Fordel StudiosWrap legacy systems with AI layers — without the full rewrite.
The systems with the most valuable business logic are usually the hardest to replace. The strangler fig pattern — extract AI-amenable workflows into new services, integrate via an API facade, let the legacy system keep running what it runs well — is how you add AI value incrementally without betting the business on a multi-year rewrite.
Organizations with significant business value in legacy systems face a specific AI adoption problem: the systems that most need AI capabilities are the ones least suited to receive them. A 20-year-old insurance claims system, a manufacturing MES from 2010, or an ERP with business logic embedded in stored procedures cannot be replaced on a timeline that captures near-term AI value. The full-rewrite path is slow and risky. The augmentation path is underexplored.
Document AI has matured to the point where paper-based legacy processes can be augmented without touching the underlying system. AWS Textract, Azure Document Intelligence, and Google Document AI handle extraction from forms, invoices, and mixed-format documents with high accuracy. Custom extraction models handle domain-specific document types that general models struggle with. The output can integrate with legacy systems via file-based batch, database write, or API — whichever interface the legacy system exposes.
- Data is in legacy formats — transformation is required before AI can process it
- Legacy system behavior cannot change — AI must work within existing constraints
- Integration options may be limited to file-based batch or database access if no API exists
- Business logic in the legacy system must be preserved — augmentation adds capability, not replacement
- Rollback must be possible — every augmentation path must be reversible without data loss
We start with an integration architecture assessment: what interfaces does the legacy system expose (APIs, file exports, database, UI), what data is available and in what format, and which workflows are highest-value candidates for AI augmentation. The assessment produces an integration feasibility report before any implementation starts — we do not commit to an architecture without understanding the actual integration surface.
For document-heavy legacy workflows, document AI is often the highest-leverage starting point. Paper forms, PDF invoices, handwritten records — AWS Textract and Azure Document Intelligence handle these with high extraction accuracy. Custom extraction models handle domain-specific document types. The extracted structured data integrates with the legacy system via whichever interface it exposes.
Legacy AI augmentation approach
Inventory legacy system interfaces: APIs, file exports, database access, UI. Identify highest-value AI augmentation candidates. Assess integration feasibility and produce options with reliability and maintenance trade-offs documented.
Design an API abstraction layer wrapping legacy system access. This becomes the integration point for AI capabilities and the routing layer for gradual traffic migration. Built incrementally — start with the highest-value workflow.
AWS Textract, Azure Document Intelligence, or custom extraction models for document-heavy workflows. Extraction pipelines with confidence scoring and human review routing for low-confidence extractions.
Transform legacy data formats — COBOL records, fixed-width files, archaic XML, stored procedure outputs — into clean inputs for AI systems. Transformation logic is tested and documented.
Route increasing traffic through the AI-augmented path. Maintain the legacy path as fallback. Every augmentation is reversible without data loss. Monitor quality and rollback capability throughout migration.
Strangler fig architecture
We apply the strangler fig pattern to extract AI-amenable workflows from legacy systems incrementally. The legacy system continues operating while new AI-powered capabilities are built alongside it — reducing migration risk and enabling incremental value delivery.
Document AI for legacy paper processes
AWS Textract, Azure Document Intelligence, and custom extraction models handle paper forms, PDF documents, and mixed-format records that legacy systems were designed to process manually. Structured data extracted at scale, integrated back to the legacy system via whatever interface it exposes.
API facade and abstraction
An API abstraction layer in front of the legacy system enables routing, transformation, and AI augmentation without modifying the legacy codebase. The facade is built incrementally around the highest-value workflows.
Legacy data transformation
COBOL records, fixed-width files, archaic XML schemas, database tables with undocumented semantics — transformed into clean, typed inputs for AI systems. Transformation logic is tested against production samples before it handles live traffic.
Reversible migration design
Every augmentation path is designed with rollback: the legacy path is preserved and can be restored without data loss. This enables safe incremental migration at low risk — no "big bang" cutover required.
- Integration architecture assessment with feasibility analysis and options comparison
- API facade or integration layer wrapping legacy system access
- Document AI extraction pipeline with confidence scoring and human review routing
- Data transformation pipeline from legacy format to AI-ready input
- Migration plan with rollback procedures and quality monitoring
- Integration documentation for ongoing maintenance
Legacy AI augmentation captures AI value on systems that cannot be replaced without multi-year rewrites. The strangler fig approach delivers incremental AI-powered improvement to existing workflows on shorter timelines than full platform migration requires.
Common questions about this service.
How do you integrate with systems that have no API?
In order of preference: database integration (direct read access, read-only), file-based batch integration (if the system exports or imports files), RPA-based integration (UI automation for web-based or desktop systems), screen scraping as a last resort. We document the reliability and maintenance characteristics of each approach and recommend based on your specific constraints.
What is Document AI and when does it apply?
Document AI refers to ML-powered extraction from documents — forms, invoices, contracts, handwritten records. AWS Textract, Azure Document Intelligence, and Google Document AI handle common document types with high extraction accuracy. Custom models handle domain-specific formats that general models struggle with. Document AI applies when: your legacy workflow processes large volumes of paper or PDF documents manually, and that processing is a bottleneck or error-prone.
How do you handle undocumented legacy system behavior?
Reverse engineering legacy behavior is part of the integration architecture assessment. We analyze existing data, observe system behavior, and interview users with operational knowledge. Discovered behavior is documented before integration code is written — building on undocumented assumptions produces brittle integrations that break on edge cases.
How long does legacy AI augmentation take?
Highly variable depending on integration complexity. Simple file-based integrations with well-understood data formats can deliver first AI capability in 4-6 weeks. Complex integrations requiring RPA or significant data transformation take longer. We scope the integration architecture assessment first — it produces a realistic timeline estimate before the full engagement begins.
Ready to get started?
Tell us what you are building. We will scope it, price it honestly, and give you a clear plan.
Start a ConversationFree 30-minute scoping call. No obligation.