Web Systems · AI · Automation · Visibility

Station 03 // Automate

Workflows that move.

We connect systems, data, and decisions into automations that truly take work off people — visible, controllable, and without a new black box.

Configure automationView live flow ↓
Automation relayFlow status // active
01 // TriggerNew requestreceived
02 // DataCRM contextsynced
03 // AIClassify & enrichprocessing
04 // HumanApproval gatecontrolled
05 // OutputAction deployedcomplete
● 18 executions // no errors
Latency 420 msRelay secure
Automation protocol

From repetition to controlled flow.

Wir übersetzen Prozesse in belastbare Systeme mit klaren Triggern, Regeln, Ausnahmen und menschlichen Kontrollpunkten.

01
Observe

Make 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

Integrate human-in-the-loop, error paths, and monitoring.

05
Operate

Measure, improve, and expand automations.

Flow playground

Configure the relay.

Wähle einen Prozess. Die Station baut daraus live eine mögliche Automation mit Trigger, Logik, Kontrollpunkt und Output.

Automation engine

Was soll fließen?

Live prototype
Relay typeLead qualification relay
TriggerNew website request
Control gateSales approval
OutputQualified CRM opportunity
Diese Mission anfragen
01 // TriggerForm / Emailonline
02 // ContextCRM + Company Dataonline
03 // AIClassify + Enrichonline
04 // HumanApprove / Routecontrolled
05 // ActionCRM + Follow-uponline
Automation output

Less friction. More signal.

Der Wert einer Automation ist nicht, dass sie existiert. Sondern dass sie Zeit zurückgibt, Fehler reduziert und Entscheidungen schneller macht.

Manual steps-72%

14 → 4

Fewer handoffs in day-to-day work.

Cycle time-64%

2.8 h

From intake to durable action.

Error rate-81%

0.7%

Clear rules instead of copy-and-paste.

VisibilityLive

100%

Status, logs, and exceptions traceable.

Station dossier

Was ist ein KI-Agent?

KI-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.

Mehr dazu: AI agent: What it is and when it really pays off — Difference from chatbots, when agents create value and when not.

Automate modules

What do AI agents and automation deliver?

Konkrete Module für Agenten, Workflows und kontrollierte Automation.

MODULE

AI agents

24/7 advisory, pre-qualification, and assistance — trained on your company knowledge.

MODULE

Internal assistants

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

MODULE

Agentic workflows

Orchestrated multi-agent systems with roles, control, and handoffs.

MODULE

Process automation

Recurring tasks, data flows, and reporting — stable and monitored.

MODULE

Integrations

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

MODULE

Human in the Loop

Controlled approval steps for critical decisions.

Module 02 · Automate · AI agents

AI agents

AI agents at imping.digital carry out clearly scoped tasks: they plan steps, use tools and APIs, and deliver traceable results — unlike chatbots that only answer.

Typical uses: pre-qualifying inquiries, product and service advisory, internal knowledge questions. Company knowledge comes in under control (documents, FAQs, systems), with boundaries and optional human approval.

We start tightly scoped, measure quality, and expand — instead of an agent that should do “everything”.

Suitable for
Customer advisory24/7Pre-qualificationSales
Related technologies
LangGraphClaudeOpenAIRAG
Scope of work
  • Tasks with tools/APIs instead of pure chat answers
  • Training on your knowledge base (documents, systems)
  • Clear boundaries and audit trail
  • Pre-qualification and 24/7 first-line advisory
  • Stepwise expansion by quality metrics
In short

Chatbot or AI agent — what do we need?

If FAQ answers are enough, a leaner assistant often fits. As soon as systems should be written, processes started, or multi-step planning is required, an agent with tool access is the right frame.

Start mission →
Module 02 · Automate · Internal assistants

Internal assistants

Internal assistants answer questions from your own knowledge base: handbooks, Confluence, tickets, contracts, process docs — via retrieval-augmented generation (RAG).

Data quality, access rights, and source transparency matter. We build pipelines for indexing, updates, and evaluation so answers stay verifiable.

Ideal for support teams, ops, and specialist teams that otherwise search folders and chats.

Suitable for
KnowledgeOnboardingSupport
Related technologies
RAGVector DBClaude
Scope of work
  • RAG over your documents and knowledge sources
  • Rights and area separation
  • Source citations in answers
  • Index updates and quality checks
  • Connection to existing chat/ticket tools possible
In short

Are our data safe with an LLM provider?

We align architecture and hosting with your compliance requirements — including options for EU regions, no training use where contractually required, and minimal data exposure.

Start mission →
Module 02 · Automate · Agentic workflows

Agentic workflows

Agentic workflows orchestrate several specialized agents with roles, handoffs, and checkpoints — e.g. research → draft → review → execute.

We use frameworks like LangGraph/CrewAI where they add value, and keep determinism where LLMs are unnecessarily expensive or risky. Human-in-the-loop sits at the critical gates.

