Use AI sensibly
CodeGuides helps you find sensible AI use cases, realistically evaluate the effort and benefits and implement concrete AI projects in a technically clean manner, from the initial potential analysis to the MVP.
- Identify and prioritize AI use cases
- Realistically assess feasibility, effort and benefits
- Think about data protection, interfaces and operations early on
- Concrete roadmap instead of general AI workshop
- From the AI roadmap to the internal AI platform
- Implementation possible by an experienced software team
45 minute initial consultation. We talk about your processes, possible AI use cases and a realistic initial assessment. No AI pitch.
- AI consulting from Germany
- Use case evaluation
- Feasibility & data protection
- Consulting through to MVP implementation
This is how CodeGuides evaluates your AI use cases: structured, realistic, implementable
Use case collection
- Document processing
- Email classification
- Interne Wissenssuche
- Reporting & Analysis
- Support relief
Assessment & prioritization
- Benefits & time savings
- Feasibility & data situation
- Integration effort
- Data protection & rights
- MVP potential
Result
- Prioritized use cases
- MVP recommendation
- Roadmap & next steps
- Risks & open questions
- Implementation offer optional
Selected projects and customers
For medium-sized companies, B2B software, interfaces and digital solutions, implemented for production.
Why AI is often used in medium-sized companies doesn't go beyond experiments
Many companies start with individual ChatGPT tests, tool demos or general AI workshops. There will be no real benefit if use cases are not evaluated, data sources are not checked and technical implementation, data protection and operation are not taken into account.
Typical problems
How CodeGuides does it differently
Secure AI infrastructure instead Tool proliferation
Many medium-sized companies start with individual AI tools, but quickly realize that the real leverage only arises when AI can securely access internal data, documents and processes. A mere tool recommendation is not enough.
CodeGuides not only evaluates use cases, but can also create the technical basis: an internal company AI platform that connects your data sources, rights, workflows and existing systems.
Architecture: From your data to the company's internal AI platform
Your data sources
- SharePoint & Documents
- PDFs & Forms
- CRM & ERP
- Ticket systems
- Databases
- Internal applications
Internal AI platform
- Roles & Rights
- RAG / Wissenszugriff
- AI workflows
- Interfaces
- Monitoring & Quality
- Data protection
Your applications
- Interne Wissenssuche
- Document analysis
- Request classification
- Support assistance
- Process automation
- Departmental copilots
Make your own data usable securely
Documents, knowledge databases, SharePoint, CRM, ERP and ticket systems are controlled and connected in accordance with data protection regulations.
Replace shadow AI in a structured manner
Instead of uncontrolled tool use, a clear, secure AI infrastructure with defined rights and processes is created.
Bringing departments and IT together
Technical requirements, data protection, rights and technical integration are considered together and not one after the other.
From the first use case to the platform
An MVP can be built in such a way that later AI applications can be built on top of it without having to start from scratch.
Check private cloud or on-premise
Depending on data protection requirements, a secure AI infrastructure can be operated on-premise, privately or hybrid.
Consulting and implementation from a single source
We don't stop at the roadmap, but rather take on architecture, MVP, integration and operation directly with our own software team.
Which AI use cases can be useful in medium-sized businesses
Not every process needs AI. We examine where AI really makes a difference and where classic automation or better software is the more sensible solution.
Evaluate documents and PDFs
Information from invoices, forms, evidence or contracts is read and transferred manually.
Extract, check, summarize and further process data in a structured manner, without manual effort.
- Less manual testing work
- Faster processing
Classify emails and requests
Incoming messages are manually read, sorted and forwarded to the right places.
Automatically recognize and assign concerns, priority, customer type and next steps.
- Faster response times
- Better handoffs
Interne Wissenssuche
Knowledge is distributed in PDFs, wikis, tickets, folders or SharePoint and is difficult to find.
Internal AI assistant with access to shared knowledge sources and clear source information.
- Faster answers
- Fewer queries to experts
Relieve support and customer service
Support cases must be read, categorized and prepared for processing manually.
Automatically create summaries, suggested answers and ticket prioritization.
- More efficient editing
- More consistent quality
Prepare offer and sales processes
Requirements from conversations, emails or forms are manually transferred into offers.
Structure information, identify gaps and automatically prepare offer templates.
