Skip to content
Start project
AI Automation • Operations • Integration

AI Automation for Recurring Workflows and Data Processes

We connect existing systems, reduce manual handovers, and use AI where it can support service, planning, or data analysis in a traceable way.

Request a Free Potential Analysis
Discovery → Prototype → Integrate → Operate
Technology partners
AWSAzureGoogle CloudSAPUiPathn8nPythonNode.jsPostgreSQLRedisDockerKubernetesWhatsApp
Problem & urgency

The challenge

Overloaded Support Teams

Service teams lose time answering recurring requests while customers wait for reliable responses across channels. Without automation, quality depends too heavily on availability, manual routing, and individual knowledge.

Unreliable Forecasts

Planning often relies on fragmented data, spreadsheets, and gut feeling. Demand shifts, seasonal patterns, and supply volatility are detected too late, which leads to overstocking, stockouts, or missed revenue potential.

Missed Revenue Opportunities

If every customer sees the same content, offers, and follow-up, valuable buying signals remain unused. Without personalized recommendations or next-best actions, cross-sell and upsell potential gets lost in critical moments.

Opaque Customer Feedback

Reviews, tickets, surveys, and social feedback are spread across channels. Teams spend too much time interpreting sentiment manually and react late to recurring issues or changes in customer expectations.

Solution & value

Our solution

AI Assistants for Service and Operations

We design assistants that answer standard questions, qualify requests, route tickets, and hand complex cases over to the right team with context.

Shorter response times, cleaner routing, and more capacity for conversations that need human judgement.

Predictive Demand and Inventory Planning

We connect relevant data sources and build forecasting workflows that surface demand patterns, stock risks, and planning anomalies earlier.

More reliable planning, optimized stock levels, and fewer costly surprises in supply and fulfillment.

Personalized Customer Journeys

We implement recommendation logic and next-best-action workflows that use behavior, context, and existing customer data responsibly.

More relevant offers, stronger conversion potential, and better timing for cross-sell and upsell moments.

Automated Sentiment Intelligence

We consolidate feedback from reviews, support tickets, surveys, and social channels into dashboards that highlight what needs attention.

Faster decisions and a clearer view of recurring customer concerns, product issues, and service opportunities.

AI Automation for Recurring Workflows

Many teams lose time to recurring handovers between ERP, CRM, accounting, and email. When data is entered more than once, invoices are transferred manually, or messages are sorted by hand, errors and unnecessary waiting time become part of daily operations.

We connect existing systems through APIs and add AI workflows where they can support rules, text, or data responsibly. Large language models, AI agents, and classic automation are used so results stay traceable and your team can manage routine work more clearly.

See How We Work in Detail →
01

Process Mining & Bottleneck Analysis

Automation without a clear goal often moves problems into a new tool. We analyze existing business processes, identify manual bottlenecks, media breaks, and hidden data silos. Then we prioritize workflows by effort, risk, and expected relief.

Process MiningROI CalculationBottleneck Analysis
02

API Architecture & Data Consolidation

Clean data flows are the foundation of useful automation. Instead of immediately introducing new monolithic software, we use middleware such as n8n and custom Python or Node.js scripts to connect existing systems through APIs. Data flows are consolidated step by step and synchronized in both directions where it makes sense.

API-FirstSystem Integrationn8n & Middleware
03

AI Agents & Smart Logic (LLM)

We add AI to classic rules where language, documents, or ambiguous data are involved. OpenAI, local LLMs, or machine learning are selected according to privacy, cost, and operating model. Typical use cases include data extraction from PDFs, email classification, and prequalification of complex customer requests.

LLM IntegrationAI AgentsOCR Data Extraction
04

Continuous Monitoring & Error Handling

An automated process that stops unnoticed can quickly create operational effort. That is why we do not hand over a black box: dashboards, logging, and error handling make issues visible and easier to fix when APIs or data formats change.

Workflow MonitoringError HandlingOperations

Free & no obligation

Let’s plan your next step

In 15 to 20 minutes you’ll get a clear assessment and concrete next steps.

