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02 · Automate

AI agents that take over routine work.

I build AI agents and automations that take recurring work off your team: sorting requests, transferring data, preparing documents. Wherever a decision is needed, a person stays involved.

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Example flowRequest to action
01 // TriggerNew requestreceived
02 // DataCRM contextsynced
03 // AIClassify & enrichprocessing
04 // HumanApprovalapproved
05 // OutputAction takendone
● Every step is logged
Automated where it fitsPeople stay in charge
Approach

Turning routine into a reliable process.

I turn your processes into automations with clear triggers, rules, exceptions and human checkpoints.

01
Observe

Make the process, bottlenecks and handoffs visible.

02
Model

Define triggers, rules, data and decisions.

03
Connect

Connect CRM, ERP, APIs, databases and communication channels.

04
Control

Build in human approval, error paths and monitoring.

05
Operate

Measure, improve and extend the automations.

Examples

Which process costs you time?

Choose a process to see what an automation for it could look like: trigger, context, AI step, approval and result.

Example flow

Which process?

FlowPre-qualify leads
TriggerNew website enquiry
CheckpointSales approval
ResultQualified CRM opportunity
Ask about this process
01 // TriggerForm / Emailautomatic
02 // ContextCRM + company dataautomatic
03 // AIClassify + enrichautomatic
04 // HumanApprove / routeapproval
05 // ActionCRM + follow-upautomatic
Benefits

What automation delivers.

An automation pays off when it gives time back, prevents errors and speeds up decisions. I estimate the effect for your case up front and measure it after launch.

Manual workless

Fewer handoffs

Data moves between systems automatically instead of by copy and paste.

Cycle timeshorter

Faster processes

Requests are captured, classified and routed immediately.

Errorsfewer

Fewer mistakes

Fixed rules instead of retyping and chasing.

Transparencyalways

Fully traceable

Status, logs and exceptions are visible at any time.

Definition

What is an AI agent?

An AI agent is a software system that carries out a clearly defined task on its own: it plans steps, uses tools and APIs, pulls information from data sources and delivers a traceable result. Unlike a chatbot, the agent acts – it calls systems, writes data and starts processes.

Read more: AI agent: What it is and when it really pays off – How agents differ from chatbots – and when an agent pays off and when it does not.

Services

What AI agents and automation do for you.

Select a service for details, typical use cases and technologies.

Service

AI agents

Round-the-clock advice, lead pre-qualification and assistance – using your company’s knowledge.

Service

Internal assistants

AI assistants built on your own documents and processes (RAG).

Service

Agentic workflows

Several specialised agents working together – with clear roles, handoffs and control.

Service

Process automation

Recurring tasks, data flows and reports – stable and monitored.

Service

Integrations

Connecting CRM, ERP and other tools via APIs and webhooks.

Service

Human in the loop

Human approval at the points where it matters.

02 · Automation · AI agents

AI agents

An AI agent carries out clearly scoped tasks: it plans steps, uses tools and interfaces and delivers traceable results – unlike a chatbot, which only answers.

Typical uses: pre-qualifying enquiries, advising on products and services, answering internal knowledge questions. Your company knowledge comes in under control (documents, FAQs, systems) – with clear boundaries and human approval where you want it.

I start with one tightly scoped task, measure quality and then expand step by step. That way you do not end up with an agent that is supposed to do “everything” and does nothing reliably.

Suitable for
Customer advice24/7Pre-qualificationSales
Technologies
LangGraphClaudeOpenAIRAG
Scope of work
  • Completes tasks via tools and APIs instead of just answering
  • Works with your knowledge (documents, systems)
  • Clear boundaries and complete logging
  • Pre-qualification and first-line advice around the clock
  • Step-by-step expansion based on quality metrics
In short

Chatbot or AI agent – what do we need?

If answers to common questions are enough, a lean assistant often does the job. As soon as data needs to be written to systems, processes started or several steps planned, an agent with access to tools is the right approach.

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02 · Automation · Internal assistants

Internal assistants

Internal assistants answer questions from your own knowledge base: handbooks, Confluence, tickets, contracts, process descriptions. Technically this uses retrieval-augmented generation (RAG): the assistant first finds the relevant documents and then writes its answer based on them.

Data quality, access rights and traceable sources are what matter. I build the pipelines for indexing, updates and quality checks so that answers stay verifiable.

Ideal for support, operations and specialist teams who would otherwise spend a long time searching folders and chats.

Suitable for
KnowledgeOnboardingSupport
Technologies
RAGVector DBClaude
Scope of work
  • RAG over your documents and knowledge sources
  • Access rights and separated areas
  • Source references in the answers
  • Ongoing updates and quality checks
  • Can connect to existing chat and ticket tools
In short

Is our data safe with an AI provider?

I align architecture and hosting with your compliance requirements – with options for EU data centres, no use of your data for training where contractually required, and as little data sharing as possible.

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02 · Automation · Agentic workflows

Agentic workflows

Agentic workflows connect several specialised agents with fixed roles, handoffs and checkpoints – for example research → draft → review → execution.