- Faster quotation creation
- Less rework
Reporting and decision-making basis
Data from different sources are regularly merged and prepared manually.
Condense data, detect anomalies and automatically prepare management summaries.
- Faster decisions
- Less manual reporting
Do you recognize one of these areas? Then we will check together what could be technically implemented.
Which AI projects are really worth it
Not every AI idea is a useful project. These criteria help to assess whether a use case can be realistically implemented and economical.
| Evaluation criterion | Why it matters | Good prerequisites |
|---|---|---|
| Repeatability of the process | One-off tasks rarely pay off AI investment | Process runs daily or weekly |
| Data availability | AI needs examples, documents or structured data | Recurring documents, emails or data present |
| Manual time saving | Low effort does not justify AI development | Process takes several hours per week |
| Integration effort | Bad interfaces increase effort and risk | Systems have APIs or export options |
| Privacy and rights requirements | Sensitive data needs clear concepts | Data protection concept can be considered early on |
| MVP capability | First steps that are too big fail more often | Delimited sub-process possible as first MVP |
| Technical benefit | Only measurable improvements justify investment | Time savings or quality improvement can be clearly quantified |
| Quality of the sample data | Bad training data produces poor results | Existing data is consistent and representative |
AI consulting, the does not end at slides
The difference is not just in the result, but in what happens afterwards.
| Criterion | Classic AI strategy consulting | CodeGuides AI advice |
|---|---|---|
| Ziel | Strategy paper and collection of ideas | Decision basis and actionable MVP candidates |
| Result | Workshop documentation | Prioritized use cases, feasibility, roadmap, next steps |
| Technical depth | Often conceptual without knowledge of the system | Data, interfaces, architecture and operation are taken into account |
| Implementation | Must be ordered separately later | Implementation possible by our own software team |
| Realism | Often lots of ideas, little prioritization | Focus on benefit, effort, risk and MVP capability |
| Data protection | Often not considered | Data protection and access rights from the start |
| Technical basis | Recommends tools or describes target images | Can directly develop internal AI platform, data connection and MVP |
This is how it works AI advice with CodeGuides
No long lead-up, no pure strategy round. We start with your specific processes and give a clear assessment.
Initial consultation
We understand your initial situation, goals, processes and previous AI experiences.
Process and use case collection
We collect possible areas of application from specialist departments, IT and management.
Assessment and prioritization
We evaluate the benefits, feasibility, effort, data situation, risks and MVP potential.
Technical classification
We check interfaces, data sources, system landscape, data protection and operations.
Roadmap and MVP proposal
You will receive a clear recommendation as to which use cases make sense and what you should start with.
Conversion optional
If a use case is convincing, we can take over MVP, integration and further development directly.
What you get at the end of the AI consultation
Not a general strategy paper. But concrete foundations for an informed decision.
Prioritized AI use cases
Evaluated according to benefits, feasibility and effort, not an unfiltered collection of ideas.
Evaluation according to benefits & feasibility
Clear assessment of time savings, complexity and requirements for each use case.
Assessment of data & interfaces
What is there, what is missing, what needs to be built.
Risks and open questions
Data protection, data quality, integration hurdles and realistic limits.
Recommendation for the first MVP
Which use case is suitable as a first step and why.
Concrete next steps
What needs to be done now, with whom, in what order and with what time horizon.
Why CodeGuides for AI consulting in medium-sized companies
We are not a pure strategy consultancy or a pure AI tool provider. We combine consulting, software development and productive implementation.
Consulting and implementation from a single source
We not only think strategically, but can also implement sensible use cases directly from a technical perspective.
Software development as a core competency
AI projects need stable systems, interfaces, data models and operations.
Realistic view of AI
We only recommend AI where it offers real added value compared to classic software.
Understanding for medium-sized businesses
We pay attention to budgets, existing systems, pragmatic implementation and quick usability.
Bringing departments and IT together
We translate requirements into technical solutions and restrictions into understandable decisions.
MVP instead of major project
We prefer to start with a clearly defined, provable first step rather than with an oversized transformation project.
AI consulting is only valuable if it is clear in the end what can be implemented, what it costs and what it brings. Everything else is an expensive workshop.
Alexander Hähnel · CodeGuides GmbH
Who is it for AI consulting makes sense?
AI consulting is worthwhile if you need clarity about potential, feasibility and next steps before you invest.
