Service
Enterprise & Legacy Integration
Connect AI to SAP, MES, core banking and the databases your business actually runs on, through interfaces your teams can maintain.
The value is in systems that were never designed for AI.
Orders live in the ERP, production data in the MES, accounts in core banking, and history in databases older than most of the team. AI only becomes useful when it can read from these systems reliably, and sometimes write back, without putting them at risk.
We build the integration layer between AI and your core systems: stable interfaces, clear data contracts and safeguards, documented so your teams can maintain them.
You may need this if
- Every AI pilot starts with weeks of "getting the data out".
- Core systems can't take extra load or direct writes.
- Nobody owns the meaning of the fields an AI system depends on.
- AI results need to flow back into the system of record.
What we deliver
01
System and data mapping
Which systems hold which data, how each can be reached (APIs, replicas, files, change data capture) and who approves each path.
02
Anti-corruption layer
A translation layer that shields AI services from legacy formats, and core systems from AI traffic.
03
Reliable data flows
Change data capture or scheduled extracts, with idempotency, retries and reconciliation.
04
Data contracts
Versioned schemas and quality checks agreed with the owners of each system.
05
Safe write-back
Human approval and an audit trail before anything changes a system of record.
06
Handoff
Interface documentation, runbooks and monitoring for your integration team.
Reference architecture
A reference design, not a fixed product. Discovery adapts it to your systems and constraints.
AI applications
- Assistants and agents
- Forecasting and analytics
- Search index
Domain services
- Clean domain model
- Data contracts
- Approval workflow
Integration layer
- Anti-corruption layer
- Change data capture
- Scheduled extracts
Systems of record
- ERP
- MES
- Core banking
- Legacy databases
Safeguards
- Read replicas
- Rate limits
- Reconciliation
- Audit trail
How an engagement usually runs
- Discovery2 weeks
Systems and access paths mapped, risks agreed with system owners, and the first integration chosen.
- Pilot6–8 weeks
One production integration feeding an AI use case, with monitoring and reconciliation.
- PodMonthly
Further sources, write-back with approvals, and a reusable integration platform.
Constraints we design for
- Change control
- Releases go through your change process; nothing touches a core system without its owner's approval.
- Load on core systems
- Read replicas, off-peak extracts and rate limits protect production.
- Vendor-managed systems
- We work through supported interfaces and the vendor's rules, not around them.
- Data residency
- Data stays in approved locations, and every flow is documented for audits.
Typical stack
- SAP OData, RFC and IDoc
- Debezium
- Kafka
- PostgreSQL
- REST and gRPC
- Airflow
- dbt
- Python
- Java
We use the tools you already run where they fit. Names are for reference only.
Tell us about your systems
An engineer reads every message and replies within one business day.