blueprints — for AWS, Databricks, Google Cloud, Microsoft Azure, Oracle Cloud and Snowflake — include standard reference architectures, prebuilt, ecosystem-specific “recipes” and GenAI Model-as-a-Service and vector database connectors to minimize GenAI development complexity and accelerate implementation.

These comprehensive and easy-to-follow blueprints are designed to facilitate the use of AI-ready data as organizations seek to develop and quickly generate business value from GenAI applications. The blueprints comprise architectural guidelines and pre-defined configurations for use with Informatica’s Intelligent Data Management Cloud (IDMC) platform and leading cloud data platforms. Professional services firms such as Deloitte and Capgemini, are building their GenAI platforms on top of Informatica blueprints to bring industry-specific capabilities and value-added services and IP on top of the company’s foundational GenAI blueprints. The blueprints address a range of data and metadata-driven requirements that are common to GenAI initiatives, including data discovery, data engineering, master data management (MDM), access control and policy enforcement.

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Key capabilities of the blueprints include:

• Data Quality and Master Data Management (MDM) to ensure Gen AI applications are grounded and enriched by high-quality enterprise data for key business stakeholders such as customers, products and suppliers.
• Use of business glossary metadata and data governance to ensure that Gen AI applications are optimized for the industry and enterprise.
• A no-code approach to allow scalable, robust scaffolding of projects aimed at delivering and managing Gen AI apps.
• Support for responsible AI with policy and security enforcement to ensure data is included in Gen AI prompts and responds appropriately and securely.
• Accelerate Gen AI app development with pre-built, no-code, ecosystem-specific iPaaS recipes for AWS, Azure, Google and Oracle using the rapid iPaaS integration and orchestration capabilities of the IDMC platform.
• Scaling up with high throughput/low latency serverless runtime for integrating AI agents and RAG-chain executors and boosting efficiency through a modern design interface.

“As a strategic alliance of Informatica, we’re excited about the potential of these blueprints to deliver industry-specific GenAI capabilities and value-added services. Business leaders are looking for ways to implement GenAI to meet exacting enterprise requirements, from grounding to contextualization, that require a robust foundation of data management,” said Jim Rowan, Principal and Applied AI Leader at Deloitte Consulting LLP. “Informatica’s GenAI Blueprints are a development for both the industry as well as for our customers as they elevate the practice of developing GenAI applications to meet enterprise requirements for delivering responsible GenAI at scale.”

“A myriad of components must be brought together for GenAI development, including large language models, vector data management, prompt handling and more,” said Rik Tamm-Daniels, Group Vice President of Strategic Ecosystems at Informatica. “These blueprints provide a prescriptive path for customers to unlock the potential of GenAI for their enterprise use cases. Informatica is continuing to simplify and streamline the process of delivering GenAI applications at scale by minimizing the do-it-yourself integration required. And, most importantly, we do so with AI-ready data that is relevant, responsible and robust.”

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