What may enter, and what can go wrong
We sample real inputs, including incomplete, contradictory, malformed, duplicate, and sensitive cases. The workflow contract states what it accepts, rejects, quarantines, or sends for review.
AI automation agency
AI Automation for B2B Teams
We connect the systems, define what the workflow may do, route uncertain cases to a person, and instrument the release. You receive a working automation and its written operating envelope.
10 days
Typical window to the first production automation with system access and a test environment
−38%
Median ticket-volume drop after intake automation, measured 60 days after launch
Hard caps
Team-level spending limits set before production traffic
The operating envelope
Automation needs a boundary before it needs a model.
A production automation is a chain of explicit steps: accepted inputs, system access, deterministic rules, any model-assisted decision, allowed actions, human escalation, logs, cost limits, monitoring, rollback, and a person accountable for operation.
A model belongs only where it can handle a defined variable input better than a normal rule or integration. Representative acceptance cases and failure cases are agreed before production. Outputs that fall outside the operating envelope stop, retry safely, or move to a person.
The [NIST AI Risk Management Framework](https://airc.nist.gov/airmf-resources/airmf/5-sec-core/) calls for governance, mapping, measurement, management, pre-deployment testing, and production monitoring. Its [Measure playbook](https://airc.nist.gov/airmf-resources/playbook/measure/) covers overrides, unexpected inputs, alerts, errors, and go/no-go decisions. We turn those questions into the workflow build and handover.
The broader [AI & Automations service](/services/ai-automations) shows the implementation capabilities. The [SME readiness guide](/insights/ai-automation-sme-guide) covers the controls in more detail.
Three production questions
A useful workflow starts with limits the team can approve.
What may the workflow read, change, or send
Permissions follow the narrowest useful scope. Messages, records, money, status changes, and external actions receive separate approval rules, limits, idempotency protection, and audit events.
Who sees failure, and who can stop it
The production design names alerts, logs, cost caps, retry behaviour, manual takeover, rollback, change approval, and the owner who decides whether the workflow remains live.
- 10d Typical window to the first production automation
- With system access and a test environment
- −38% Median ticket-volume drop after intake automation
- Measured 60 days after launch
- Hard caps Team-level spending limits before production traffic
- Cost reporting finance can read
What ships in an automation engagement
A working automation plus its operating record.
Discovery defines one decision unit and its evidence. Scope follows input variation, system interfaces, permissions, data sensitivity, failure exposure, and the acceptance cases the client can provide.
✓
Current-state workflow and data-flow map with the manual baseline and known evidence gaps
✓
Operating envelope: accepted inputs, allowed actions, permissions, model use, escalation, caps, logs, rollback, and owner
✓
Integration and security design for supported interfaces, secrets, data movement, and change authority
✓
Representative acceptance, edge, abuse, and failure cases with pass criteria
✓
Production workflow with deterministic steps, model-assisted steps where justified, and safe fallback paths
✓
Release evidence: test results, access review, cost controls, monitoring, alerts, and go/no-go record
✓
Runbook and handover covering incidents, overrides, change approval, vendor dependencies, and retirement
FAQ
Questions teams ask before an automation engagement.
How much does an AI automation project cost?
Our planning band for one bounded production workflow is €8,000 to €20,000 plus VAT. A model-assisted or multi-system workflow is planned at €20,000 to €40,000. Fixed-price discovery is €3,500 to €5,000 and produces the assumptions, acceptance criteria, and build quote. These are Some Tech Work planning bands; market averages are outside their scope. See the AI automation project cost guide.
Where should a German B2B team start with AI automation?
Choose one recurring workflow with an accountable owner, accessible systems, sample inputs, visible exceptions, and a measurable current baseline. Discovery should define the operating envelope and acceptance cases before selecting tools or adding a model.
Do we need to replace our existing tools to use AI automation?
That depends on supported interfaces, access, security constraints, data quality, licensing, and change authority. We prefer to use stable existing systems when they expose a safe integration boundary. The decision memo records any replacement, connector, or manual-boundary trade-off.
How do you decide what to automate?
We compare current effort and failure exposure with input variation, integration feasibility, permissions, exception rate, reversibility, and the value of the outcome. A deterministic integration may solve the problem. Model assistance enters only for a defined step with testable acceptance criteria.
What does a typical AI automation engagement look like?
It moves through discovery, operating-envelope approval, build, representative testing, production release, observation, and handover. Schedule is scoped after the systems, permissions, input variation, test environment, reviewers, and risk boundary are known. The approved ten-day evidence on this page applies only to a first production automation with system access and a test environment.
Can you connect AI tools to legacy systems?
Sometimes. Feasibility depends on a supported interface or controlled access path, authentication, data shape, transaction safety, vendor restrictions, testability, and authority to change the system. Where direct integration is unsafe, the recommendation may be a staged export/import boundary, a middleware layer, a system change, or no automation.
How do you measure ROI on an AI automation project?
The baseline and decision metric are specific to the workflow: handling time, accepted output, rework, exception volume, cycle time, cost per completed case, or another observable unit. We also track overrides, failures, model and vendor cost, and downstream effects. One approved example showed a 38% median ticket-volume reduction 60 days after intake automation; that prior result is evidence of one engagement rather than a forecast.
What is the difference between AI automation and RPA (robotic process automation)?
The useful design choice happens at step level. APIs, workflow rules, scripts, and RPA can execute deterministic work. A model can assist with a bounded variable-input task such as classification or extraction. The workflow may combine both, with explicit confidence or validation rules and human review for cases outside the approved envelope.
Is AI automation GDPR-compliant when using LLMs?
Compliance cannot be concluded from the presence of an LLM, EU hosting, a DPA, or a short control list. We document purpose, roles, data categories, sources, recipients, transfers, retention, rights, security, model and vendor behaviour, and any automated effects. The client’s privacy or legal owner confirms the lawful basis and required assessments. The EDPB AI-model opinion describes case-by-case analysis for anonymity, legitimate interest, necessity, expectations, and rights. AI Act classification and duties also depend on the system and use; see the European Commission overview.
Where to go next
Automation is one layer. Here is the full picture.
AI automation sits inside our AI & Automations service, which covers workflow automation, CRM and API integrations, LLM deployment inside existing tools, data pipelines, and legacy system connectors.
If your website is a surface in the automation workflow, such as lead capture forms, intake routing, or content publishing, see how automation connects to the Website & Relaunch service.
For marketing-side automation, lifecycle email, lead scoring, and CRM workflows, see Lifecycle Email Automation.
If you are deciding whether to build internal tooling or use an existing platform, our Tech Strategy team runs that scoping before an automation build starts.
Local delivery context: tech consulting Cologne, tech consulting Düsseldorf, and WordPress agency Frankfurt.
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