System Integration for AI & Engineering
We build the technical components so AI works reliably with your enterprise systems, processes, and data. APIs, interfaces, data pipelines, backends, workers, dashboards, CLI and admin tools, logging, monitoring, audit trails — everything that turns AI from gimmick to production tool.
For whom, what problem, what outcome
Companies with grown system landscapes who do not want AI as an island but as part of real business processes. Where data flows between systems, where workflows span multiple tools, where audit trails are required. Result: AI becomes productively usable, islands disappear, media breaks decrease, data flows are documented, automation scales.
Typical use cases
- API integration between ERP, CRM, ticketing, knowledge base, AI models
- Interface development (REST, gRPC, GraphQL, webhook, event streams)
- Data pipelines (ETL/ELT) with Airflow, dbt, Camel, Kafka, Spark
- Backends for AI workflows with FastAPI, worker queues (Celery, RQ, Dramatiq)
- Dashboards and admin tools (Streamlit, Plotly Dash, custom React/Vue)
- CLI and admin tools for operations and maintenance
- Logging architecture with structured logs, correlation IDs, OpenTelemetry
- Monitoring with Prometheus, Grafana, Loki, OpenTelemetry, OpenSearch
- Audit-trail pipelines for AI decisions — KRITIS-, ISO-, BaFin-grade
How we work
- Map the system landscape — which systems, which interfaces, which data flows exist today? Documented or "magical"?
- Requirements analysis — which AI workflows are to be integrated? What availability, security, latency requirements?
- Architecture sketch — components, responsibilities, data flows, error handling. We favor simple, maintainable architecture over fashionable complexity.
- Iterative delivery — vertical slice, end-to-end function in 2–3 week sprints. No half-finished layers.
- Operations & maintenance — monitoring, alerting, runbook, documented interface contracts. Handover or continued maintenance possible.
Tech stack
Deliverables
- API interfaces with OpenAPI/AsyncAPI specs and versioning
- Data pipelines with idempotency, retry logic, consistency guarantees
- Backend code modular, tested, documented, with CI/CD
- Dashboards for operations, business, compliance
- Monitoring stack with alerting rules and escalation paths
- Audit-trail pipelines with audit-grade storage
- Operations runbook and training of your engineering teams
Customer benefit
- AI becomes productively usable, not just prototype-like
- Fewer islands — workflows use existing systems instead of bypassing them
- Fewer media breaks because interfaces are cleanly defined
- Better data flows with consistency guarantees and audit trail
- Higher automation scales without linear engineering growth
- Reliable technical implementation — maintainable, not magical
Compliance & standards
- GDPR & ePrivacy — data flows with classification and processor contracts
- ISO/IEC 27001 — ISMS-compliant architecture
- BSI C5 / BSI IT-Grundschutz where contract or sector requires
- KRITIS / NIS-2 with audit trail and availability SLAs
- BaFin / KWG / MaRisk-compliant interfaces in banking environments
FAQ
Greenfield or legacy integration?
Both — we are explicitly made for grown landscapes. Pure greenfield is the exception. We respect existing systems, data models, processes and build integration so nothing existing must be replaced before something new can run.
Which languages and frameworks?
Python (FastAPI, Pydantic) as default for AI backends and pipelines. TypeScript for front-end and edge. Go or Rust where performance or memory safety matter. The fitting tool, not the trendy one.
How do you test AI integrations?
Three levels: (1) unit tests for deterministic logic. (2) contract tests against interface specs. (3) eval suites for AI components with gold set and regression gate. Test coverage is measurable and required for production deploys.
What is your operating model?
Flexible: full handover, shared operations, or managed service by us. In every case there is a documented runbook, monitoring with alerts, and a clear escalation path.
How long does a typical project take?
Six weeks for a first end-to-end slice (e.g. ticket triage into your ticket system). Full platform integration with multiple AI workflows and compliance requirements typically 3–6 months.
Discuss your integration project
Which systems should work with AI? Where are the break points today? We respond with a first architecture sketch and effort estimate.
> Start AI Readiness Check