Generative AI development services: LLM apps, RAG and agents that hold up in production.
We build generative AI solutions on OpenAI, Claude, Gemini and open-weight models — LLM applications, retrieval-augmented generation (RAG) over your documents, ChatGPT-style assistants wired into your systems, and AI agents — with evals, guardrails and a cost ceiling from the first week. Generative AI consulting is part of the job: we'll also tell you where it doesn't fit.
What are generative AI development services?
Generative AI development services design, build and run software that uses large language models (LLMs) and other generative models to draft, summarise, extract, classify and answer questions — integrated into the tools where your team already works. The deliverable isn't a prompt or a demo: it's an application with a data pipeline, retrieval over your content, evaluation, guardrails, cost controls and monitoring, running in your environment.
We're model-agnostic. OpenAI, Anthropic's Claude, Google's Gemini or an open-weight model served on your own infrastructure — chosen per task on cost, latency, quality and where your data is allowed to go, with the trade-off written down against your data instead of taken from a vendor default.
Generative AI solutions we build
LLM features and copilots inside your product; RAG assistants that answer from your documents and cite the source; document and email processing that turns unstructured text into structured data; ChatGPT and OpenAI API integration into CRMs, ERPs and internal tools; and AI agents that act across systems, with a person approving the risky steps. Content generation is the smallest part of it — the work that pays back is usually operational.
Generative AI consulting: where it fits and where it doesn't
Generative AI consulting with us is engineering-led: a senior engineer reviews the workflow, the data and the risk, and gives a written recommendation — build, buy, use a tool you already pay for, or don't use generative AI at all. It fits language-heavy work that tolerates an occasional error and has a person in the loop. It fits badly where the answer must be exact every time — totals, tax, fiscal data — unless a deterministic check sits behind it.
RAG, fine-tuning or prompt engineering?
Start with prompt engineering and structured outputs. Add retrieval-augmented generation (RAG) when the model needs private, changing knowledge — product docs, contracts, policies — and must cite where an answer came from. Fine-tune only when you need a consistent format or tone, or a smaller, cheaper model to match a larger one on a narrow task. For most business cases, prompts plus RAG are enough; fine-tuning is the exception.
LLM development services: our generative AI stack
Models from OpenAI, Anthropic and Google, plus open-weight models from Hugging Face when data has to stay on your servers. Services in Python and TypeScript, retrieval over a vector index, prompts and model versions in version control, evals in CI on a labelled test set, and cost ceilings enforced in code. Deployed on AWS, Azure or Google Cloud — or on your own infrastructure.
How much does generative AI development cost?
Two numbers matter: the build and the running cost. Builds are fixed-price after scoping, and most of our custom AI projects start between €15k and €50k. The running cost is mostly model tokens or GPU time — measured on your real volume before launch and capped in code per request and per month, so the bill is a number you agreed rather than a surprise.
Guardrails, evals and GDPR
Generative models are probabilistic, so we engineer around it: schema-constrained outputs, a labelled test set scored on every change, prompt-injection mitigation, PII handling and human review on high-stakes steps. For European companies we keep data in-region where required and document which data goes to which model provider under GDPR; the EU AI Act risk check is part of our AI strategy work.
Generative AI in production: two examples
For a medical-device QMS platform we built a regulatory AI assistant grounded in the regulatory text, where every answer cites the clause it came from — delivered under an ISO 27001-certified development process. For Seed Real Estate, OpenAI categorises bank transactions inside an n8n workflow that reconciles statements against invoices autonomously, on a 6-hour cycle.
We map your current workflow and identify the highest-impact build.
Architecture and scope, fixed before we write production code.
Senior engineers ship week by week, with a demo every Friday.
Measured against the metric we agreed on — and tuned until it holds.
Generative AI development services build software on large language models and other generative models — assistants, RAG over your documents, document processing, AI agents — and run it in production with evals, guardrails and cost controls. A good provider delivers a working system integrated with your tools, not a prompt library or a proof of concept that never leaves the demo.
Both, done by the same senior engineers. Consulting answers whether generative AI fits a workflow, which model and architecture to use and what it will cost to run; development builds it. If the recommendation is not to build — or to use a tool you already pay for — that's the deliverable, and you don't need to hire us for the rest.
Yes. ChatGPT integration usually means calling the OpenAI API — or Azure OpenAI, if data must stay in your Azure tenant — from your own backend, with retrieval over your data, structured outputs your systems can consume, and logging and cost limits around every call. We connect it to CRMs, ERPs, helpdesks and internal tools, and use Claude or Gemini instead where they fit the task better.
Whichever wins on your data. We test candidate models against a labelled sample of your real inputs and compare quality, latency and cost per request; often a large model handles the hard cases while a smaller, cheaper one takes the volume. Open-weight models served on your own infrastructure are the option when data can't leave your environment.
A first production workflow — one assistant, one document flow — typically takes 2 to 4 weeks at a fixed price. A larger product feature follows our usual cadence: working code demoed every week, with the first version typically live in about 9 weeks. Either way it goes live against an accuracy bar agreed on your real data, not when the demo looks good.
It can be, if it's designed in. We send a model only the data a task needs, mask personal data where possible, use providers' business terms that exclude your data from training, keep data in the EU where required and log every call. When data must not leave your environment at all, we serve an open-weight model on your own infrastructure.
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Talk directly to a principal engineer.
No sales team.
No discovery workshops.
No procurement circus.
We scope, build and ship.
- Reply within one business day
- Engineer-led assessment
- Written proposal
- Lisbon · UK time zone
No commitment. Just an engineer.