Good if...
- You want to use AI sensibly, but don't know where to start
- There are several ideas, but no clear prioritization
- Processes are manual, document-heavy or recurring
- Departments see potential, but IT has to check feasibility and security
- You want to understand the effort, benefits and risks before implementation
- You don't want a pure strategy presentation, but rather concrete next steps
Not particularly suitable if...
- You're just looking for a general AI talk
- You don't want to look at internal processes or data
- You are looking for ready-made standard software without customization
- You want to introduce AI regardless of benefits and feasibility
Unsure? In the initial consultation, we will clarify whether and where AI makes sense for you.
Unsure where AI really makes sense for you?
We examine your processes together, prioritize realistic use cases and show which first MVP would make sense.
30 minute initial consultation · concrete assessment · no AI pitch
Frequently asked questions about AI consulting in medium-sized businesses
What does an AI consultancy do for medium-sized businesses?
An AI consultancy for medium-sized businesses identifies realistic AI use cases, evaluates the benefits, effort and feasibility, checks the data situation and interfaces and gives a clear recommendation on what can make a sensitive start. The result is not a general strategy, but a concrete basis for decision-making.
Do we need concrete AI ideas yet?
No. It is enough if you describe which processes seem complex to you or where you think there is potential. The collection, evaluation and prioritization of concrete use cases is part of the consultation.
What is the difference between AI consulting and AI automation?
AI consulting is the first step: We clarify which use cases make sense and are feasible. AI automation is the next step: We implement selected use cases in a technically productive manner. Both can be combined with CodeGuides.
How do we find useful AI use cases?
Through structured discussions with departments, IT and management. We specifically ask about recurring, manual, data-rich processes and then evaluate where AI realistically offers added value.
How is it assessed whether an AI project is worthwhile?
We evaluate use cases based on benefits, time savings, feasibility, data availability, integration effort, data protection and MVP capability. The result is a comprehensible prioritization, not a gut decision.
Is data protection taken into account?
Yes, from the beginning. We clarify early on which data is affected, where it will be processed and what requirements there are for access rights, logging and data storage. If necessary, we recommend involving a data protection officer.
Can CodeGuides take over the implementation after the consultation?
Yes. If a use case is prioritized, we can take on MVP, interfaces, integration and further development directly. This is an advantage over pure strategy consultancies that do not have their own implementation expertise.
How much does an AI consultation cost?
The initial consultation is free of charge. A complete use case evaluation and prioritization is calculated individually, depending on the scope, number of processes and system landscape. In the initial consultation you will receive a transparent assessment.
How quickly can a first MVP be created?
A delineated MVP for a clearly defined use case is often productive in 6 to 10 weeks. That depends on the process, data situation and system connection.
Is AI always the right solution?
No, and we say that clearly. Sometimes classic automation, a better software solution or simple process optimization makes more sense than AI. Honesty about this is part of our advice.
What is an in-house AI platform?
An in-house AI platform connects AI functions with shared data sources, roles, rights and workflows. Employees can use AI for internal information and processes in a controlled manner without having to copy sensitive data into individual external tools. This is safer and more comprehensible than shadow AI.
Can CodeGuides also take over the implementation after the consultation?
Yes. This is exactly what makes us different from pure strategy consulting. After evaluating the use cases, we can also take over architecture, MVP, data connection, interfaces and operation from a single source.
CodeGuides is an app and AI agency from Germany for Flutter apps, custom software, AI automation and local AI infrastructure. Consulting and implementation come from a single source: We evaluate use cases technically, build the first pilot and scale it up to company-wide operation, GDPR-compliant and with hosting in Germany.
Have your AI potential checked free of charge
Briefly describe your initial situation, your processes or your previous AI ideas. We give you an honest assessment of where AI can be useful and what next step would be realistic.
What happens after the request?
- We will contact you personally within 1 working day
- We talk about your initial situation and possible AI use cases
- You will receive an initial assessment of the benefits, effort and feasibility
- We show sensible next steps
- We check whether individual use cases are sufficient or whether an internal AI platform would make sense as a basis
- NDA possible if required
- No sharing of your data, no AI pitch
You should bring this with you to the interview
- Which processes are the most complex?
- Are there already AI ideas or experiments?
- Which systems are in use (ERP, CRM…)?
- Who should take part in the conversation?