Modernize your legacy systems and existing applications with AI capabilities without the risk and cost of complete rebuilds. Add intelligence incrementally.
You've invested heavily in existing software systems that power your business. Rebuilding from scratch is too risky and expensive, but staying with manual processes puts you at a competitive disadvantage:
Older systems weren't designed for AI, making integration complex and creating technical debt.
Complete rewrites disrupt operations, risk data loss, and require extensive retraining of users.
Your team is focused on maintaining current systems and lacks AI expertise for integration.
Critical data is trapped across multiple systems, making it difficult to leverage for AI models.
We add AI capabilities to your existing systems through careful integration that minimizes disruption while maximizing value.
We analyze your existing architecture, data flows, and integration points to create a low-risk integration roadmap that preserves system stability.
We build robust data pipelines that extract, transform, and prepare data from your existing systems for AI model training and inference.
We create a dedicated AI service layer that sits alongside your existing systems, providing intelligence without modifying core applications.
We integrate AI capabilities through APIs, webhooks, and event-driven architectures, with comprehensive testing to ensure stability.
We deploy AI features gradually with careful monitoring, allowing you to validate benefits before full-scale adoption.
Explore how we've helped organizations modernize legacy systems with AI.
Different systems require different integration approaches. Here are proven patterns we use:
Build AI services as standalone APIs that your existing applications call. Best for systems with existing API infrastructure. Minimal changes to core systems, easy rollback if needed.
AI services listen to events from your existing systems and respond asynchronously. Ideal for batch processing, notifications, and background tasks. Decouples systems for flexibility.
AI models access shared databases or data warehouses directly. Best when existing systems can't easily expose APIs. Requires careful data governance and access control.
Create an integration layer that translates between legacy protocols and modern AI services. Essential for older systems with proprietary interfaces or limited connectivity options.
Add AI-powered features to existing user interfaces without backend changes. Works through browser extensions, embedded widgets, or wrapper applications. Quick wins for user-facing AI.
We have deep experience integrating AI with common enterprise platforms:
SAP, Oracle, Microsoft Dynamics - Add predictive analytics for inventory, demand forecasting, and process optimization.
Salesforce, HubSpot, Microsoft Dynamics - Enhance with lead scoring, churn prediction, and intelligent recommendations.
SharePoint, Documentum - Add intelligent search, automated classification, and content extraction capabilities.
Tableau, Power BI, Looker - Augment with natural language queries and automated insight generation.
Proprietary systems built on .NET, Java, PHP - Add AI through RESTful APIs and microservices architecture.
SCADA, MES, PLM - Integrate predictive maintenance, quality control, and production optimization AI.
Many legacy systems lack modern APIs. We create custom connectors, use database-level access, or implement screen scraping when necessary. We also help modernize systems gradually by adding API layers.
Legacy data often has inconsistencies, missing values, and format issues. We implement data cleaning pipelines, validation rules, and enrichment processes to prepare data for AI while preserving source systems.
Adding AI workloads can strain older systems. We use caching, asynchronous processing, and dedicated infrastructure to ensure AI capabilities don't impact existing system performance.
Integrating new systems raises security concerns. We implement zero-trust architectures, encryption at rest and in transit, and comprehensive audit logging to maintain security posture.
Users are accustomed to existing workflows. We design AI enhancements that feel natural, provide extensive training, and allow gradual adoption to minimize resistance.
Integration is often 3-5x more cost-effective than rebuilding from scratch. Here's why:
Existing systems contain years of refined business rules. Integration preserves this investment while adding new capabilities.
Integration projects typically deliver results in 3-6 months vs. 18-24 months for complete rebuilds.
Phased integration allows validation of each enhancement. If an AI feature doesn't deliver value, you can pivot without abandoning the entire project.
Your business continues running on proven systems while AI capabilities are added incrementally. No "big bang" migration disruptions.
Lower cost vs. complete system rebuild
Average time to first AI capability in production
System uptime maintained during integration
No. We design integration to run alongside existing systems with minimal disruption. Phased rollouts ensure business continuity, and we can roll back changes if issues arise.
Yes. We've successfully integrated AI with mainframe systems, AS/400, and proprietary platforms. We find or create the right integration points regardless of system age.
We implement encryption, secure APIs, role-based access control, and comply with industry standards (SOC 2, GDPR, HIPAA). All data transfers are logged and monitored.
Our modular architecture allows easy updates and additions. AI models can be retrained or replaced without touching core integrations. We design for flexibility.
Usually not. We typically create read replicas or data warehouses for AI workloads, leaving production databases untouched. This preserves stability and performance.
Get a free integration assessment to understand how AI can enhance your current software stack.
Related: Building Custom AI Applications | Scaling Custom AI Solutions