Recurring work that moves forward reliably.

Deliberately divide fixed rules from AI

An automation does not need a language model at every step. Use understandable rules for totals, deadlines and deterministic matching where sufficient. AI supports text, unstructured content and preparatory work where useful. This division keeps the process understandable and simplifies later troubleshooting.

Define triggers, data and effects as a contract

We document what triggers a task, permitted inputs and intended output. For example, a customer inquiry is routed, enriched with approved account data and prepared as a response draft. An external change receives separate approval. Systems, owners and acceptance criteria are confirmed before implementation.

Treat failures as a normal part of operations

Tests include incomplete input, duplicate events, expired authentication and timeouts. The workflow shows which step remains open and prevents uncontrolled repetition of external actions. Manual handling remains possible. Success is verified through the actual outcome, not merely technically successful execution.

Enter operations through a measurable pilot

You receive the working version, tests, understandable operating instructions and ownership assignments. Pilot evaluation compares cycle time, rework and errors against the previous process. The workflow scorecard supports selection, while the AI system check gathers key information for joint assessment. Expansion follows acceptance of the agreed scope.

Connect repeated handovers reliably

Enquiries are copied, information is maintained in several places and reports are repeatedly assembled from the same sources. This work consumes attention even when its steps follow a familiar pattern. AI automation connects those steps and adds language processing where simple field mapping is insufficient.

We build bounded processes with a clear input and result. These may prepare responses, content briefs, CRM handovers or recurring analyses. Before implementation, we establish which actions follow rules and which still require subject-matter judgement.

Who a project suits

The service fits small businesses and marketing teams with recurring tasks, several systems and an identifiable process owner. A strong starting point is work that occurs regularly and produces an outcome someone can reliably assess.

Where tasks and responsibilities are unclear, we begin by mapping the process. Not every manual step needs AI. A fixed rule may be enough for a straightforward assignment, while a limited AI step can help structure an unformatted message.

What implementation includes

Within the agreed project, we define the trigger, data flow, system access and intended output. We build the specified connections, establish review and approval points and make failure states visible. Where a participating service can be unavailable, planning includes an appropriate retry or manual handling route.

Handover includes an understandable operating description and the agreed checks. We also document ownership and which changes require another review. Additional systems, new workflows and ongoing support are specified separately in the proposal.

From working example to usable process

We first inspect the current workflow and representative inputs. Together we agree the objective, scope and technical prerequisites. A limited initial process is tested against suitable examples before the agreed introduction and joint review of results.

In an explicitly hypothetical scenario, a website enquiry is stored, matched with an existing CRM contact and grouped by the requested offer. AI prepares a short summary and response draft. A person checks both and continues the conversation. Missing information becomes a proposed question rather than an invented answer.

Our own product · Technology in practice

In Verbandbuch.org, we built AI into a specific language task: foreign-language QR reports of first-aid incidents and near misses can be translated into German. Responsible staff review the result and explicitly choose whether to apply it. Our own product shows AI assistance and human approval working together within one process.

In SiGeKo, web AI prepares editable proposals from text, dictation, photos and files. Users review sources and values, select individual entries and explicitly apply them. Safety-plan and training drafts, as well as preliminary document reviews, remain subject to human review and approval.

Requirements and cost factors

We need suitable examples, approved information and the access required for the process. Participating systems must offer appropriate interfaces or another viable connection method. We check that feasibility before recommending a specific implementation commitment.

Effort depends on the number and quality of interfaces, data structure, exceptions, approval requirements and support scope. Software and usage charges are identified separately or clearly assigned in the proposal. Actual time savings require evaluation of the complete process, including review and corrections.

Can the process prepare drafts only?

Yes. Limited preparation with human approval is a sensible starting point for many tasks. Additional automation is planned once output quality, ownership and potential consequences are sufficiently understood.

What happens during an outage?

We plan visible error states and an agreed handling route. Depending on the process, that may include retries, notifications or manual handover. The precise behaviour is defined around the actual workflow.

Is this the same as an AI agent?

A workflow usually follows known steps. AI agents can take a more flexible approach where the task requires it. Tool integration addresses connections between existing systems. We choose the approach from the working objective.