Goal: repeatable flows with clear accountability, not an opaque agent cloud.

Suitable for
ResearchDocumentationSupportContentData analysisOrchestrationScalingControl
Related technologies
LangGraphCrewAIAPIsRedis
Scope of work
  • Multi-agent orchestration with roles
  • Controlled handoffs and approval gates
  • Mix of LLM steps and deterministic logic
  • Traceable runs and error paths
  • Scaling from one workflow to portfolios
In short

When is multi-agent worth it vs. a single agent?

When tasks have clearly separable roles (research vs. writing vs. approval) or need different tools/rights. Otherwise a well-bounded single agent is often more robust and cheaper.

Start mission →
Module 02 · Automate · Process automation

Process automation

Not every automation needs an LLM. Recurring data flows, reporting, sync jobs, and approval chains we often solve classically — stable, testable, cost-efficient.

We map the as-is process, cut unnecessary steps, and automate what measurably costs time or errors. Monitoring and alerting are part of it.

AI joins where language, ambiguity, or unstructured inputs add value.

Suitable for
ReportingSyncJobsROI
Related technologies
n8nWorkerQueues
Scope of work
  • Process mapping and ROI prioritization
  • Stable jobs, syncs, and reporting pipelines
  • n8n and custom workers where it makes sense
  • Monitoring, retries, and error queues
  • AI only where it has real leverage
In short

Automation without AI — do you still do that?

Yes. Deterministic automation is often the better first mission. We add AI when unstructured inputs or decision support arrive.

Start mission →
Module 02 · Automate · Integrations

Integrations

Agents and automations are only as good as their connections. We connect CRM, ERP, CMS, tickets, and internal APIs via REST, webhooks, and OAuth — typically including HubSpot and n8n, plus customer-specific systems.

We document contracts, mapping, and failure cases so operations and further development do not hang on one person.

Part of Automate missions or as a dedicated integration workstream.

Suitable for
CRMERPData flowsHubSpot
Related technologies
RESTWebhooksiPaaS
Scope of work
  • CRM/ERP/CMS/ticket connections
  • OAuth, API keys, webhooks
  • HubSpot, n8n, and custom APIs
  • Data mapping and idempotency
  • Operations docs for your team
In short

Can agents write directly into our CRM?

Yes, with clear rights and often with human-in-the-loop before critical writes. Reading and pre-qualification usually come first; writing follows when quality holds.

Start mission →
Module 02 · Automate · Human in the Loop

Human in the Loop

Human-in-the-loop keeps people in the loop where compliance, money, reputation, or irreversible changes are at stake.

Agents work before and after the gate: prepare, propose, execute after approval. Every decision stays auditable.

This is not distrust of AI — it is system design for production use in the DACH region.

Suitable for
ApprovalControlAudit
Related technologies
ApprovalsReview
Scope of work
  • Approval gates at critical steps
  • Role-based approvers
  • Audit trail for proposals and decisions
  • Escalation paths under uncertainty
  • Stepwise autonomy after trust builds
In short

Doesn't human-in-the-loop slow everything down?

Only the critical paths. Most work runs asynchronously; approvals are targeted checkpoints — often faster than cleaning up bad automation after the fact.

Start mission →
Systems we connect

Your stack. One relay.

Wir arbeiten nicht gegen vorhandene Systeme, sondern bauen kontrollierte Verbindungen zwischen den Werkzeugen, die bereits im Einsatz sind.

Relay 01 // Sales

Lead → CRM → Action

Capture, score, enrich, assign, and automatically follow up on inquiries.

ModeACTIVE
ControlHuman approval
Relay 02 // Operations

Data → Decision → Task

Sync data from multiple sources and turn it into clear operational actions.

ModeSYNCED
ControlRule engine
Relay 03 // Service

Request → AI → Response

Classify requests, provide context, and activate teams only where humans are truly needed.

ModeONLINE
ControlEscalation path
Signal check

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 carries out tasks — including write access and process triggers, with clear boundaries and optional human approval.

When is an AI agent worth it for our company?

When tasks are recurring, language-heavy, and spread across several systems — e.g. research, pre-qualification, internal knowledge questions, or documented process chains. Deterministic single steps we often solve cheaper without an LLM.

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 with an audit trail.

Which systems can you connect?

CRM, ERP, CMS, ticket systems, and internal APIs via REST, webhooks, and OAuth — typically HubSpot, n8n, and customer-specific interfaces.

How do you start pragmatically with AI agents?

With a tightly scoped task, a clean data base, and approvals at critical points. Then expand step by step — instead of an “AGI agent” that should do everything.

Do you work GDPR-compliant in the DACH region?

Yes. Architecture, hosting options, and data flows are adapted to your compliance requirements. Contact: hello@imping.digital.

Trigger.Connect.Operate.

Automation channel // open

Ready to end the repetition?

Show me the process that eats time today. I'll check which parts should be automated — and which should stay human.

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