How You Can Leverage AI-enhanced RPA integration and Gen AI to build smarter, efficient and user-friendly applications.

Building AI-enhanced RPA application for process automation can unlock up to 40% and in some cases 80% cost savings in CAPEX as well OPEX. Building smarter apps with AI is not just about embedding machine learning models—it requires a strong foundation in platform upgrades, legacy application integration, and API services to ensure scalability, performance, and seamless user experiences.

Here is why our expertise in these areas is critical

Some notable applications that we implemented for leading US financial institutions include the following:

• Application to help route 3000+ Call/Hours to the Right Customer Service Agents
• RPA solution with UiPath that led to 90% reduction in claims document processing and decision time
• Application integrated with Agent AI for 401(K) process $25000 Dollar savings in monthly OPEX costs
• Automated Microsoft Teams desktop application using Automation Anywhere
• Integrate Automation Co-Pilot with Microsoft Teams for a Leading Financial Services Company
• 80% reduction in correction calls with enhanced customer support application for personal, commercial, property, and vehicle insurance

How we save product leaders from challenges involving custom application development:

1. No more wasted resources

On-board the best talent for your projects with the flexibility by letting us set up your Offshore Software Development Center.

2. Complete Knowledge of ROI

Ensure resources are deployed after a comprehensive study of market rates, ROI, and savings that can be unlocked with a dedicated Center of Excellence (CoE).

3. Reduced Cost of Product Development

Ensure the future scope of rework on a piece of code or alterations and iterations to any newly launched application is included in the project cost.

4. Outsource Infrastructure Management

Get the right people at the best cost for your application maintenance using our expertise in data, runtime, middleware, and operating systems.

How we can help you with our skills and expertise to build smarter apps with AI: Platform Upgrade, Legacy Application Integration, and API Services

1. Platform Upgrades Ensure AI Apps Run on Modern, Scalable Infrastructure

AI-driven apps require high computational power, scalable architecture, and real-time processing capabilities. Without platform modernization, AI applications may suffer from performance bottlenecks.

Why Platform Upgrades Matter for AI

AI workloads require modern cloud-native architectures (e.g., AWS, Azure, GCP) with auto-scaling capabilities.

AI models need GPU and TPU acceleration for training and inference, which legacy systems may not support.

Microservices-based architecture enhances AI deployment and reduces monolithic dependencies.

Upgrading to serverless or containerized environments (e.g., Kubernetes, Docker) optimizes cost and scalability.

Example: Upgrading from an on-premise monolithic system to a cloud-native microservices architecture allows an AI-driven chatbot to process thousands of customer interactions per second without downtime.

2. Legacy Application Integration Bridges the Gap Between Old & New AI Capabilities

Many enterprises still rely on legacy applications that were not designed to support modern AI functionalities. Expertise in legacy integration is essential to enable AI capabilities without disrupting existing workflows.

Challenges & Solutions in Legacy AI Integration

Data Silos: Legacy systems store data in inaccessible formats → Use ETL pipelines, APIs, and middleware to enable AI access.

Outdated Infrastructure: AI models need real-time data processing, but legacy systems rely on batch processing → Implement event-driven architectures using Kafka or RabbitMQ.

Security & Compliance Issues: AI needs access to sensitive legacy data → Implement secure API gateways and role-based access controls (RBAC).

Example: An insurance company using a 20-year-old claims processing system integrates an AI-based fraud detection engine via REST APIs and middleware, allowing real-time risk assessment while keeping the core system intact.

3. API Services Enable AI Models to Seamlessly Communicate Across Systems

AI applications must interact with multiple services, databases, and external platforms. APIs serve as the bridge between AI models and business applications, ensuring seamless data flow and automation.

Why API Services Are Crucial for AI Apps

Real-time Data Exchange: AI models require real-time access to structured and unstructured data via APIs.

Model Deployment & Access: AI models must be exposed as RESTful or GraphQL APIs for integration with front-end applications.

Cross-Platform Compatibility: APIs allow AI functionalities to be embedded in web, mobile, IoT, and enterprise applications.

Security & Governance: API management platforms (Apigee, Kong, AWS API Gateway) help in securing AI APIs and enforcing rate limits.

Example: A retail company uses an AI-powered recommendation engine via an API to deliver personalized product suggestions in its mobile app, website, and chatbot—all in real-time.

Highlights of Key Technologies Expertise that you Require

Cloud & Platform Upgrades: AWS SageMaker, Azure AI, GCP Vertex AI, Kubernetes, Docker

Legacy System Integration: Apache Kafka, RabbitMQ, ETL Pipelines, Middleware (MuleSoft)

API Development & Management: FastAPI, GraphQL, Apigee, AWS API Gateway, Kong

Conclusion: AI Needs a Strong Tech Foundation

• Platform Upgrades ensure scalability and efficiency for AI workloads.
• Legacy Application Integration bridges old and new technologies, enabling smooth AI adoption.
• API Services provide seamless connectivity between AI models and business applications.