Manual processes with Automate AI, production ready
CodeGuides develops AI-powered automation solutions for companies: process documents, classify emails, extract data and close system breaks. Not as a demo, but in productive operation.
- Individual AI workflows, integrated into existing systems
- Documents, emails, tickets and ERP processes
- MVP approach: quick start with measurable results
- Secure AI infrastructure for internal data and automation workflows
Bring a specific process with you, we will check technical feasibility and automation potential.
- AI automation from Germany
- ERP and CRM integration
- Documents, emails, tickets
- Productive operation instead of demos
This is how CodeGuides connects input data, AI processing and your target systems
Input data
- PDF invoices & contracts
- Email inbox
- Form data & uploads
- ERP/CRM exports
- Support Tickets
AI processing
- Data extraction (OCR + LLM)
- Classification & Routing
- Transformation & Validation
- Error handling & logging
- Rule-based escalation
Result in the system
- ERP data record filled
- CRM entry updated
- Ticket routed correctly
- Report created automatically
- Notification triggered
Selected projects and customers
Where AI automation has the greatest leverage
Not every process is suitable for AI automation. These patterns show where use makes economic sense.
Manual process today
With AI automation
Invoices, contracts and forms that expire the same every day are ideal for AI automation.
Manual email categorization and forwarding costs time, AI classifies and routes automatically.
Manual data transfers between systems without a direct interface tie up capacity unnecessarily.
Incoming tickets are categorized, prioritized and enriched with standard information.
Manually compiling reports from different sources regularly costs hours.
AI-powered search delivers instant, accurate answers from your manuals, instead of manual searches.
AI automation works when it is embedded in real systems and processes.
CodeGuides develops the connection between the AI model, your data and your existing systems so that automation runs productively.
AI automation projectsthat we implement
No generic promises. These automations are technically feasible and bring measurable benefits.
Automate invoice processing
Incoming invoices as PDFs are opened manually, data is read out and transferred to the ERP.
KI automatically extracts supplier, amount, items and payment terms from PDFs and transfers structured data to the ERP.
- Less manual data entry
- Faster processing times
Email classification and routing
Incoming inquiries, orders and complaints are manually read, categorized and forwarded to the correct department.
AI classifies emails, extracts relevant fields and routes them automatically, with escalation logic for unknown categories.
- Faster response times
- Less manual sorting work
Contract review and risk marking
Contracts are checked manually for critical clauses, terms and notice periods, which is time-consuming and error-prone.
KI analyzes contract documents, highlights relevant passages and creates a structured summary of the key points.
- Faster contract review
- Systematic recording of critical clauses
Support ticket categorization
Incoming support tickets are manually read, prioritized and assigned to the correct team.
AI categorizes, prioritizes and routes tickets automatically, enriched with relevant information from the knowledge base.
- Faster initial response
- More even load distribution
Data transfer between systems
structured
Order created ✓
Data from forms, emails or exports is manually transferred to ERP, CRM or other systems.
AI extracts and transforms data, passes it to target systems in a structured manner, with validation and error logging.
- System breaks closed
- Lower error rate
Automated reporting
Reports are compiled and formatted manually from various sources on a weekly or monthly basis.
KI aggregates data from source systems, structures it and delivers ready-made reports, time-controlled or on demand.
- Less manual reporting work
- More consistent database
Do you recognize one of these processes? Then we will check the technical feasibility together.
The technical basis for productive AI automation
Many AI automations fail not because of the model, but because of the lack of a technical basis. Data is in SharePoint, CRM, ERP, ticket systems and PDFs. Rights, data protection, traceability and operation must be taken into account right from the start.
CodeGuides can build an in-house AI platform that connects your data sources, controls AI workflows and securely integrates automation into existing processes.
Architecture: From your data to AI automation
Your data sources
- SharePoint & Documents
- Email & Forms
- CRM & ERP
- Ticket systems
- Databases
Internal AI platform
- Roles & Rights
- RAG / Wissenszugriff
- AI workflows
- Interfaces
- Monitoring & Operations
Automation results
- Process steps without manual work
- Documents processed in a structured manner
- Systems filled automatically
- Escalations routed correctly
- Operation and control secured
Make documents, wikis, SharePoint, CRM, ERP, ticket systems or databases usable, controlled and traceable.
Employees only see the information that they are allowed to use. No uncontrolled data access.
Classification, extraction, summary or decision preparation become real, controllable process steps.
Monitoring, quality assurance and adjustments are not only considered after the MVP, but are planned from the beginning.
AI automation vs. classic RPA
Robotic Process Automation (RPA) automates rule-based processes. AI automation can handle unstructured content.
| Criterion | Classic RPA | AI automation |
|---|---|---|
| Unstructured data (free text, PDFs) | ✕ Not possible | ✓ Core competence |
| Content understanding & context | ✕ Click paths only | ✓ Meaning recognized |
| Flexible formats & layouts | ✕ Rigid structures | ✓ Format independent |
| Classification & prioritization | ✕ Fixed rules only | ✓ Semantisch gelernt |
| Maintenance effort for changes | Up, adjust script | Low, model adapts |
| Combination with system integration | ✓ Well suited | ✓ Ideal kombinierbar |
| AI infrastructure and database | Individual interfaces, no central AI database | Internal AI platform with data connection, rights and workflows possible |
This is how it works AI automation project
No long lead time, no months-long concept project. We start with your specific process.
