From idea to production-ready solution — structured, honest, no hype.
In short: I help Swiss companies make honest decisions about AI — and then deliver in production. That covers structured discovery workshops, 2-week PoC sprints, independent architecture reviews before large investments, and concrete vendor and hosting strategies for Swiss FADP/EU compliance. Four concrete offers, each with a clear deliverable, a clear timeframe, and a written outcome. No default retainer model. I say no when AI is the wrong answer for the question.
Clearly scoped, individually bookable, with a written deliverable.
Discovery workshops with stakeholders, mapping of existing data and processes, identification of 3–5 prioritised use cases with rough effort/value estimates. Deliverable: a written report with ranking, risks, and next steps — not a slide show, but a decision document you can keep working with even without me.
One clearly scoped use case, built end-to-end: data wired in, model chosen, eval setup, runnable prototype. Includes handover documentation. The goal is not a demo but an honest verdict on whether the use case is viable in production with your current data — and what a production implementation would realistically cost.
Independent review of an existing AI system or a planned design before a large investment. Tradeoffs, risks, blind spots, concrete recommendations. Deliverable: a written review with prioritised findings. Typical triggers: before a vendor decision, before a go-live, or when a running project isn't delivering the expected results.
Which LLM vendors fit your compliance and cost requirements? Which hosting (Azure Switzerland, AWS Bedrock EU, on-premises)? Which guardrails are needed before going to production? Deliverable: a written decision record with reasoning — so an audit or a successor can later understand why this choice was made.
I say no when AI is wrong here. Not every use case benefits from an LLM. A clean SQL query, a classical rules engine, or a simple workflow is the right answer in many cases. If that surfaces in the readiness assessment or in the review, I say so openly — even when it means a smaller follow-on engagement. An honest no is worth more in the long run than a well-packaged maybe.
Honest effort estimates, not aspirational ones. Estimates are calculated against what I have actually built — not against what looked possible in a demo video. If a PoC sprint needs two weeks, I say two weeks. If data quality has to be resolved first, I say that too — and don't push it into a follow-on contract.
No default retainer model. Every offer above has a clear deliverable and a clear endpoint. If ongoing support makes sense afterwards, a retainer can be added — but only if you want one, not because it's bundled into the default package. I don't manufacture vendor lock-in.
Written deliverables, not slideware. Every engagement ends with a document you can keep and reuse: report, decision record, architecture review, handover docs. So you can keep working without me — and so whoever comes after me finds a real foundation rather than a vibe.
Flexible, transparent, and matched to the consulting need.
Fixed price for a clearly scoped deliverable — readiness assessment, PoC sprint, architecture review. Ideal when scope and timeframe are clear.
Flexible billing by effort. Fits exploratory discovery, ongoing accompaniment, or sparring sessions on concrete decisions.
Monthly allocation for continued accompaniment after a project — only on request, not by default. Clear hours, clear notice period.
All prices are calculated individually based on scope and complexity. Concrete numbers come after a non-binding discovery call.
Request a quoteWhen you know AI has to become a topic, but it's still unclear where the first concrete step lies. Typical triggers: a management mandate ("we need an AI strategy"), a vendor pitch you can't quite place, or a list of ten possible use cases without prioritisation. The readiness assessment delivers in 2–3 days a prioritised ranking of the 3–5 most promising cases, sorted honestly by effort and value — and says clearly which use cases are not a fit for AI. It isn't useful when the use case is already clear; in that case start directly with a PoC sprint.
A tech demo shows that something works — usually on the happy path, with curated examples, no eval. A PoC sprint builds the use case end-to-end against your data, with an evaluation setup and honest error analysis. The deliverable is not just a running prototype but a written verdict: does the use case work in production with the current data? Where do the real limitations sit? What would a production implementation cost? The sprint is explicitly designed so that a clear "no, not worth it" is a valid result — and even then your money is well spent.
Yes. AI Readiness Assessment, Architecture Review, and Vendor/Hosting/Governance Strategy are pure consulting engagements — the deliverable is always a written document, not code. You can book these offers even when you plan to implement internally or with another partner. That is explicitly part of the model: no vendor lock-in, no hidden pull towards "and now we also have to build it". If you want to do a PoC with me after the consulting, gladly — if not, equally gladly.
An architecture review is calculated project-based and typically sits between CHF 5,000 and CHF 15,000, depending on scope and complexity: a single RAG setup or a planned design lands at the lower end, a running multi-agent system with several data sources and integrations at the upper end. The deliverable is a written review with prioritised findings, tradeoff discussion, and concrete recommendations — not a generic best-practice PDF. Precise quotes after a non-binding discovery call in which we scope the review together.
Honest answer: ROI on AI projects can almost never be calculated seriously before the PoC — anyone showing a spreadsheet with five decimal places is selling storytelling. What can be measured: time saved per case (stopwatch, before/after), error rate against the human baseline, throughput time, volume per employee. My approach: before the PoC, fix the two or three metrics that matter; during the PoC, measure the real numbers; afterwards, calculate honestly. If the ROI doesn't hold, that is itself a result — and a better one than keeping an AI project alive for two years because nobody is measuring.
Classify inbound, draft replies, route to CRM — with approval queue.
Offers from CRM + catalog + tone-of-voice profile as draft — sales rep does the final polish.
Transcribe meetings, extract decisions and action items, sync to project tools.
Book a non-binding 30-minute discovery call. We figure out whether a readiness assessment, a PoC sprint, an architecture review, or a vendor strategy is the right entry point — and what effort it realistically involves.
Book a discovery call