AI, automation, SEO and GEO: notes from the field.
What we learn automating processes, shipping products and making them visible on Google and inside AI answer engines. Methods, numbers and the mistakes to avoid.

Wealth advisors: track open files and chase missing documents
A wealth advisor carries dozens of files at different stages. Here is what can run alone in the follow-up and reminders, what stays your decision, and how to size the gain.

Targeted alerts: only hear about what leaves the ordinary
Too many alerts are worse than none. How to choose what deserves a message, set thresholds, what AI adds, and when to install nothing at all.

Wealth manager: from first meeting to a complete client file
Between the first meeting and a complete file, chasing and filing take weeks. Here is what can run on its own, what stays your decision, and how to put a number on the gain.

Summarising Customer Service Calls: One Week of Listening
Nobody replays five days of calls. Here is what an automatic summary surfaces in one week, the six-step flow, its limits, and when not to install it.

Daily summary: replace ten browser tabs with one morning message
Touring your tools takes time and gets skipped on busy days. See what a good morning summary contains, how to choose its figures and when to skip it altogether.

Wealth manager: prepare client reports without losing evenings
A client report takes little time alone and a lot once multiplied. See what can be prepared automatically, what stays your commentary, and how to measure the gain.

Site reports dictated by voice: from voice memo to document
A report dictated on site replaces evening typing: the AI transcribes and structures, the person reviews and signs. Steps, limits, an error table and a test before you start.

AI-generated quotes and contracts: the safeguards to set
An AI assembles a quote or contract from your templates; it decides neither the price nor the clauses. Here are seven safeguards, a table of roles and a test to run before launch.

Accounting firms: get through the close without data-entry overtime
The close crushes teams because everything arrives at once. Here is the four-phase calendar, what AI prepares, and when this is the wrong choice.

Supplier invoice entry: what AI reads well, and badly
An AI reads well what is printed plainly and badly what needs context. Here is where it is reliable, five rules for deciding who has the last word, and how to test on your own invoices.

Reactivating Old Clients When Your CRM Has Been Asleep for Two Years
A CRM asleep for two years hides deals. Here is where to start: sort first, three messages, a four-step calendar, and what an AI prepares while you approve.

Accounting firms: collect client documents without chasing ten times
Chasing a client ten times for one invoice is not inevitable. Here is the collection loop, the reminder cadence and the point where a person takes over.

Accounting firms: the tasks that really automate
Five tasks weigh on an accounting firm’s time. Here is which ones an AI handles reliably, which need approval, and which to start with.

Answering an Inbound Enquiry Fast, Even in the Evening
An enquiry left waiting goes cold. Here are the published orders of magnitude, a five-step workflow to reply the same day, and the part an AI prepares without deciding for you.

Following Up on a Quote Without Nagging: The Rhythm That Works
An unanswered quote is almost never a refusal. Here is the follow-up rhythm that holds, the structure of a useful message, and the part an AI drafts while you approve.

Swiss FADP and AI: what to check before sending data to an LLM
Sending customer data to an LLM engages your responsibility under the Swiss FADP. Three data levels, five questions for the vendor and a checklist before going live.

What AI Does Not Speed Up in a Software Project
AI speeds up writing code, not the rest of a project. Six steps that do not compress, what serious measurements say, and how to build an honest schedule.

Why Your AI Automations Fail on Real-World Data
An AI automation that shines in a demo degrades on your real data. Six break points, a four-step diagnosis method and the fixes to apply, in order.

Fixed price or hourly billing: why we never bill by the hour
Time and materials or fixed price: who carries the risk, who benefits from going fast, and when a fixed price is a mistake. A comparison, a scoping method and five questions to ask before signing.

Choosing an AI Model for Your Business: The Criteria That Matter
Model leaderboards shift every six weeks. Here are the six criteria and the five-step method we use to choose an AI model that survives production, not just a demo.

AI Coding Assistants: What Changes in Code Review
AI-generated code is almost always well formatted, which disarms a reviewer's usual instincts. Seven failure modes to know, and a method for calibrating review depth by risk.