Analyze process
Clarify processes, data sources, system landscape and exceptions.
Assess automation potential
ROI assessment, data suitability and pilot prioritization.
Technical concept
Plan AI model, API architecture, system connections, data protection and error handling.
Develop MVP
First productive version for the delimited process, you test with real data.
Testing & Quality Control
Systematically check and optimize accuracy, error rates and borderline cases.
Productive operation & expansion
Launch, monitoring and logging. Gradual expansion to further processes.
Why CodeGuides for AI automation
AI automation needs software development expertise, not just prompt engineering.
Backend & API development
AI automation needs clean backend architecture, data pipelines and interfaces to existing systems.
System integration from practice
ERP, CRM, DMS, REST APIs, we know integration problems from real projects, not from textbooks.
Monitoring & error handling
Automation in productive operation requires monitoring, logging and fallback strategies, built in from the start.
Data protection by design
What data leaves the company? Access rights and data protection concepts are taken into account right from the start.
Pragmatic MVP entry
No big concept project in advance. We start with a delimited pilot that is running productively.
Medium-sized business experience
Grown processes and system landscapes, not a startup greenfield. This experience flows into every project.
AI infrastructure instead of tool proliferation
Instead of individual, uncontrolled tools, we build an internal company AI infrastructure that brings together data, rights and workflows in a controlled manner.
AI automation usually fails not because of the model, but because of the connection to the system, the lack of error handling or data protection gaps. That is exactly our craft as software developers.
Alexander Hähnel · CodeGuides GmbH
Who is it for AI automation suitable?
AI automation makes sense when a process is recurring, data-rich and manually complex.
Well suited
- Medium-sized companies with recurring, manual back-office processes
- Companies with high email or document volumes
- Teams that want to close system gaps between ERP, CRM and other tools
- SaaS companies that want to integrate AI automation as a feature into their product
- Projects with a concrete process, measurable effort and existing data
Less suitable
- Very creative, highly complex decision-making processes without clear rules
- Processes without existing data or without a digital starting point
- One-off tasks with no repeat effect
Unsure? In the initial consultation, we check whether your process is suitable for AI automation.
Ready to automate a manual process with AI?
We examine technical feasibility, data availability and system integration, and show what can realistically be automated.
None yet clear automation process?
If the target process, data situation or prioritization are still open, structured AI consulting before implementation is often more useful than a direct automation project.
There we clarify which use cases are realistic, whether classic automation or AI is a better fit and which technical basis is necessary for a useful MVP.
Frequently asked questions about AI automation
What is AI automation?
AI automation refers to the use of AI models to automate manual, recurring business processes, e.g. document processing, email classification, data extraction or ticket routing. In contrast to classic RPA, AI can also handle unstructured content such as free text or varying document formats.
How much does an AI automation project cost?
A first MVP for AI automation typically costs between 15,000 and 50,000 euros, depending on the process, the data situation and the system connections. In the initial consultation you will receive a concrete assessment.
Which processes are suitable for AI automation?
Recurring, data-rich processes with manual effort: document processing, email classification, data extraction from PDFs, ticket routing, multi-source reporting and system breaks between ERP, CRM and other tools.
How long does an AI automation project take?
A first MVP for a clearly defined process is often productive in 6 to 12 weeks. More complex system integrations and multiple processes require correspondingly more time.
Do I need my own AI infrastructure?
No. We use existing AI services (e.g. OpenAI API, Azure OpenAI) and integrate them into your existing infrastructure. You don’t need to build your own AI infrastructure.
Can AI automation be implemented in a GDPR-compliant manner?
Yes. We develop with data protection-oriented architecture: data storage in Germany or the EU, clear access rights, logging. We recommend additional legal review by a data protection officer.
What is the difference to RPA?
Classic RPA automates rule-based click paths. AI automation understands content: It can analyze free text, interpret PDFs, classify emails and deal with varying formats.
Can you integrate AI into our ERP or CRM?
Yes, that is one of our focuses. We develop connectors and APIs for connecting AI solutions to ERP, CRM, DMS and other systems.
Can we start with a pilot?
Yes, we strongly recommend an MVP for a delimited process. This quickly delivers measurable results and is the basis for expansion decisions.
Do we need our own AI platform for AI automation?
Not always. For individual use cases, a slim MVP is sometimes enough. If multiple processes, internal data sources, rights or sensitive information are involved, having your own internal AI platform can make sense. In the initial consultation, we check which architecture suits your process.
Can you also connect AI with our internal data?
Yes. We can connect documents, databases, SharePoint, CRM, ERP, ticket systems or other sources, provided access, rights and data protection are clearly clarified.
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.
Request AI automation
Describe the process you want to automate. In the initial consultation we check suitability and technical feasibility.
Arrange a meeting
45 minutes, technical assessment of your automation project.
Was passiert danach?
Process check
This is what you should bring with you to the interview:
- Which process should be automated?
- How frequently and in what volume?
- Which systems are involved (ERP, CRM…)?
- Are there example documents or data?