AI for Business
We combine custom AI integration in software and existing systems with AI Craft, our enterprise offer for strategy, governance, enablement, integration, measurement and operations.
Clarify AI potentialNot every AI initiative needs the same starting point. Some companies want concrete functions in their software; others want to build a controlled AI practice across teams and processes.
For concrete requirements in new or existing software: we build AI features, agents and automations around systems, data, roles and user experience.
Our flagship for organizations that want to move AI from experimentation into operations: with clear strategy, governance, enablement, integration, measurement and continuous evolution.
AI creates value only when connected to the right context. That is why we consider processes, data, permissions, users, costs and architecture from the start.
We prioritize use cases with understandable impact, measurable criteria and realistic data conditions.
We connect AI to portals, backoffice, document flows, APIs and existing workflows.
We consider privacy, permissions, quality, costs, model behavior and human validation.
AI Craft
AI Craft combines consulting, engineering, integration, training, security and operations. Companies can start with one module or build a complete program.
AI Craft
Advise Govern Enable
Connect · Build · Measure · Manage
Assessment, opportunities, roadmap and initial business case.
Policies, allowed data, tool assessment, security and risks.
Workshops, playbooks, AI Champions and role-based support.
MCPs, APIs, authentication, permissions and traceability.
Agents, automations, copilots and AI-augmented software.
KPIs for adoption, productivity, quality, costs, risk and ROI.
Support, tool radar, cost optimization and continuous improvement.
Whether it is a single use case or an AI Craft program, we work step by step so decisions, risks, implementation and scaling stay understandable.
Understand maturity, processes, risks, systems, involved teams and possible starting points.
Define policies, allowed data, tools, security controls and human review.
Implement the case with real data, APIs, roles, agents or automations.
Support users, business areas, management and technical teams with suitable formats.
Measure adoption, productivity, quality, costs and risks, then derive the next areas.
AI adoption works when different groups know how to make decisions, check quality and use tools responsibly.
Evaluate investments, acceptable risk and clear expectations for each area.
Redesign processes with AI, steer quality and report progress clearly.
Apply prompting, agents, MCP, code reviews, tests and security limits in practice.
Use recurring tasks, documents, analysis and personal assistants in a controlled way.
Build internal contacts who answer questions, carry standards and escalate cases.
A good workshop does not end with inspiration alone. It should produce decisions and a prioritized, testable starting point.
We assess use cases by whether they reduce real work, improve decisions or extend existing software in a useful way.
When knowledge is spread across documents, wikis, tickets, files or emails and teams spend too much time searching.
When recurring documents must be read, compared, checked or transformed into structured data.
When service teams handle many similar requests and still need to maintain quality, tone and control.
When existing portals or tools can become more useful through intelligent features.
When unstructured information needs to be summarized, assessed or prepared for management decisions.
A pilot becomes credible when impact and operations are measurable. That is why we define metrics early and compare results against the baseline.
Active users, frequency, participating teams and covered use cases.
Time saved, cycle time, automated tasks and relieved routines.
Defects, rework, reviews, source quality, coverage and incidents.
Licenses, consumption, infrastructure, support and estimated value by process.
When AI is used in production, it needs care: new tools, costs, policies, connectors, agents and user questions keep evolving.
KI bleibt ein laufendes System, nicht ein einmaliger Rollout.
Backlog, prioritization, exceptions, review meetings and next scaling steps.
Assessment of new tools, models, agents and integrations with a clear recommendation.
Office hours, problem solving, updated guides and role-based support.
Keep licenses, consumption, duplicates, quotas and usage by team visible.
Serious AI projects need technical and organizational guardrails. We plan them early so a pilot does not later fail because of operations, privacy or adoption.
Who may see which data, and how do we prevent AI from answering from the wrong context?
Which answers are acceptable, how are sources shown and when is human review required?
How do model, infrastructure and operating costs develop under real usage?
How are teams enabled, and what rules do they need for responsible AI use?
Whether it is a concrete AI feature, integration into existing systems or AI Craft as an enterprise program: one first conversation is enough to clarify the right starting point.