I use frameworks such as LangGraph or CrewAI where they add value. Where a language model would be unnecessarily expensive or risky, the logic stays hard-coded. At the critical points a person decides (human in the loop).

The goal: repeatable processes with clear accountability that you can follow at any time.

Suitable for
ResearchDocumentationSupportContentData analysisOrchestrationScalingControl
Technologies
LangGraphCrewAIAPIsRedis
Scope of work
  • Several agents working together with clear roles
  • Controlled handoffs and approval points
  • Mix of AI steps and hard-coded logic
  • Traceable runs and error paths
  • From the first workflow to further processes
In short

When are several agents better than one?

When the task splits clearly into roles (research, writing, approval) or the steps need different tools and permissions. Otherwise a well-scoped single agent is often more robust and cheaper.

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02 · Automation · Process automation

Process automation

Not every automation needs AI. I often solve recurring data flows, reports, syncs between systems and approval chains the classic way – stable, testable and cost-effective.

I map the current process, remove unnecessary steps and automate what measurably costs time or causes errors. Monitoring and alerts when something goes wrong are part of it.

AI comes in where language, ambiguous input or unstructured data are involved.

Suitable for
ReportingData syncJobsROI
Technologies
n8nWorkerQueues
Scope of work
  • Process mapping and prioritisation by benefit
  • Stable jobs, syncs and reporting pipelines
  • n8n or custom workers, depending on needs
  • Monitoring, automatic retries and error queues
  • AI only where it adds real value
In short

Do you also automate without AI?

Yes. Classic, rule-based automation is often the better first step. I add AI when unstructured input or decision support comes into play.

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02 · Automation · Integrations

Integrations

Agents and automations are only as good as their connections. I connect CRM, ERP, CMS, ticket systems and internal APIs via REST, webhooks and OAuth – often with HubSpot and n8n, plus custom systems.

I document interfaces, data mapping and failure cases so that operation and further development do not depend on a single person.

As part of an automation project or as a standalone integration project.

Suitable for
CRMERPData flowsHubSpot
Technologies
RESTWebhooksiPaaS
Scope of work
  • Connection of CRM, ERP, CMS and ticket systems
  • OAuth, API keys and webhooks
  • HubSpot, n8n and custom APIs
  • Clean data mapping without duplicates
  • Operations documentation for your team
In short

Can agents write directly to our CRM?

Yes – with clearly defined permissions and often with human approval before critical changes. We usually start with reading and pre-qualifying; write access follows once quality is right.

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02 · Automation · Human in the loop

Human in the loop

Human in the loop means: for anything involving compliance, money, reputation or irreversible changes, a person decides.

The agents work before and after that approval: they prepare, propose and execute once approved. Every decision is logged in a traceable way.

This lets you use AI productively without giving up control over important decisions.

Suitable for
ApprovalControlAudit log
Technologies
ApprovalsReview
Scope of work
  • Approval points at critical steps
  • Role-based approvals
  • Log of all proposals and decisions
  • Escalation when in doubt
  • More autonomy once trust has grown
In short

Doesn’t human in the loop slow everything down?

Only at the critical points. Most of the work carries on in the background; approvals are targeted checkpoints – and often faster than cleaning up after a faulty automation.

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Typical flows

Your systems, properly connected.

I build on the tools you already use and connect them with clear rules and checkpoints.

Example 01 // Sales

Request → CRM → Action

Capture, score, enrich and assign enquiries, then follow up automatically.

ControlHuman approval
Example 02 // Operations

Data → Decision → Task

Reconcile data from several sources and turn it into clear next steps for your team.

ControlFixed rules
Example 03 // Service

Request → AI → Response

Classify requests, provide context and involve your team only where people are really needed.

ControlEscalation to your team
FAQ

Frequently asked questions

What is the difference between a chatbot and an AI agent?

A chatbot answers inputs. An AI agent plans steps, uses tools and APIs and completes tasks – including writing data and starting processes, with clear boundaries and optional human approval.

When is an AI agent worth it for our company?

When tasks recur, involve a lot of language and are spread across several systems – such as research, pre-qualification, internal knowledge questions or documented process chains. I often solve simple, rule-based steps more cheaply without AI.

Do you build agentic workflows with human in the loop?

Yes. Critical decisions, compliance and quality approvals stay with people. The agent works in between – traceably and fully logged.

Which systems can you connect?

CRM, ERP, CMS, ticket systems and internal APIs via REST, webhooks and OAuth – often HubSpot, n8n and custom interfaces.

How do you start pragmatically with AI agents?

With one tightly scoped task, a clean data foundation and approvals at the critical points. Then expand step by step – rather than building a “super agent” that is supposed to do everything.

Do you work GDPR-compliant in the DACH region?

Yes. I align architecture, hosting options and data flows with your compliance requirements. I am happy to answer questions directly: hello@imping.digital.

Trigger.Connect.Operate.

Free initial call

Ready for less routine?

Show me the process that costs the most time today. I will check which steps can be automated and which should deliberately stay with people. The initial call is free and takes 30 minutes.

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