AI app consultant and AI app developer in one.
You want to build an AI application that mirrors your actual business process — not a generic chat UI. I advise, design, and build: web apps, mobile apps, and AI agents that run reliably in production.
Why a specialized AI app almost always beats handing out ChatGPT seats
A generic ChatGPT seat for an entire 100-person workforce easily costs €25,000 per year — and delivers measurably little because nobody actually integrates it into daily work. A specialized AI app that mirrors a concrete workflow tends to cost similar in year one and deliver multiples in impact.
As an AI app consultant I am both: the one who decides architecture with you, and the one who actually builds the app. One point of contact — no handoffs between consulting and delivery, no 'that wasn't in the concept' arguments.
Typical apps I've built for mid-market clients: search assistants over internal document corpora (RAG), workflow apps with AI suggestions plus human confirmation, mobile apps for field teams, AI agents that take over recurring office tasks. Always with a clear owner, a measurable KPI, and a roadmap that grows with usage.
Types of AI apps I build
Web, mobile, or backend agent — the use case picks the stack, not the other way round.
- 01
Search and knowledge apps (RAG)
Web apps that make your documents, tickets, emails searchable. With source attribution, permission filters, multilingual search.
- 02
Workflow apps with AI augmentation
Apps that mirror an existing workflow and surface AI suggestions at decisive points. Humans decide, AI takes load off.
- 03
Mobile apps for field and service teams
iOS and Android apps with offline capability, voice input, AI-supported documentation. Inspired by what I'm building for Valiro.
- 04
AI agents for internal office tasks
Agents that take over recurring tasks — email triage, scheduling proposals, data prep. With eval gates and monitoring.
- 05
Embedded AI features in existing products
If you have a SaaS product and want to add AI features, I take architecture, build, and stack integration.
- 06
API-based AI backends
Sometimes you need no frontend, just an AI backend that supplies your existing application with intelligence. I build those too.
Typical outcomes from AI app projects
- 8–14 weeks
from kickoff to a productive app used by real users
- 30–80%
efficiency gain per addressed workflow in year one
- 1 person
one point of contact for consulting, architecture, build, operations
How an AI app build with me runs
- 01
Discovery — 1 week
We sharpen the use case, define success metrics, identify critical technical risks. Outcome: clear target image and architecture sketch.
- 02
Design sprint — 1 week
UX design of the core flows, clickable prototype for stakeholder validation. Usually clarifies a lot about what the app actually needs to do — and not do.
- 03
Build — 4–10 weeks
Iterative development with weekly demos, pilot tests with real users from week three or four. Eval suite and monitoring from day one.
- 04
Pilot operation — 2–4 weeks
Real operation with a small user group. Feedback feeds into tuning, weak spots get hardened. This is where we decide whether the app scales.
- 05
Rollout & handover or co-operations
Full rollout, training, documentation. Either handover to your team or continued retainer for joint evolution.
FAQ — AI app development
What technical stack do you use?
Typically: TypeScript / Node or Python on backend, React / Next.js on frontend, native Swift / Kotlin or React Native for mobile, Postgres with vector extension for data. Stack decisions follow the use case and your existing infrastructure.
What does an AI app cost?
A focused web app with RAG search typically lands at €35,000–80,000 as a pilot implementation. Mobile and multi-tenant apps sit above. You get a fixed price or clear T&M estimate before kickoff.
Can you work with our existing frontend team?
Yes. If your team builds the frontend, I take architecture and AI backend. If you have an existing app and want AI features added, I integrate into your stack.
Who hosts the app?
Usually you — we deploy in your cloud (Azure, AWS, GCP) or on-premise. Code, data, and models stay under your control. In rare cases I offer hosting, but that's not the default.
What happens to the code after the project?
The code is yours. Full repo handover, clean docs, onboarding for your dev team — or we move to a retainer if you don't want to continue internally.
Can you only consult, without building?
Yes. Architecture reviews, stack selection, code audits of existing AI apps — a few days per engagement. But: most clients end up wanting both from one source.
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Let's talk for 30 minutes.
I listen, ask questions, and tell you honestly whether and how I can help.