Your AI experts for development, operations and forward thinking.

A retainer is a fixed, monthly engagement of our AI Tech Team. It secures continuous delivery capacity for your AI implementation, with a predictable budget and a reliable team that continuously prioritizes, implements and operates your topics.

This enables you to drive multiple initiatives in parallel: implementing new AI use cases, operating and evolving existing systems, maintaining quality through monitoring and retraining, and covering security updates and support cleanly, without having to set up new projects or reorganize resources each time.

An AI Tech Team suits companies that have already recognized AI as strategically relevant but realize that the bottleneck is not just the first AI implementation, but also AI operations: monitoring, continuous improvement and technical responsibility.

It is particularly suitable when manual solutions and one-off automations are no longer enough, because competitive pressure, efficiency demands, talent shortages and process complexity are tangible, and AI is seen as a lever to scale processes and manage them in a data-driven way.

Typical clients are established mid-sized companies and corporations with mature IT landscapes, productive or near-production AI applications, and the expectation that architecture, data sovereignty, security, compliance and operations are handled properly.

Project work is ideal when scope and goals can be clearly defined. A retainer is the better choice once AI needs to deliver lasting impact, because the real effort begins after the first go-live: operations, monitoring, security updates, retraining and continuous improvements.

In classic projects, exactly these tasks often fall through the cracks or become the next add-on project. The result is stop-and-go, knowledge loss between phases, and systems that run but never mature properly. With a retainer, the topic stays in one hand throughout, priorities can be adjusted continuously, and AI systems are not just built but kept stable and continuously improved.

This works particularly well because AI initiatives always involve research and development, and not everything can be planned upfront. Instead of stacking change requests, work continues directly as soon as data, usage or requirements deliver new insights.

Our retainers in the AI Tech Team have a minimum term of 6 months. After that, the collaboration continues and can be terminated with 3 months notice.

Yes, multiple AI projects in parallel are possible. How much parallelism makes sense depends on the package and available capacity.

How much can run in parallel depends on the booked capacity: In Stable Operations (S), the focus is on maintenance, operations and support — parallelism is more selective here. More Power (M) is designed to handle AI development and AI operations in parallel. Next Level (L) is explicitly built for high speed across multiple parallel AI implementations.

Communication and status transparency are an integral part of the AI Tech Team. You have a dedicated contact person at PLAN D who maintains the overview and coordinates the team. In regular check-in meetings, we align priorities, progress and next steps, and review what has been delivered and what is coming next.

Day to day, AI operations, support, monitoring and ad-hoc error analyses run continuously. In parallel, we work on developments along a jointly maintained backlog, so it is always clear what we are working on, what is finished and what comes next.

For critical incidents, we respond immediately after alerting. Your AI systems are monitored, and we are automatically notified of outages or anomalies, starting analysis and resolution right away.

You can reach us via ticket, email or phone. We quickly provide an initial assessment, prioritize the fix and keep you updated with status reports.

Additionally, we monitor security-relevant settings and best practices and track current vulnerability reports (CVE), so that updates and mitigations for your AI infrastructure are not left unattended.

We work with your developers as one joint AI development team. Not throw a ticket over the fence and hope, but closely aligned, with clear responsibilities and good communication.

Typically it works like this: We align priorities and interfaces in regular check-ins. APIs, data models and responsibilities are well documented so that integration remains plannable and no dependencies arise. Depending on the setup, we take ownership of entire topic packages or work pairing style on critical areas.

If you want to take over parts internally in the long run, that is possible. We then shape the collaboration so that knowledge flows naturally within the project and responsibility transitions step by step.

In the AI Tech Team, the rule is clear: everything we build for you and everything created with your data belongs to you.

This includes your training data and the resulting AI models, as well as data generated during the ongoing operation of your AI systems. The individually developed software solution with source code, business logic and interfaces is also your intellectual property. You can build on it permanently and, if needed, continue the solution internally.

Our KPIs are not for marketing but for realistically assessing result quality. They transparently show how well the AI system actually performs its task, traceable, explainable, and comparable over time.

Which KPIs are meaningful depends on the specific use case. That is why we recommend the appropriate metrics, explain the rationale behind their selection, and clarify what they mean in business terms. Examples include:

• Prediction accuracy: How close the AI gets to the expected or known result. Examples: R², MAE (Mean Absolute Error), RMSE, MAPE, Accuracy
• Error types and error distribution: What types of errors occur and which of them are critical or tolerable in the business process. Examples: Confusion Matrix, False Positives / False Negatives, Precision, Recall, F1-Score
• Model drift: Detection of changes in data or patterns that cause the model to gradually lose predictive power. Examples: Data Drift, Prediction Drift, Population Stability Index (PSI), Feature Shift
• Response quality for agents: Assessment of factual correctness, source grounding, and goal achievement of responses. Examples: Mean Reciprocal Rank (MRR), Retrieval Precision / Recall (R-P / R-R), Context Coverage (CC), Groundedness Score (GS), Hallucination Rate (HR), Task Success Rate (TSR).
• Cost savings: What impact the AI results have on costs, turnaround times, risks, or revenue. Examples: Time saved per process, reduction of manual reviews, avoided error costs, automation rate
• Cost-performance ratio: The relationship between computational effort, runtime, infrastructure costs, and achieved model performance. Examples: Inference cost per prediction, latency, performance per euro of cloud costs

We ensure that your AI system remains operational even when individual team members are unavailable. You do not work with a single resource, but with a team of AI experts where know-how is distributed across all relevant disciplines.

Availability is planned proactively, backup coverage is arranged, and absences are cushioned. When team members change, replacements are staffed promptly and handovers are conducted properly. New colleagues are systematically onboarded into your business model, data landscape, AI systems and security requirements. Knowledge is continuously shared and documented so that no critical dependencies arise.

Typically, the resource commitment on the client side is manageable. Beyond the designated contact persons and access to relevant data, no extensive internal capacities are required.

Internal IT resources are only needed when special conditions apply, for example when connecting individual systems, with special interfaces, or when AI operations need to run on customer-specific infrastructure. In these cases, we align the requirements jointly and early on.

Because we deliver AI end-to-end – from AI pilot project to production. As an AI service provider, we take responsibility for the outcome, not just for individual project phases. What sets us apart as an AI service provider:

• End-to-end delivery: Strategy, AI use case refinement, architecture, custom AI models, software engineering, cloud infrastructure, deployment, operations, and continuous improvement. No handoffs, no gaps.
• Custom AI model training: We build tailored AI models with real data science – no prompting wrappers, no drag-and-drop AI, no generic off-the-shelf solutions.
• Technical depth with business focus: Project decisions are driven by value, risk, and ROI – not by trends or tool recommendations.
• Ownership through go-live and beyond: The 100-day MVP ends with production deployment. On request, we continue with operations, support, and scaling.
• Experience from real AI projects: AI development since 2017. Hundreds of completed AI implementations for enterprises with high demands on integration, security, and scalability.‍
• Compliance & security: We develop, operate, and maintain AI systems in Germany under ISO 27001. Encryption, anonymisation, clean architectures, and traceable documentation are part of our DNA.