Can you integrate AI into legacy systems?
Yes. A large part of this service is about layering reliable AI functionality into systems that were never designed for it originally.
Overview
Bring AI into the systems your team already uses. We design the APIs, data flows and access controls around your CRM, databases and applications. Typical uses: E-commerce Personalization, Healthcare Systems and Financial Services.
6 capabilities
Ways to support your workflow
3 use cases
Examples to explore with your team
5 integration options
Including Salesforce and SAP
Commercial outcomes
Capabilities
Custom RESTful and GraphQL APIs built for your specific needs. Secure, scalable, and documented endpoints that connect AI capabilities to any application.
Modernize without disruption. We connect AI to your existing mainframes, databases, and enterprise systems without requiring full replacements.
Native integrations with AWS, Azure, and Google Cloud. Deploy AI across hybrid and multi-cloud environments with consistent performance.
Real-time and batch data pipelines that feed AI models with clean, structured data. ETL processes optimized for machine learning workflows.
Pre-built connectors for Salesforce, SAP, Oracle, Microsoft Dynamics, and more. Bi-directional sync keeps your AI and business systems aligned.
Encrypted connections, OAuth 2.0, and integrations designed around POPIA, GDPR and sector rules such as HIPAA where they apply to your data.
Applications
Connect AI recommendation engines to your storefront. Real-time product suggestions, dynamic pricing, and personalized search results.
Integrate AI diagnostics with EHR systems. Secure data exchange between clinical applications while maintaining HIPAA compliance.
Connect fraud detection AI to transaction systems. Real-time risk scoring integrated with banking platforms and payment gateways.
Integrations
Yes. A large part of this service is about layering reliable AI functionality into systems that were never designed for it originally.
We scope access carefully, use least-privilege patterns, isolate credentials, and define clear audit and handoff points before anything touches production workflows.
The hardest part is often not the model. It is aligning data quality, permissions, business rules, and downstream systems so the AI output is usable in practice.