AI Consulting for Enterprises

As an extension, not a replacement. Typical divisions of labor: your team holds domain knowledge, data access, and long-term ownership; we contribute delivery capacity, production engineering discipline, and research depth on problems that exceed standard methods — drawing on our state-of-the-art research work. Every engagement includes joint working sessions and code-level handover, so your team's capability grows with each project instead of being displaced by it.

Yes. We design for your target environment from the start — on-premises, private cloud, or your existing hyperscaler tenancy — rather than building on our infrastructure and negotiating migration later. For language-model use cases we deploy private or self-hosted models where required, so sensitive corporate data never leaves your environment; see our custom LLM chatbot project. Security architecture, access control, and network constraints are inputs to the design, not afterthoughts.

As a German company, we build to European data-protection standards by default: data processing agreements, data minimization, and architectures that keep personal data under your control. On the EU AI Act, we help you navigate the practical engineering side — classifying use cases by risk category, and building the documentation, transparency, human-oversight, and logging measures that regulators expect into the system itself. We are engineers and researchers, not a law firm: for formal legal opinions we work alongside your counsel, and our job is to ensure the system they review is defensible.

Enterprise procurement needs defined scope, defined price, and defined outcomes — which is exactly how our staged model works. Each stage (assessment, PoC, MVP, production) is a separately contracted, fixed-price deliverable with acceptance criteria, so it fits standard purchasing workflows without open-ended time-and-materials exposure. We support vendor onboarding, security questionnaires, and DPA reviews as part of engagement setup. Contact us to start with a scoping conversation your procurement team can work from.

By treating production as the requirement, not the sequel. Concretely: every PoC is scoped against its real deployment target — infrastructure, integration points, data access rights, and compliance constraints — before a line of model code is written; stakeholders from IT, security, and the affected business unit are in the loop from discovery, not at handover; and success criteria are operational metrics, not demo impressions. A pilot that cannot articulate its path to production is a pilot we will tell you not to fund.

IP ownership is defined explicitly in the contract before work begins, and for custom-built solutions the standard arrangement is that the client owns the project-specific deliverables — models, code, and documentation created for you. Where a solution builds on pre-existing components or third-party and open-source elements, licensing terms are documented transparently so your legal team can verify the position. For inventions with patent potential, our research and patent experience helps you assess whether and how to protect them.

Production AI is not fire-and-forget: models drift, data changes, and platforms evolve. We agree support and maintenance terms — response expectations, monitoring, retraining cadence, and escalation paths — as part of the production stage, scoped to how critical the system is to your operations. In parallel, structured handover and team enablement shift routine operations to your staff over time, so long-term support focuses on what genuinely requires specialist involvement.

Because the constraint is usually specialization and capacity, not talent. Internal teams carry the reporting, pipeline, and stakeholder load that keeps the business running — which leaves little room for the research-heavy, uncertain work that hard AI use cases require. We absorb that uncertainty: applied R&D, model development, and production hardening, delivered alongside your team and handed over with the knowledge to run it. You get the specialist depth without building a permanent research function.

Pricing depends on scope, data landscape, integration depth, and compliance requirements — but the structure is always staged: a defined assessment, then a fixed-price proof of concept, then MVP and production phases, each with agreed outcomes and an explicit decision point. That gives your governance process evidence at every gate and prevents the open-ended commitments that make AI programs hard to defend internally. Reach out for a scoped proposal based on your use case.

Yes. We operate from Darmstadt and Berlin in Germany and deliver internationally, with remote-first execution and structured communication at every project stage. Our project portfolio spans healthcare, insurance, real estate, and industrial clients across borders. Reach us at info@aisuperior.com or +49 6151 7076909.