Testing an AI System Before the Demo: A 5-Step Method
A successful demo proves nothing about how an AI system really behaves. Here is how to build a systematic evaluation before presenting it to a client or shipping it to production.

2026 Tech Stack for a Swiss MVP: The Choices and Their Cost
Frontend, database, authentication, hosting: the most boring stack possible is usually the right one for an MVP. Our choices, the alternatives we skipped, and what each one actually costs in 2026.

The Real Cost of an AI Feature in Production
The API price tag is almost never the real cost. Four cost layers make up an AI feature's production bill, and one of them usually outweighs the other three combined.

GEO for a Swiss SME: the first five fixes
A diagnostic method, five concrete GEO fixes ranked by impact and timeline, and when this work is not the priority for a Swiss SME.

What an AI Says When You Search for an Accounting Firm in Geneva
A firm can rank first on Google and be absent from a ChatGPT answer about Geneva accounting firms, or the opposite. What comparable-market studies show, and how to test your own visibility.

How to Write a Page AI Engines Actually Cite
A page can rank well on Google and still stay invisible in ChatGPT or AI Overviews if none of its paragraphs can stand alone. Structure, factual density and a six-step method for writing citable pages.

AI Overviews: Why Your Traffic Drops While Rankings Hold
Stable rankings, falling clicks. That is not a tracking bug or a penalty. How to diagnose the AI Overviews decoupling, and what actually works next.

GEO vs SEO: What Changes and What Does Not
SEO ranks you inside a list of results, GEO gets you quoted inside an answer already written. What changes between the two disciplines, what stays the same, and seven steps to align them without a rebuild.

How ChatGPT Chooses Its Sources: What We Actually Know
Ranking well on Google is not enough. Here is how ChatGPT sorts your questions, fetches its sources, and, more often than not, ends up citing no one at all.

Server-Side Rendering and AI Crawlers: The JavaScript Trap
Google executes your JavaScript, ChatGPT does not. GPTBot, ClaudeBot, and PerplexityBot only read the raw HTML the server returns: JavaScript content can stay invisible to them, even while ranking well on Google. How to check and fix it.

Measuring AI Search Visibility: A Practical Tracking Method
Ranking third on Google says nothing about what ChatGPT tells a prospect. Here is the method and the sheet to track your AI visibility, month after month.

SEO in the Architecture: Building an MVP That Gets Found
SSR or CSR, URL structure, internal linking, AI crawlers: the architecture decisions that decide whether an MVP is ever found, on Google and inside answer engines, and when it is better not to worry about this yet.

RAG in 2026: What Still Holds and What Got Replaced
Long context, agents, cost math: in 2026 the RAG versus context window debate is framed wrong. What actually got replaced, what still holds, and the method for choosing right on an SME project.

Which Schema.org Types Actually Matter for AI Answer Engines
Rich results and AI citation follow different rules. The decision table for which Schema.org types matter to ChatGPT, Perplexity and Claude, with a JSON-LD example and a five-step method.

AI Agent in Production: What Breaks First
An AI agent that passes every test can still break in production for five specific reasons, almost never tied to the model itself. The checklist to catch them before launch.

GPTBot, ClaudeBot, PerplexityBot: Block or Allow?
GPTBot protects training, OAI-SearchBot feeds ChatGPT answers: mixing them up loses visibility without gaining protection. Here is how to decide, crawler by crawler, with a working robots.txt configuration.

llms.txt: a practical guide and its real limits in 2026
A well made llms.txt takes an hour to write. Here is how to structure one, a real example, and the 2026 data that honestly shows what it changes and what it does not.

GEO: getting cited by ChatGPT, Perplexity and AI Overviews
A share of searches no longer ends in a click. Here is how answer engines pick their sources, the six levers worth pulling, and how to measure progress without paid tooling.

Forward Deployed Engineer: the engineer who replaces the consultant
An engineer who starts from the problem rather than the ticket, and ships software to production rather than a recommendation. What the role covers, and when it is the wrong choice.

Shipping a product in 90 days: what AI changes, and what it does not
The figure sounds impressive, so it deserves taking apart. What AI genuinely accelerates in a project, what it leaves untouched, and the three client-side conditions for holding the pace.