Migration Graveyard

Legacy system integration is the process of connecting modern AI capabilities and services to existing business software without replacing the underlying system. The legacy application stays in production. The data stays where it is. New functionality — such as intelligent document processing, predictive analytics, natural language search, or automated workflows — layers on top through APIs, database connections, middleware, or webhook triggers. The goal is to extend what the system can do, not to rebuild it from scratch.

Yes. We use five different integration methods depending on what the system exposes. If there is no API, we can connect at the database level — reading directly from MySQL, PostgreSQL, SQL Server, Oracle, or AS/400 DB2. If database access is also restricted, we build a middleware layer that sits between the legacy system and modern AI services. For completely closed systems with no API and no database access — such as mainframes or terminal-based applications — we use an RPA + AI hybrid approach where robotic process automation mimics user actions while AI handles the decision logic.

Most single-system integration projects take 6 to 10 weeks from initial assessment to production deployment. This includes 1 to 2 weeks for system assessment and scope definition, 2 to 3 weeks for architecture and build, 1 to 2 weeks for testing and validation, and 2 weeks for deployment and stabilization. Multi-system integrations that connect 2 to 4 legacy platforms typically take 10 to 16 weeks. Enterprise-scale projects with middleware layers and multiple AI services can extend to 16 to 24 weeks depending on complexity and compliance requirements.

Costs depend on the number of systems, the integration method required, and the AI capabilities being added. A single-system integration — connecting one legacy platform to one AI service — typically costs agencies $8,000 to $15,000 to deliver, with a recommended client price of $18,000 to $35,000. Multi-system integrations connecting 2 to 4 platforms cost $15,000 to $30,000 to deliver, with client pricing of $35,000 to $65,000. Enterprise integration suites with full middleware layers and multiple AI services range from $30,000 to $50,000 in delivery cost, with client pricing of $65,000 to $120,000 or more. Ongoing maintenance runs $300 to $800 per month.

No. That is the entire point of the integration approach versus full modernization. The legacy system remains in production throughout the project. Users continue working exactly as they do today. AI capabilities are added alongside existing workflows — not in place of them. We test with production data samples during the validation phase and deploy in a way that allows instant rollback if any issue arises. There is no cutover event, no downtime window, and no retraining period.

In most cases, no. API-based integrations, database connections, and webhook triggers do not require access to the legacy system’s source code. We connect to the system through its external interfaces — the same way any authorized application would. The only scenario where source code access becomes relevant is when we need to add webhook event triggers to a custom application that does not currently fire them, or when building a tightly coupled middleware layer for a completely proprietary system. Even then, we only touch the integration points — not the core application logic.

On-premises systems are fully supported. We deploy a lightweight connector agent on the client’s infrastructure that establishes a secure outbound connection to the AI services. All data processing can happen within the client’s network if compliance requires it. For organizations with strict data residency requirements — common in healthcare, finance, and government — we can architect the integration so that sensitive data never leaves the on-premises environment. The AI services process locally and only transmit results, not raw data.

Workflow automation moves data between systems based on predefined rules. If X happens, do Y. Legacy system integration goes further by adding intelligence to the process. AI reads unstructured documents, makes classification decisions, generates summaries, detects anomalies, and triggers actions based on pattern recognition — not just simple conditional logic. Workflow automation handles the plumbing. Legacy integration with AI handles the thinking. In practice, most projects combine both: workflow automation handles the data routing, and AI handles the decisions that used to require a human.

Yes. We have built integrations for healthcare organizations under HIPAA and for financial services companies requiring SOC 2 compliance. Our integration architecture supports encrypted data transmission, role-based access controls, audit logging, and data residency requirements. For HIPAA-regulated projects, we ensure that Protected Health Information is handled in accordance with the Security Rule and that all AI processing meets BAA requirements. For SOC 2 environments, we document all integration points and data flows to support the client’s compliance audits.

After deployment, we monitor the first 500 transactions to ensure everything runs as expected. Performance tuning happens during the first two weeks of production use. We then hand off full documentation — architecture diagrams, API specifications, monitoring dashboards, and runbooks — to your team or your client’s IT department. Ongoing maintenance is available at $300 to $800 per month and includes monitoring, error resolution, AI model updates, and capacity adjustments as usage grows. Most agencies resell this maintenance at $600 to $1,500 per month as a recurring revenue stream.