  • 15 to 20 minutes of clarity
  • A roadmap, not guesswork
  • Free and no obligation
Schedule a free call

AI Process Automation | Tech stack

Make unstructured data actionable

Unstructured data from contracts, emails, or PDFs can be analyzed and structured with LLM pipelines. We extract relevant information, convert it into machine-readable formats, and connect it to ERP or CRM systems where needed. This reduces manual transfer work and makes data more useful in daily processes.

RAGStructured OutputEntity RecognitionDocument AIData Extraction

AI-assisted outreach automation

LLM-assisted text generation can prepare follow-ups, segmentation, and outreach without fully automating communication. We build workflows that use CRM data and user context to give your team better drafts for relevant messages.

LLM AgentsContext OptimizationBehavioral TriggersZero Shot PromptingCRM Integration

Demand forecasting and inventory optimisation

Use predictive machine learning to forecast supply chain demand with data-backed confidence. Our models analyze historical sales data and seasonal patterns to calculate future material needs more reliably. This helps you reduce overstock, lower tied-up capital, and optimize supply chain planning.

Time Series ForecastingPredictive AnalyticsGradient BoostingSupply Chain MLRegression

Automated customer insight analysis

Customer feedback from reviews, support tickets, or surveys can be evaluated more systematically with NLP models. We categorize recurring topics and make trends visible in dashboards so product and service teams can respond earlier.

NLPSemantic SearchSentiment AnalysisTopic ModelingVector Embeddings

Technology is only as good as the people behind it.

Alexander Haus

Senior AI & Automation Consultant

“I make sure AI projects are built on stable data pipelines, clear interfaces, and backends that teams can operate.”

Short profile: Alexander Haus

Mohamed-Ali Masmoudi

AI Engineer & Solution Architect

“I design RAG, agent, and integration architectures so they work with real data, clear interfaces, and traceable evaluation in production.”

Short profile: Mohamed-Ali Masmoudi

Experiences that matter

Thomas M.

“The system helps our dispatch team in their day-to-day work and saves us time every day. We were able to introduce it with limited disruption, and the support has been reliable. We're very happy with the result.”

Head of Dispatch, Eurocargo

Christoph Albrecht

“The team carefully planned the architecture for a complex integration project and built a reliable connection between our railway systems and modern streaming platforms. We particularly valued how they managed technical risks and coordinated work across departments.”

IT Project Manager, DB Netz AG · now DB InfraGO AG

Luca Fernandes

“We used to rely on a lot of manual handoffs between sales and fulfillment, which left room for errors. The new automation is well documented and saves us a great deal of time in our day-to-day work.”

E-Commerce / Wholesale, L’Oréal

Thomas M.

Head of Dispatch, Eurocargo

“The system helps our dispatch team in their day-to-day work and saves us time every day. We were able to introduce it with limited disruption, and the support has been reliable. We're very happy with the result.”

Christoph Albrecht

IT Project Manager, DB Netz AG · now DB InfraGO AG

“The team carefully planned the architecture for a complex integration project and built a reliable connection between our railway systems and modern streaming platforms. We particularly valued how they managed technical risks and coordinated work across departments.”

Luca Fernandes

E-Commerce / Wholesale, L’Oréal

“We used to rely on a lot of manual handoffs between sales and fulfillment, which left room for errors. The new automation is well documented and saves us a great deal of time in our day-to-day work.”

1 / 3

CHECK
THE NEXT STEP

Ready to Review Your Next Automation Step?

No obligation. No cost. In a short call, we clarify which workflows can realistically be automated.

How long does a typical implementation take?

It depends on scope and your data readiness. In the potential analysis we map a realistic timeline and define quick wins first.

What does an automation project cost?

Pricing depends on scope and expected ROI. We can start with a fixed-scope workshop and expand once the value is clear.

Is the solution secure and aligned with privacy requirements?

Security and data protection are part of the design: access control, auditability, and a hosting approach that matches your requirements.

Do we need internal AI expertise?

You do not need an internal AI team to start. Domain experts and access to the relevant systems are important. Training and support can be planned where needed.

Request a Free Potential Analysis