Many organizations rely on mission-critical legacy systems that, while stable, lack modern capabilities and hinder digital transformation efforts.
These established systems often hold vast amounts of valuable historical data and power core business processes. However, they can be difficult to modify, integrate with newer technologies, and lack the flexibility needed for today's AI-driven world. Replacing them entirely is often prohibitively expensive, risky, and time-consuming.
Vis Ed specializes in bridging this gap. We help you leverage the power of modern AI by integrating it intelligently with your existing legacy infrastructure. This allows you to enhance functionality, extract deeper insights from your data, and automate processes without abandoning your significant investments.
We prioritize minimizing disruption and maximizing the value of your existing assets.
Our integration methods focus on connecting *to* your legacy systems rather than modifying their core code whenever possible. This reduces risk, shortens project timelines, and avoids interrupting critical business operations.
You've invested significantly in your legacy systems. Our goal is to extend their lifespan and enhance their value by layering modern AI capabilities on top, rather than forcing costly replacements.
Unlock capabilities previously impossible with your legacy systems, such as predictive analytics based on historical data, intelligent automation of manual processes tied to the system, or natural language interfaces.
Ensure data can move securely between your legacy systems and modern AI tools. This allows AI to learn from legacy data and enables AI-driven insights or actions to be reflected back in the legacy environment.
We employ various techniques depending on your specific legacy system, goals, and constraints.
Leveraging existing APIs on the legacy system or building new APIs (if feasible) to enable direct, real-time communication with modern applications and AI services.
Using intermediary software (like our Vis Ed Integration Platform or other ESBs/iPaaS) to handle data transformation, protocol translation, and orchestration between systems.
Extracting data from legacy databases (batch or near real-time) and replicating it to modern data warehouses or lakes where AI models can access and analyze it efficiently.
Employing RPA bots to interact with legacy system UIs for data entry or retrieval, augmented with AI for decision-making, data interpretation, or handling exceptions.
The best approach depends on factors like the legacy system's technology, available interfaces, data volume, real-time requirements, and your budget. We work with you to determine the most effective and least intrusive integration strategy.
We implement robust security measures at the integration points, including secure protocols (HTTPS, SFTP), authentication/authorization layers, encryption of data in transit and at rest, and network segmentation to protect your legacy systems.
Our phased approach, focus on non-invasive techniques, and thorough testing in staging environments are designed to minimize downtime and impact on your core business operations during integration.
We utilize data transformation and mapping tools within middleware or integration platforms to handle differences in data formats, structures, and semantics between legacy and modern systems.
We carefully design integrations to avoid overloading legacy systems, often using asynchronous patterns, caching, and optimized data extraction methods to maintain performance.
Our team has experience reverse-engineering interfaces and data structures when documentation is missing, using specialized tools and discovery techniques to map out integration points.
The integration layer is built using modern, scalable technologies, ensuring it can handle growing data volumes and increased interaction frequency as AI usage expands.
Scenario: A company has decades of valuable customer data locked in an older relational database (e.g., DB2, Oracle Forms).
Integration: Set up a secure data extraction pipeline (e.g., using ETL tools or database connectors via middleware) to replicate relevant data into a modern data warehouse. AI/ML models are then run on this data warehouse to generate predictive customer insights, churn analysis, or segmentation, visualized in a modern BI tool.
Scenario: A financial institution relies on mainframe applications for core transaction processing, involving manual data entry and exception handling.
Integration: Deploy RPA bots trained to navigate the mainframe's terminal interface. Integrate these bots with AI services (via API) for tasks like: reading scanned documents (OCR), validating data consistency, classifying exception types, and making rule-based decisions, significantly reducing manual effort.
Scenario: An internal legacy application (e.g., inventory management) has a clunky, outdated interface but contains critical data.
Integration: Build a modern web or mobile front-end that communicates with the legacy system via a newly developed API layer or middleware. This new interface can incorporate AI features like natural language search for inventory items, predictive stock level alerts, or an AI assistant for common tasks.
Scenario: An established e-commerce platform built on older technology holds years of purchase history.
Integration: Extract and process historical transaction data. Train an AI recommendation model (e.g., collaborative filtering). Build an API for the recommendation engine. Integrate API calls into the legacy platform's product pages or checkout process to display personalized product suggestions.
Contact us today to discuss how Vis Ed can design and integrate the right AI solution for your organization. Whether you're taking your first steps into AI adoption or looking to enhance existing capabilities, our team has the expertise to guide your journey.
Let's unlock the future of possibility togetherReach out to discuss your AI integration needs or schedule a consultation with our experts. We're here to answer your questions and explore how Vis Ed can help.