Search engine optimization: ranking in Google and Bing results. Pages search engines can crawl and index. Fast pages that pass Core Web Vitals. Content that answers what people search. Structured data, internal links and backlinks. Measured in Google Search Console and Semrush.

Generative engine optimization: being mentioned and cited when people ask ChatGPT, Gemini, Perplexity or Google's AI Overviews. Clear, consistent facts about your brand everywhere. Answer-first pages that are easy to quote. Listings and reviews on the portals AI reads. Deliberate crawl rules for AI bots. Measured as your share of AI answers, with Semrush and Peec AI.

How do you work on SEO and GEO?

Five steps, measured from day one, so every change can prove what it did. Baseline in Google and in AI answers. Prompt research per page with an open model such as Qwen, run privately. Technical and entity fixes: crawlability, structured data, facts. Citable content, listings and reviews, in order of impact. Monthly tracking and experiments in sprints.

Use cases

The situations where I make the biggest difference, and what changes after.

Cost & cloud

Your cloud bill keeps growing

Cost baseline per resource, right-sizing on real peak load, reservations and budgets matched to the new footprint.

−48% total cloud costs in five weeks, nothing customers notice

See example Official guide: FinOps Foundation: FinOps Framework

Kubernetes is oversized or fragile

Right-sizing on p95 load, fewer nodes, GitOps with FluxCD and Helm, autoscaling and clean upgrades.

Smaller cluster, calmer on-call

See example Official guide: Kubernetes docs: resource management for pods

You need cloud and on-prem together

Hybrid Kubernetes across bare metal and AWS, Windows workloads as VMs on KubeVirt, air-gapped where needed.

One platform, two locations

See example Official guide: Microsoft Cloud Adoption Framework

A regulated workload moves to Azure

Terraform from day one, Azure Policy and Blueprints across teams, Key Vault, Defender, Azure Monitor and audit-ready release processes.

Cloud costs about 25% lower in automotive

Official guide: Azure Well-Architected Framework

AI

Customers ask ChatGPT instead of Google

AI-visibility baseline with Semrush and Peec AI, prompt research per landing page with an open model, then structured data, citable pages and listings in order of impact.

Found where buyers ask now

See example Official guide: Google Search Central: AI features and your website

Your AI-built prototype has to go live

Your Lovable, Bolt or v0 app gets staging, tests, row-level security, backups and a release pipeline, without a rewrite from scratch.

Production-grade and safe to change

See example Official guide: AWS Well-Architected: Generative AI Lens

Your team answers the same guest questions all day

A WhatsApp AI agent answers from a knowledge base that grows with every staff reply. New knowledge goes live only after manager approval, and unanswered messages escalate.

Fast replies, your team stays in control

See example Official guide: OWASP Top 10 for LLM applications

You want to ship with AI coding agents, safely

An AI-assisted delivery lifecycle: written guardrails, every change through staging and a real-browser check, an impact analysis before production. Auth, payments and data deletion always need a human.

3,025 merged pull requests in ten months

See example Official guide: DORA: software delivery research

You want Claude or ChatGPT to work with your data

MCP servers and APIs for AI assistants: read-only where possible, keys hashed, personal data left out, and each user's own permissions when they act.

AI tools that respect your access rules

See example Official guide: OWASP Top 10 for LLM applications

Your phone rings while the team is busy

A voice AI agent takes the call, books appointments, answers the usual questions from your knowledge base and hands over to a person when needed.

No missed calls, no missed bookings

Your data is too sensitive for cloud AI

Local open models on your own hardware, personal data anonymized, retrieval on your own documents. At most an aggregate ever leaves the building.

The benefits of AI without the data risk

Official guide: Azure Well-Architected: AI workloads

Ship & run

Releases are manual and risky

Pipelines with checks before merge, smoke tests after every deploy, signed mobile releases and a quick way back.

Release cycles about 40% faster at a major bank

Official guide: DORA: software delivery research

You hear about outages from customers

Outside-in checks on vital flows, watchdogs on business outcomes, alerts only on state changes, backups restored every night.

You know first

See example Official guide: Google SRE book: monitoring distributed systems

Your legacy system holds the business back

AI-assisted analysis of the old code, a migration plan in steps, a parallel run and a cut-over with identical row counts and checksums.

Off the old stack without losing data

See example Official guide: Microsoft CAF: migrate to Azure (incl. lift and shift)

Two systems that should agree, don't

Serverless middleware on Azure Functions with queues and retries that keeps ERP and CRM in sync in both directions while both stay in daily use.

Both systems stay usable during the move

Tests pass, users still hit bugs

AI browser testing in a real Chrome: describe the flow in plain language, get pass or fail with errors and a fix prompt.

Bugs caught before users see them

See example

SEO and GEO, explained

Search now has two front doors: the classic results and the answers AI assistants give. You want to be found behind both.

Search engine optimization: ranking in Google and Bing results.

1Pages search engines can crawl and index
2Fast pages that pass Core Web Vitals
3Content that answers what people search
4Structured data, internal links and backlinks
5Measured in Google Search Console and Semrush

Tools I use

Semrush
Peec AI
Google Search Console
Qwen (open model)
Schema.org

Official guidance

Book a 30-minute GEO check Download the GEO Adaptation Framework (PDF)See the case

AI use cases by department

Where AI saves time in a typical company, from a ten-person startup to a large enterprise. Each one starts small and grows, with local models where the data is sensitive.

Sales

Lead scoring and enrichment
Reply drafts and follow-ups
Call notes straight into the CRM

Customer service

WhatsApp, chat and phone agents
Ticket triage and routing
Answer drafts with human approval

Operations

Invoices, delivery notes and forms read into your systems
Planning and scheduling assistants
Reports written from live data

Finance

Invoice matching and anomaly alerts
Cloud and vendor cost reports with forecasts
Expense categorisation

HR & recruiting

Onboarding assistant for new hires
Answers on internal policies
CV pre-screening on clear criteria, people decide

Marketing

Content calendar and drafts in your brand voice
SEO and AI-search content audits
Campaign reporting

IT & engineering

AI coding agents with guardrails
AI browser testing before every release
Legacy code analysis and incident summaries

Management

Weekly KPI digest
Assistants that answer from your own data
Decision memos built from the numbers

Which of these would save your team the most time? A 30-minute call is enough to find your first three.

Book a 30-minute call

Keep exploringWho you'd work with: experience and certificates

Get in touch

Tell me what you're building. I'll reply within 1–2 business days.

Video call: Book a 30-minute call Free slots: see the calendar
Email: mail@dominikheinrich.ch
Company: MADO Consulting
LinkedIn: Follow me on LinkedIn

Available worldwide, remote. On-site on request, depending on the country.

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