Your legacy systems hold decades of business logic and critical data. Learn how to unlock AI capabilities without risky rewrites or disrupting operations.
Organizations face a critical dilemma: legacy systems running core business processes can't easily adopt AI, yet wholesale replacement is too risky and expensive. Common challenges include:
Critical data locked in mainframes, AS/400 systems, or proprietary databases that AI models can't access directly.
Legacy systems built decades ago lack modern APIs, making real-time AI integration nearly impossible without custom development.
Modern AI frameworks (Python, TensorFlow) don't communicate with legacy languages (COBOL, RPG, Fortran) or outdated protocols.
Mission-critical legacy systems can't afford downtime for AI integration experiments, creating organizational paralysis.
These architectural approaches enable AI capabilities without replacing your legacy infrastructure.
Deploy an API gateway layer between legacy systems and AI services, translating modern REST/GraphQL calls into legacy protocol requests.
Organizations with mainframes (IBM z/OS, AS/400) needing real-time AI predictions integrated into existing workflows.
Monitor legacy databases for changes and stream updates to AI systems without modifying legacy application code.
Real-time AI applications (fraud detection, recommendations) that need instant access to legacy database changes.
Create read-only replicas of legacy databases in modern formats (PostgreSQL, MongoDB) for AI model training and inference.
Training ML models on historical data or batch AI processes where real-time isn't critical.
Wrap legacy system functionality in lightweight microservices that expose modern APIs for AI consumption.
Gradual modernization where specific legacy functions need AI augmentation without full system replacement.
Decouple legacy and AI systems using event streams, allowing asynchronous communication without tight coupling.
Complex enterprises with multiple legacy systems needing coordination and loosely-coupled AI augmentation.
Our integration specialists have connected AI to mainframes, AS/400s, and custom legacy systems across industries. Get a free architecture review.
Document legacy system architecture, data flows, integration points, and performance requirements. Identify AI use cases with highest ROI.
Select the pattern(s) that best fit your technical constraints, timeline, and use case requirements. Often hybrid approaches work best.
Implement the chosen pattern with focus on reliability, monitoring, and error handling. Start small with pilot use case.
Connect AI models to integration layer and validate end-to-end workflows with production data in controlled environment.
Establish operational procedures, monitor integration health, and gradually expand to additional use cases.
A European manufacturer running SAP R/3 on Oracle needed to add AI-powered demand forecasting without replacing their 20-year-old ERP system containing critical business logic.
We implemented a hybrid approach combining CDC and API Gateway patterns:
No. Properly designed integration patterns read data from legacy systems without modifying them. We use read-only database replicas, CDC from transaction logs, or API gateways that translate requests. The legacy system continues operating exactly as before.
We implement caching layers, predictive pre-computation, and asynchronous processing. For example, AI models can pre-compute predictions during off-peak hours, storing results for instant retrieval. Critical paths use optimized queries to legacy systems with sub-second response times.
Even file-based or screen-scraping integrations can work. We've successfully integrated AI with systems that only export batch files or use terminal emulation. While not ideal, these approaches unlock AI capabilities until proper APIs can be developed.
Pilot projects range from $50K-150K depending on complexity. This includes integration layer development, AI model deployment, and initial use case implementation. Compare this to $5M+ for legacy system replacement - integration is 97% cheaper while delivering immediate AI value.
Absolutely - we recommend it. Start with a single high-value use case (fraud detection, recommendation engine, predictive maintenance) using one integration pattern. Once proven, expand to additional use cases leveraging the same infrastructure.
Don't let legacy infrastructure block your AI transformation. Our integration specialists will design a solution that preserves your investments while enabling modern AI capabilities.