Ready to bring your AI projects from prototype to production-ready systems?

The duration depends heavily on the use case, the state of the data and the integration requirements. A simple prototype can be up in a few days; a fully integrated, productive system typically requires several weeks to a few months. Our experience shows: clear prioritization and a minimal but real scope significantly accelerate the path to production.

In practice we distinguish three phases: Discovery (scope & feasibility), Rapid Prototyping (validation) and Production-Readiness (architecture, security, CI/CD). Each phase has its own deliverables and gate decisions, so stakeholders can transparently track progress and control risks.

Factors that can extend the timeline are unstructured data, complex legacy integrations or strict compliance requirements. We reduce delays by performing early technical feasibility tests, data pipeline work and interface tests.

Concrete timeframe: Discovery 1–3 weeks; Prototype 1–4 weeks; Production preparation 4–12 weeks. For enterprise integrations and self-hosted infrastructure, allow additional time for approvals and infrastructure provisioning.

Cost categories include engineering effort, infrastructure (hosting, GPU/inference), license or API costs, data preparation and ongoing operation and maintenance. Depending on the architecture, hosting costs can vary widely — self-hosted solutions have different CAPEX/OPEX profiles than cloud-based models.

We calculate ROI along direct savings (e.g., process automation, reduced personnel costs), indirect effects (better decision quality, higher conversion) and risk reduction (better compliance, lower error costs). It’s important that metrics are defined and measured upfront so the business impact can be demonstrated.

A realistic financial plan considers initial development investments, ongoing support and predictable costs for model updates and infrastructure. When selecting models we compare cost per request, latency and result quality to choose the most economical option.

We support the business case: we provide cost estimates, scenario analyses and sensitivity calculations so decision-makers can make an informed investment decision.

Security and data protection are an integral part of our engineering process, not after-the-fact add-ons. We start with a privacy and risk analysis: which data flows exist, which personal data is processed, and which regulatory requirements apply?

Technically we rely on encryption in transit and at rest, role-based access controls, audit logs and strict key management processes. For particularly sensitive use cases we recommend self-hosted infrastructures (e.g., Hetzner + MinIO) to ensure data sovereignty and full control over logs and models.

Operationally we ensure compliance through documentation, data governance processes and clear responsibilities. For models we define policies on usage, feedback loops and allowed data sources. We also implement monitoring for data drift and anomaly-based alerts to detect implicit risks early.

If needed, we work closely with your data protection officer and external auditors to secure certifications or auditable processes. For us, security is a continuous process that grows with the operation of the system.

The required data depends on the use case: for conversational agents, dialog histories, FAQs and structured HR or CRM data are relevant; for forecasting we need historical time series; for document intelligence annotated documents and metadata are required. What matters is not only quantity, but quality and representativeness of the data.

Our data preparation pipeline includes data collection, cleaning, labeling, feature engineering and the setup of feature stores or vector databases (e.g., Postgres + pgvector). We implement ETL jobs and validation suites to automatically monitor data quality.

For knowledge-based applications we choose between RAG approaches and no-RAG architectures based on security and performance requirements. For RAG we build efficient retrieval layers with vector search and relevance metrics.

If data is missing, we help build data generation processes, annotated datasets or synthetic data to bootstrap models and reach production quality.

Integration starts with a detailed mapping of available interfaces: REST APIs, event buses, message queues, databases or batch exports. Our architecture defines adapter layers that ensure robustness and idempotence so AI components don’t cause unwanted side effects in core systems.

We prefer small, well-defined integration steps with feature flags and canary releases to minimize risk. This lets us test behavior under real operating conditions and roll back quickly if needed. Additionally, we implement end-to-end tests that automatically validate integration paths.

For ERP extensions and internal tools we build modular APIs and webhooks that behave like normal microservices. This keeps ownership and observability with the existing SRE/platform team while making new AI functions safely consumable.

After go-live we support stabilization, performance tuning and knowledge transfer so your internal teams can take long-term control over API contracts, deployments and maintenance.

Long-term operation requires clear ownership, automation and a release strategy for models and data. We define roles and responsibilities, create runbooks and provide automated tests for data integrity and model performance. This enables your teams to act independently after handover.

Technically we emphasize CI/CD, infrastructure-as-code and automatic model deployment with versioning. MLOps practices like data-drift monitoring, retraining pipelines and canary rollouts reduce the risk of unexpected quality degradation.

Organizationally we support training and coaching for your developers, data scientists and operations teams. We provide documentation, on-call plans and offer initial co-management options so knowledge isn’t lost and operational stability is ensured.

Finally, regular review cycles and KPIs (e.g., accuracy, cost per request, adoption) are crucial. We implement dashboards and regular health checks to ensure continuous improvement and business alignment.