AI Consulting for Startups

They solve different problems. A founding ML engineer makes sense once you have continuous ML workload, a proven product, and the funding to compete for senior talent — a search that routinely takes months and founding-level compensation.

Before that point, a consultancy gets you a full senior team — data scientists, ML engineers, architects — productive in week one, for the finite job of validating and shipping the product. Many clients do both in sequence: we build the PoC and MVP, and when the round closes, we hand over clean code and architecture to their first in-house hire. Nothing about working with us forecloses hiring later; it usually makes the hire easier, because there is a real system to hire into.

A scoped proof of concept typically takes weeks, not months — our first milestone is a working prototype on real data that answers your riskiest technical question. That speed is a design goal: the PoC exists so you have something concrete to show users, your board, and prospective investors while your runway is still long. From there, an MVP in users’ hands is typically a matter of a few additional months, depending on integrations and scope.

You own what we build for you. Deliverables developed under the engagement — code, models trained on your data, documentation — are contractually yours, which is exactly what technical due diligence will check. We build with handover in mind: standard tooling, documented architecture, no proprietary runtime you are locked into. Your future in-house team inherits a system, not a dependency on us.

Yes — meeting your stack where it is comes with the job. We work across the mainstream ML and cloud ecosystems and integrate with the languages, frameworks, and infrastructure your product already runs on, rather than imposing a parallel stack your team cannot maintain. Where your architecture has a genuine problem — one that will hurt at scale or in diligence — we will tell you directly and propose the smallest change that fixes it.

That is a decision point, deliberately. Some clients continue with us into the production stage — hardening the MVP with data pipelines, monitoring, and MLOps as usage grows. Others use their new funding to hire in-house and take over, with our support during handover. Others pause, gather user feedback, and return for the next iteration. Because every stage of our startup packages is a separate fixed-price engagement, there is an off-ramp at each one — you are never contractually carried past the point the evidence supports.

No — we work fee-based, at fixed prices per stage. That is a deliberate choice: it keeps our advice honest (we have no incentive to tell you your idea is more feasible than it is), it keeps your cap table clean for the investors who fund your growth, and it makes every stage a clear, bounded budgeting decision rather than an open-ended dilution. Fixed price per stage is usually the more founder-friendly economics anyway: you know the cost of the PoC before it starts.

By treating LLMs as one component in an engineered system, not the system itself. In practice that means retrieval over your own data so answers are grounded and your data becomes a moat; systematic evaluation so quality is measured, not vibes; cost and latency engineering so unit economics survive scale; and custom or fine-tuned models where they genuinely outperform an API call. Our team’s background is machine learning research and production engineering — see our project portfolio — which is exactly the depth a thin wrapper lacks and a technical investor looks for.

Yes. Systems we build come with the artifacts diligence teams ask for: documented architecture, reproducible training and evaluation results, clear data provenance, and honest statements of what the models can and cannot do. If you already have a system, we can review it before investors do — identifying the gaps between what the deck claims and what the codebase supports, while there is still time to close them.

In fixed-price stages with a defined outcome — the model behind our packages for startups. The exact figure depends on the complexity of the problem, the state of your data, and the integrations required, so we scope it in a free initial call. What we do not do is open-ended time-and-materials billing at the stage where your budget certainty matters most. Contact us for a quote scoped to your product.

It has to be — and as a German company, we hold ourselves to European data-protection standards (GDPR) by default, for every client worldwide. Engagements run under confidentiality agreements, with data processing agreements and architectures where your data stays under your control. For LLM products we can deploy private, hosted models so your knowledge base never leaves your environment — see our custom LLM chatbot case study.

Yes. We’re headquartered in Darmstadt in the Frankfurt Rhine-Main region, with a second office in Berlin, and work with founders internationally. Projects run remotely with structured communication at every stage — from feasibility through deployment and handover — so time zones have never been a barrier to a shipped product. Reach us at info@aisuperior.com or +49 6151 7076909.