Beyond the technologies themselves, the challenge is to build an abstraction layer that makes your heritage exploitable by the modern AI ecosystem. This involves three pillars: (1) a secure API Gateway that isolates the legacy from modern consumers; (2) a Change Data Capture pipeline (Debezium, IBM InfoSphere CDC, Striim, Qlik Replicate) that exports changes from DB2, VSAM, IMS, or Adabas to a data lake usable by your models; (3) a RAG (Retrieval-Augmented Generation) layer or vector store (Pinecone, Weaviate, pgvector, Qdrant) that allows LLMs to query your business documentation, operational procedures, and transactional archives.
This progressive architecture, popularized by Martin Fowler's strangler pattern, allows you to replace one legacy module at a time, starting with the peripherals (front-office, reporting, partner integrations) before tackling the transactional core. At each step, you capture measurable business value: AI conversational agents plugged into real-time data, dynamic credit scoring, fraud detection via anomaly detection, contextualized recommendations, industrial digital twins, and predictive maintenance.
The sectors where this work unlocks the most value are historically banking (core banking, scoring, KYC/AML), insurance (underwriting, claims, actuarial), the public sector (tax, social, registries, citizen portals), telecoms (BSS/OSS, churn, customer service agents), and industry/energy (SCADA, predictive maintenance, energy optimization). In the French-speaking world, our teams operate in France, the Maghreb (Morocco, Algeria, Tunisia), and West Africa (Senegal, Ivory Coast, BCEAO and Bank Al-Maghrib compliance).