Combining Generative AI with Automation and APIs: realizing AI at scale

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Generative AI models and similar architectures are known for their impressive and versatile features. These models have revolutionized natural language understanding and generation.

The Generative capabilities like text generation, translations, summarization, and keyword/metadata extraction are applicable in various industries in the areas of marketing automation, customer relation management applications, ERP, Promotion, and Loyalty applications and are seamlessly integrated into the MAGE platform through scalable and highly available microservice containers with API driven approach.

Reusable components like data synthesizers, semantic search, and metadata extractions are integrated with automation processes that are event-driven/batch schedulers through configuration or metadata-driven approaches, which effectively can be used for any of the Model training processes like building recommendation/prediction/classifier engines, etc.

How MAGE platform Generative AI APIs solve Industry use cases.

MAGE platform Generative AI APIs are driven with a template-based approach that standardizes and simplifies usability and development efforts and maintains consistency across system.

Based on different scenarios, the MAGE platform Generative AI APIs are integrated with Prompt Engineering frameworks that create prompt requests and send them to the Generative AI models, integrated with semantic search flows, fine-tuning models, and integration with other system and enterprise APIs based on the requests.

A few of them are listed below and used in various industry use cases.

APIs for the customer journey in a transactional-based system

Scenario for order management system

Generative AI can power chatbots or virtual travel assistants that engage with users in natural language. These AI-powered assistants driven with API based integrations can handle a wide range of tasks, including ordering products, providing product recommendations, answering questions, and assisting with pricing details.

API for content personalization:

Scenario for Marketing and Automation

The content that has to be personalized targeting users based on dynamic profiling/segmentation can leverage MAGE Platform Generative AI API, which personalizes the content based on user profiles and targets to generate personalized email campaigns. The content that has to be personalized targeting for channel-friendly can leverage MAGE platform Generative AI APIs for channel-friendly content creation that personalizes the content based on the targeted marketing channel.

Scenario for Recommendation Engines

Various recommendation engines can integrate with the MAGE platform Generative API for providing personalized recommendation content to the targeted users and channels for product promotions.

API for metadata extraction/summarization:

Scenario for Insurance Industries

Extract relevant information from claim forms, such as claimant details, incident dates, descriptions of events, and supporting documents. Summarize claim documents to give claims adjusters a quick overview of the claim, helping them make faster and more informed decisions. Generate summaries of applicant data, making it easier for underwriters to assess risk and determine policy eligibility.

Scenario for Retail Industries

Create inventory summaries to provide an overview of stock levels, restocking needs, and product categories. Summarize competitor data to identify pricing trends, product assortments, and market positioning. Summarize customer sentiments and reviews to gain insights into product satisfaction and identify areas for improvement.

API for audio-text-iMAGE conversions

Scenarios for healthcare industries:

Digital scribe agent that captures a doctor’s conversation with the patient, transcribes the audio to text, translates to the required language, creates the summary from the transcribed text

Scenarios for Insurance industries:

Claims agent that can receive audio files from insures, transcribes to text, extracts the metadata, and determine the severity of the loss Property iMAGE interpretations converted to the text used in dynamic underwriting use cases.

Generative API’s scalability and automation

With APIs built in through the MAGE platform for all the Generative AI capabilities, the platform provides seamless integrations to external systems/partners.

These APIs have the capability to integrate with the workflow management that caters to end-to-end business capabilities.

MAGE platform user interface built with micro-frontends are integrated with scalable approach through backend APIs.

Given the core components for the MAGE platform for Data ingestion, Auto EDA, data transformation, and scalable AI model components integrations, the Generative AI APIs can be seamlessly integrated with integration services that have the capability to build rule engines, workflows, etc.

For example, a retail industry looking to build a robust Recommendation system can leverage the MAGE platform, integrating with Data ingestion, EDA, and transformation process that connects to different data sources related to products, customers, inventory data, etc, and use Generative AI APIs of MAGE platform that can synthesize the required data for model training, build the model and deploy the model for recommendations.

As required, the MAGE platform Generative API can integrate with other deep learning/ML-based algorithms for data synthetization, classification, and enrichment, making more model accuracy.

The APIs are integrated with the MAGE MLOps platform for continuous builds and deployments, monitor and check for any model drifts, and trigger the hyperparameter tunings or model fine-tunings if there are any drifts.

The APIs are built with a Microservice framework with core features like service discovery, resiliency through circuit breaker, and load balancing, ensuring the services are highly auto-scalable and highly available, ensuring zero downtime.

Generative API Security and Data security

The Generative APIs are integrated with enterprise security leveraging OAuth2/OpenID connect. The APIs for synthetic data generator adheres to data privacy, compliances, and reduced model bias.

Automation

MAGE platform automation is spread over different areas, from Data Engineering(ETL) to Industry Use cases.

ETL process

MAGE Platform ETL process integrated with Language Model (LLM) assistants powered by NLP, allowing business users to query domain-specific entities and fetch data effortlessly. ETL framework enables seamless connections to multiple data sources, facilitating easy data ingestion, transformations, and visualizations.

Auto EDA framework

The MAGE Platform, Auto EDA framework, leverages NLP to provide insights into data completeness, quality, and summary. Users can visualize correlations, helping them identify and resolve data issues swiftly. The framework is integrated with MAGE platform APIs seamlessly from any channel.

Web Scraping

The MAGE platform has built automation scripts to scrape large amounts of text or data from websites, social media, or other online sources. This data can be used as training material for generative AI models.

Document Parsers

The MAGE platform has in-built automation scripts to parse large amounts of text or data from Word and Excel documents and iMAGEs with OCR capabilities. It extracts key information that can be given to Generative AI models.

MLOps

MAGE platform MLOps uses different practices and tools to streamline and automate the end-to-end machine learning lifecycle, from model development to deployment and monitoring with auto. MLOps can be integrated with various automation workflows to enhance automation capabilities in the context of machine learning and data science. The MAGE platform for MLOps can automatically track and version machine learning models and associated artifacts, making it easy to roll back to previous versions if necessary. Leverages automated monitoring of model performance, data drift, and system health to trigger alerts and notifications when anomalies are detected and retrigger the training pipeline. Triggers Responsible AI for explainability, Bias reporting, Mitigation, and Data anomaly identification that has integration with various APIs for reporting and data ingestion.

Automated Testing

The MAGE platform has the capability for automation testing to compare model versions and determine which performs best in production. Uses Generative AI APIs for A/B testing for different versions of data and content.

Error Handling and Remediation

The MAGE platform has inbuilt Automation scripts that can use APIs to monitor system health and respond to issues automatically.

Infrastructure as Code (IaC):

MAGE Platform leverages IaC modules like Terraform, which uses APIs to provision and manage infrastructure resources. Infrastructure changes are defined in code and executed through API-driven automation.

Resource Scaling:

MAGE Platform uses cloud autoscaling features to add or remove instances, allocate more CPU or memory, or adjust network configurations in response to traffic changes.

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