AIinproduction.Notapilot.
Forecasts improve by up to 26%, and document workflows are up to 98% automated. We deliver AI systems your teams can understand, monitor, and use reliably in daily operations.
AI reality · Status quo
AI rarely fails because of technology.
Three metrics show what separates pilots from production AI.
< 0 %
of AI projects reach production
Fewer than a third of all started AI projects in companies ever reach productive operation. The most common reason is not poor technology: but missing data foundation, unclearly defined processes, and an unplanned transition from pilot to production.
0 %
of AI time spent on data preparation
Data scientists and AI developers spend up to 80 % of project time on data preparation, cleaning, and validation: not model development. Without a clean data platform as a foundation, AI development is inefficient and error-prone.
0.0×
higher ROI with a clearly defined use case
Companies that start AI projects with a clearly defined business problem, a measurable success criterion, and planned production-readiness achieve a 3.5× higher return on investment than companies that start technology-driven.
The reality: and what is possible
Typical unstructured AI project → With a structured AI approach
Without structure → pilot stays pilot. With structure → AI in production.
Typical unstructured AI project
Ambitious pilot with impressive demo results: then nothing that has gone into sustained operation. The budget was spent, the benefit is not measurable.
AI model runs in a Jupyter notebook environment. Nobody in the company understands it, nobody can maintain it: the external provider remains permanently necessary.
Data quality was recognised as a problem during the project, not before. The model learns the wrong patterns or fails in production because real data looks different from training data.
With a structured AI approach
Clear identification of the two to three processes with the greatest AI leverage: before budget flows into development. Only projects with a realistic production path are started.
Productively deployed system with monitoring, error handling, and complete documentation. Your team understands the model, can monitor it, and retrain it when needed.
Data foundation assessment at the start of every project: not as an afterthought. If the data foundation is insufficient, it is addressed before development budget flows.
What AI systems in productive operation actually deliver
Not technology enthusiasm: but measurable improvements in real processes that are used every day.
What changes operationally
Projects that plan for production-readiness, data foundation, and handover from the start reach production: the others stay as pilots.
ML models on a clean data foundation deliver measurably better forecasts: replacing gut feel with data-driven steering.
NLP models take over repetitive classification tasks: fully documented and internally controllable.
Results vary by starting point. All figures from completed, anonymised projects.
AI projects that reach production
+3×Projects that plan for production-readiness, data foundation, and handover from the start reach production: the others stay as pilots.
Forecast accuracy (demand planning)
+26 %ML models on a clean data foundation deliver measurably better forecasts: replacing gut feel with data-driven steering.
Manually classified documents per month
−98 %NLP models take over repetitive classification tasks: fully documented and internally controllable.
Our approach: ADET methodology
From use case assessment to productively running AI system
Evaluating processes & data foundation
Which process has the greatest AI leverage: and is the data foundation sufficient? If not, we say so before any development budget is committed.
Our approach: ADET methodology
From use case assessment to productively running AI system
Evaluating processes & data foundation
Which process has the greatest AI leverage: and is the data foundation sufficient? If not, we say so before any development budget is committed.
Architecture & method for the use case
We select the right method for the use case: ML, deep learning, NLP, or LLM: not what is currently trending. Monitoring, error handling, and the production path are planned from day one.
Model, integration, production-readiness
Development in increments that are production-near from the start: documented code, robust error handling, monitoring, and integration into the existing system landscape.
Knowledge, control, ability to evolve
Complete documentation, training of relevant people, structured handover. Your team operates and monitors the system independently: no permanent external dependency.
„The pattern I see most often in AI projects is always the same: an impressive pilot, standing ovations after the demo: and six months later nobody is involved anymore and the model is not running in production. The reason is almost never the technology. It is the fact that the transition from pilot to operation was never planned. Monitoring, retraining, internal ownership, documentation: these do not emerge by themselves. They have to be planned from the start."
Dominik Wörz
Founder, alpLytics · Vienna
What you concretely receive
Concrete deliverables
Typical project results
> – %
of projects reach production
up to – %
reduction in manual classification tasks
+– %
higher forecast accuracy (avg.)
Tools & Technologies
Proven technologies
Hover for details · We work tool-agnostically.
100 % Open Source
Keine Lizenzkosten, kein Vendor Lock-in
DSGVO-konform
Alle Tools laufen auf EU-Servern
Toolagnostisch
Das beste Tool für Ihr Problem — nicht mit Provision getrieben
bewährte Tools
in 1 Kategorien
Technologie-Rail
PostgreSQL
StorageRobust open-source database as the foundation for feature stores and ML data infrastructure
Typischer Einsatz
Robust open-source database as the foundation for feature stores and ML data infrastructure
Warum gewählt
Ausgewählt für Stabilität, Wartbarkeit und Team-Fit.
Stack-Fit
Zentrale Datenbasis und Performance
Why alpLytics
What sets us apart from typical AI projects
No pilot without production path. No model without documentation. No handover without internal understanding.
Process before model
Always, without exception
No AI project starts at alpLytics without a structured assessment of the process and data foundation. If the prerequisites are missing, we say so: before development budget is committed.
Production-readiness as standard
No pilot without a production path
Monitoring, error handling, documentation, and integration are part of the original project: not an afterthought. The transition from pilot to production is planned, not improvised.
Data sovereignty & open architectures
No vendor lock-in in your AI infrastructure
We recommend architectures that belong to you: locally operable, fully documented, without growing API fees. No interest in cloud lock-in.
Who is this for?
Typical profiles
We prioritise the roles where AI creates the greatest measurable leverage in real processes.
CEO
Typical profile
Mid-size company, 50–500 employees
AI fit is clarified in a 30-minute initial consultation.
CTO
Typical profile
Technology-driven company
AI fit is clarified in a 30-minute initial consultation.
Head of Operations
Typical profile
Mid-size company with repetitive processes
AI fit is clarified in a 30-minute initial consultation.
Mid-size company, 50–500 employees
AI is strategically important: but previous projects never got beyond the pilot stage. Unclear ROI, unclear which process is the right entry point.
Technology-driven company
AI is being developed internally, but models are not running stably in production. Monitoring, retraining, and documentation are missing. Every model is a black-box system.
Mid-size company with repetitive processes
Repetitive tasks: document classification, demand planning, anomaly detection: are done manually today. Clear that AI could help. Unclear how to get started.
Frequently asked questions
What clients usually ask
Next step
In 30 minutes you will know where AI makes the biggest difference in your company.
Free initial consultation: we analyse your starting point and show which process is the most sensible AI entry point. No technology pitch.
No automated follow-up · No newsletter · Just an honest conversation