Custom AI Development — production-ready in 100 days

MVP stands for Minimum Viable Product: In the context of custom AI development, this means: the smallest self-contained product iteration of your AI project that can be used in day-to-day operations, your version 1.0.

It works with real data, supports real processes, and delivers results that business teams can directly use and evaluate. This includes data pipelines, AI models, business logic, user interfaces, integrations, and the infrastructure for production operations.

A 100-Day MVP is ideal for companies that want to bring a custom AI system into production within their own value chain and need a fixed framework for time, budget, and outcome.

This applies to established mid-market companies and enterprises with mature processes and system landscapes, as well as startups that need to move quickly from an idea to a robust version 1.0.

A custom AI project is especially well-suited when off-the-shelf software, SaaS, or pure prompting are insufficient because the use case is too specific, too integrated, or too business-critical.

Common challenges our clients face include slow processes, high variance in result quality, limited scalability, and a growing shortage of skilled workers. Manual workflows reach their limits while demands for efficiency, quality, and profitability keep rising.

A 100-Day MVP ends with go-live. The result is a production-ready version 1.0 of your AI system that works with real data, is integrated into real processes, and is used in day-to-day operations.

Depending on the use case, the system covers the complete chain: data connections and pipelines, AI models with business logic, user interfaces, integrations into your systems, and the scalable infrastructure to keep everything running smoothly in production.

What business problem it ultimately solves depends on your creativity. The project starts with a kick-off workshop where we analyse the challenge in detail and discuss different solution approaches: how AI solves the problem or fundamentally redefines the process.

Prioritisation for version 1.0 is consistently driven by the desired project outcome. The central question is which features are necessary for the AI system to reliably fulfil its purpose and function in day-to-day operations.

Technically complex topics are deliberately addressed early to quickly resolve uncertainties. In parallel, we consider technical and organisational feasibility, including effort, costs, available data, time and technical dependencies, and implementation complexity.

On this basis, a clearly prioritised scope for version 1.0 emerges. If time or budget remain after implementing the core features, additional meaningful enhancements can be added.

A 100-Day MVP is an agile AI development format. The focus is on delivering a version 1.0 within the defined timeframe where the AI reliably solves the core problem and is used in real processes.

During development and testing, new ideas and expansion stages frequently emerge. Once the AI works with real data and creates impact, additional potential becomes visible, technically, professionally, and organisationally.

The project stays transparent throughout. Progress, scope, and priorities are visible early. Further ideas are documented, prioritised, and form the basis for targeted further development.

To date, every 100-Day MVP has delivered a measurable, convincing result. That is not coincidence but commitment. We go the extra mile to ensure project success and results.

This is fully flexible. Operations, support, and further development can be organised entirely in-house, externally, or as a hybrid model.

On request, we take over these tasks permanently as part of our AI Tech Team. This includes technical operations, support and maintenance, security and infrastructure updates, monitoring, AI retraining, cloud operations, and continuous development along the roadmap.

If you want to build operations capabilities in-house, we provide targeted support for knowledge transfer, technically, organisationally, and methodologically. The goal is for you to confidently operate and evolve your AI system independently.

Many companies choose a hybrid approach: We initially handle operations and development, then gradually hand over responsibility to internal teams as structures, knowledge, and resources are built.

For us to start the 100-Day MVP cleanly and with momentum, we need clarity, commitment, and contact persons.

It begins with a jointly defined use case, not detailed to the last specification, but with a clear goal: What problem should AI solve or which process should it noticeably improve? This scope is refined in a joint kick-off workshop where we analyse the challenge and outline the solution approach.

On the client side, we need contact persons who can provide domain context, assess processes and data, and accompany the project internally. This includes coordination on data protection, security, and compliance topics, as well as the ability to make project decisions and set priorities together with us. Regular reviews and close communication ensure everyone stays informed and the project progresses in a structured manner.

From a technical perspective, we need access to relevant data sources and an overview of existing IT systems and interfaces.

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, such as connecting custom systems, special interfaces, or when operations run on client-specific infrastructure. In these cases, we align requirements jointly and early.

Collaboration starts with a joint kick-off workshop where the goal, scope, and approach are defined. We then work in three phases: First, the MVP is prepared from a domain, technical, and data perspective; then it is implemented step by step; and finally it is transitioned into production.

Throughout the entire project, there are regular check-in meetings where progress, interim results, and next steps are aligned. Results are delivered early, reviewed, and iteratively refined.

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

Because the 100-Day MVP requires more than good developers. It requires a team that takes responsibility for AI development from idea to production and delivers in 100 days. Here is what sets us apart:

• Fixed price and go-live in 100 days: The 100-Day MVP is not an open-ended project but a binding format with a clear outcome: your AI system in version 1.0, live in production.
• End-to-end ownership: From use case refinement through architecture, custom AI models, software engineering, and cloud infrastructure to go-live. No handoffs, no gaps.
• Real AI development: 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.
• Hundreds of AI projects delivered: Custom AI development since 2017. Experience from hundreds of 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.

Yes, the solution developed during the project belongs to you. You receive the complete source code and unrestricted usage rights. This means: You can use the system permanently, share it internally, and build on it.

You are also free to further develop the solution independently, with your own teams or other partners. The code is not a black box but your foundation.

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.