Automate manual processes with AI, ready for production
CodeGuides develops AI-powered automation solutions for businesses: processing documents, classifying emails, extracting data and bridging gaps between systems. Not as a demo, but for production use.
- 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
Which business processes can you Have it automated with AI?
AI can be used to automate rule-based, recurring processes relating to unstructured data: reading and checking documents, classifying and responding to emails, routing tickets, data collection, creating reports and handovers to ERP and CRM systems. CodeGuides starts with a specific existing process and automates it productively instead of building a general AI demo.
Typical patterns: evaluate documents, classify emails and transfer them to the ERP, route tickets, process PDFs, generate reports. For the connection to existing software see AI integration, for local processing of sensitive data On-premise AI.
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 impact
Not every process is a good fit for AI automation. These patterns show where it makes business sense.
Manual process today
With AI automation
Invoices, contracts and forms with the same recurring workflows 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, without the need for 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.
AI automatically extracts suppliers, amounts, line items, and payment terms from PDFs and transfers the structured data to the ERP system.
- 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.
AI 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.
- Eliminating system silos
- Lower error rate
Automated reporting
Reports are compiled and formatted manually from various sources on a weekly or monthly basis.
AI aggregates data from source systems, organizes it, and delivers ready-to-use reports on a schedule 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 automation projects fail not because of the model, but because of an inadequate technical foundation. Data is spread across SharePoint, CRM and ERP systems, ticketing systems, and PDFs. Access rights, data protection, traceability and operations must all be considered 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 / knowledge access
- 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 they are authorized to access. 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 | ✓ Learns based on meaning |
| Maintenance effort for changes | High; scripts must be adjusted | Low, model adapts |
| Combination with system integration | ✓ Well suited | ✓ Ideal for combining |
| 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
Assess ROI and data suitability, and prioritize the pilot.
Technical concept
Plan AI model, API architecture, system connections, data protection and error handling.
Develop MVP
Build a first production-ready version for the defined process, then test it 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 choose CodeGuides for AI automation
AI automation requires software development expertise, not just prompt engineering.
Backend & API development
AI automation requires a clean backend architecture, data pipelines and integrations with existing systems.
System integration from practice
ERP, CRM, DMS and REST APIs: we understand integration challenges from real projects, not textbooks.
Monitoring & error handling
Automation in productive operation requires monitoring, logging and fallback strategies, built in from the start.
Data protection by design
Which data leaves the company? Access rights and data protection requirements are considered from the start.
Pragmatic MVP entry
No lengthy concept project up front. We start with a defined pilot and bring it into production.
Experience working with mid-sized companies
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 system integration issues, inadequate error handling or data protection gaps. That’s exactly the kind of challenge we solve as software developers.
Alexander Hähnel · CodeGuides GmbH
Who is AI automation Suitable for?
AI automation makes sense when a process is recurring, involves substantial amounts of data and requires significant manual effort.
Well suited
- Mid-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 add AI automation features to their products
- 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
Not sure? In an initial consultation, we’ll assess whether your process is a good fit for AI automation.
More AI services from CodeGuides
Automation is one of CodeGuides' several AI focus areas.
AI agency overview
An overview of all CodeGuides AI services, from consulting to production implementation.
Learn moreAI consulting for companies
Use case analysis and technical feasibility check before development.
Learn moreAI chatbot development
AI assistants that use your company’s data, with access controls and source citations.
Learn moreChatGPT & OpenAI integration
Integrate ChatGPT and OpenAI API into existing software, including open source models.
Learn moreAI process automation
Use cases and insights for mid-sized companies.
Learn moreReady to automate a manual process with AI?
We assess technical feasibility, data availability and system integration, and show what can realistically be automated.
Frequently asked questions about AI automation
What is AI automation?
AI automation uses AI models to automate manual, recurring business processes such as document processing, email classification, data extraction and ticket routing. Unlike traditional RPA, AI can also handle unstructured content, such as free text and documents in varying formats.
How much does an AI process automation project cost?
An initial AI automation MVP typically costs between €15,000 and €50,000, depending on the process, available data and system integrations. You’ll receive a specific estimate during an initial consultation.
Which processes are suitable for AI automation?
Recurring processes with substantial amounts of data and manual effort: document processing, email classification, data extraction from PDFs, ticket routing, reporting across multiple sources and gaps between ERP, CRM and other systems.
How long does an AI automation project take?
An initial MVP for a clearly defined process is often in production within 6 to 12 weeks. More complex system integrations and multiple processes take longer.
Do I need my own AI infrastructure?
No. We use existing AI services (e.g., the OpenAI API or Azure OpenAI) and integrate them with your existing infrastructure. You don’t need to build your own AI infrastructure.
Can AI automation be implemented in compliance with the GDPR?
Yes. We use a privacy-focused architecture, with data hosted in Germany or the EU, clear access controls and logging. We recommend an additional legal review by a data protection officer.
What is the difference to RPA?
Traditional RPA automates rule-based click paths. AI automation can interpret content: it can analyze free text, interpret PDFs, classify emails and handle 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 starting with an MVP for a clearly defined process. This delivers measurable results quickly and provides a basis for deciding whether to expand.
Do we need our own AI platform for AI automation?
Not always. A lean MVP is sometimes enough for a specific use case. If multiple processes, internal data sources, access permissions, or sensitive information are involved, an internal AI platform may make sense. In the initial consultation, we’ll assess which architecture fits 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 develops apps for consumers and businesses, with a primary focus on B2C apps. Our in-house team in Germany provides product planning, Flutter development and long-term operations, alongside custom software, AI automation and local AI infrastructure.
Request AI automation
Describe the process you want to automate. In the initial consultation we check suitability and technical feasibility.
Schedule a meeting
A 45-minute technical assessment of your automation project.
What happens next?
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?