LLM consulting: independent strategy, no vendor bias
You need to decide which model, which architecture, and which budget fit your use case — and every vendor gives you a different answer. Our LLM consulting is vendor-neutral: we assess OpenAI, Anthropic, and open-source models (via Ollama) against your requirements, not against a commission. You walk away with a clear recommendation you can execute with us or on your own.
What our LLM consulting delivers
We answer the questions that decide cost and outcome: which model fits your use case, whether to build or buy, and whether RAG or fine-tuning is the right tool for your problem. You get a documented architecture recommendation, a defensible cost-per-request estimate, and a prioritized implementation plan. Not a deck for its own sake — decisions an engineering team can build against.
How we work
We start with a compact assessment of your data, requirements, and compliance situation. Where it helps, we build a small prototype in Python and FastAPI early, so we test assumptions against real data instead of guessing on slides. You see progress every week — transparent, with no surprises at the end.
The technology stack
For orchestration and retrieval we use Langchain and LlamaIndex; for APIs, Python and FastAPI. On models we stay deliberately open: OpenAI and Anthropic for managed APIs, Ollama and open-source models where data sovereignty or cost make the case. That keeps you free of vendor lock-in and able to switch models later.
Why Olio
Olio is a boutique agency with senior-level engineering — you talk to people who have shipped LLM systems to production, not to a sales team. We are tied to no cloud or model vendor, so we cost out the trade-offs, risks, and EU AI Act obligations openly. And we build for handoff: documented, tested, and maintainable, so your team can carry it forward.
What you take away from the engagement
- A vendor-neutral model recommendation — OpenAI, Anthropic, or open source, justified against your use case
- A clear build-vs-buy decision with a defensible cost-per-request estimate
- A grounded answer on RAG vs fine-tuning, instead of expensive detours
- Concrete cost-control levers: model routing, caching, and batching
- EU AI Act and GDPR classification, including EU-sovereign hosting options
- A prioritized implementation plan your team can pick up directly
Core Technologies
Let's clarify your LLM strategy
LLM consulting: common questions
What does LLM consulting cost?
A focused assessment with a model recommendation and implementation plan sits in a modest project range — and costs far less than the wrong model choice. For larger work we quote transparently; for reference, a simple MVP runs EUR 25,000–40,000 and a full SaaS MVP with AI EUR 50,000–120,000. You get a concrete proposal after the first call.
How long does it take?
A focused assessment usually delivers a recommendation within one to two weeks. If a prototype needs to validate assumptions against real data, we plan a few additional weeks — we work lean and build what actually de-risks the decision first.
What does the process look like?
We begin with your data, goals, and compliance requirements, build a small prototype in Python and FastAPI where useful, and show progress weekly. You end with a documented recommendation and implementation plan you can execute with or without us.
Which models and technologies do you recommend?
It depends on the use case. We weigh OpenAI and Anthropic for managed APIs, plus open-source models via Ollama when data sovereignty or cost make the case. For retrieval and orchestration we use Langchain and LlamaIndex — vendor-neutral, with no lock-in.
Can you host in the EU, GDPR-compliant?
Yes. We host in EU regions — Hetzner, GCP EU regions, or AWS Frankfurt — and run open-source models when data cannot leave the country. During the engagement we map the EU AI Act and GDPR requirements concretely to your use case.
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