Generative AI Consulting Services for Enterprises

Generative AI consulting helps an organization decide where generative AI is worth using, which architecture and models fit, and how to govern it safely.

It usually covers use-case selection, data readiness, architecture and model choices, governance and a roadmap from pilot to production. Supertype has delivered 30+ enterprise AI deployments since 2016, so the advice comes from systems we have had to run, not only design.

Consulting decides what to build, how and under what controls. Development builds it: the RAG pipelines, chatbots, agents, evaluation and monitoring.

Supertype does both, so the plan we recommend is one we know how to deliver. If you already know what you want built, our LLM development service is the place to start. If you are still weighing whether to build, buy or bring in a partner, the build vs buy vs partner guide lays out the trade-offs.

We start with discovery workshops to understand your goals and processes, rank use cases by value and feasibility, review data and technical readiness, design the target architecture and governance, and run a pilot on the top use case.

Then we either hand over to your team or build it to production with you. The first steps follow the same approach as our AI Opportunity Discovery, which is how most engagements begin.

Yes. We review stalled or underperforming pilots, covering architecture, retrieval quality, evaluation, security and running cost, and set out what it takes to reach production.

Retrieval quality is a common cause, and it is the focus of our RAG development work.

No. We are model-agnostic and work with OpenAI, Anthropic, Google and open-source models.

We test them on your own data and recommend based on accuracy, cost, latency and where your data is allowed to go. The right answer often changes between use cases inside the same company, so we do not pick one model up front.

We design for your governance requirements from the start: access control that follows your existing permissions, data residency, deployment in your cloud or on-premise, audit logs, and evaluation and human review where answers carry risk.

Where data cannot leave your network, we can run open-source models inside your own infrastructure.

Yes. Most of the value comes from connecting models to your documents, databases and business systems such as CRMs, ERPs and data warehouses. Integration is part of the architecture we design.

Depending on the use case, that might be a chatbot over your internal knowledge or an AI agent that takes actions in your systems.

It depends mostly on how ready your data is, how many systems are involved and how strict the security review is.

We start with a focused pilot on one use case so you see results early, and agree the full plan with you during discovery.

Effort depends on the number of use cases, data complexity, the systems involved and compliance requirements.

We scope these in an initial discovery and give you a fixed proposal before any larger engagement. If you want to compare against hiring your own team, the in-house vs consultancy section of our guide covers where each option costs more.

Most clients start with our AI Opportunity Discovery, a short engagement that ranks your AI opportunities and builds a proof-of-concept for the top one.

You can also book a meeting below to discuss a specific project.