AI development and engineering
AI development for your product, and the platform under it
Cloudsail is an AI engineering company with engineers in Germany and Poland. We build assistants, voice interfaces and agents into the products you sell, integrate them with the systems you already run, and build the serving, retrieval and infrastructure they depend on.
Our team has built software and production AI for
Four layers, planned together
What an AI feature costs per request, how quickly it answers and how good its answers are all depend on the model, the retrieval and the infrastructure beneath it. We plan the four layers together, so their trade-offs are settled in one design instead of between separate suppliers.
Agents in your product
Custom agents, MCP servers and A2A. A design uses several agents only where the work divides cleanly.
Applications
Assistants and voice interfaces, and applications that answer from your own documents.
Models
Choosing a model, fine-tuning it where that pays off and testing it on the task it has to do.
Infrastructure
GPUs, inference servers and cloud setup, in your own account or with a provider in the EU.
Where cost and failures start
Cost and failures start where two layers meet: a model larger than its task needs, retrieval that has gone stale, or tool permissions wider than the feature requires. We review those joins first.
In the product
Access to your systems
Underneath
Systems we have run in production
Built and operated by our engineers.
Models
Computer vision models trained on over 100 million images
Applications
Voice AI with responses in under 600 milliseconds
Agent systems
Enterprise assistants that combine retrieval, agents and MCP
Infrastructure
Cloud platforms behind public apps used by hundreds of thousands of people
Project details are available on request.
Talk to an engineerAI integration services for the product you already run
An AI feature becomes useful once it reaches your own systems. Most integrations follow the same path: read the records the user is authorised to see, draft or perform one bounded action, and return the outcome to the workflow where the request started.
Consider a hypothetical support ticket. The assistant reads the ticket, the customer's order history and relevant help articles using read-only credentials. It drafts a reply proposing a refund, and a support agent approves it before anything is sent. Issuing the refund is a separate write permission, added later only if the team wants it.
- Where your existing API or SDK does the job, we call it directly. An MCP server is worth adding when several AI clients need the same tools; for a single feature it is often unnecessary.
- Each action is scoped to specific records and fields, with a named approver where the risk calls for one.
- Failed and repeated calls are handled explicitly. We define which steps are safe to retry and use idempotency keys or status checks so a timeout does not trigger a second refund. That lowers the risk without removing it.
What we hand over, and when it counts as ready
You receive a working integration your team can run and change. It includes the agreed data flow, the permissions it uses and the integration code. Tests cover realistic failures like expired tokens or a slow upstream service. We report measured response times and cost per request, and write deployment instructions for your environment.
Acceptance is agreed up front, with a named owner on your side who signs off. Where the workflow needs caution, the first release can stay read-only or require approval for each action.
Tested on requests from your product
Each feature gets a test set built from real requests. We track the cost and the response time of each request, and the quality of the answers, before and after every change.
Agents for business processes, such as claims handling or invoice checks, have their own page.
How we handle access
We take no money from model or tool vendors, so what we recommend is what tested best for you.
The agents we build run in your environment.
Each tool is limited to the access its task requires.
Every agent action goes into a log.
We work through your works council's requirements before launch.
Questions
What does a first conversation involve?
Thirty minutes with one of our engineers. It is free, and we need no access to your systems or data for it.
What kind of AI development company are you?
An engineering company. The engineers who advise you also build the feature in your product and your cloud, test it on requests from your product and hand over the code, test sets and runbooks to your team. We don't resell tools, and no model vendor pays us.
Can you integrate AI into the systems we already run?
Yes. We connect models to your existing APIs, databases and business systems through tools or MCP servers, with each tool limited to the access its task needs. We start from your current architecture rather than a new platform.
How do you take an AI pilot to production?
We first build a test set from real requests and measure the pilot's cost per request, response time and answer quality against it. The feature goes live with the monitoring, access limits and fallback it needs, and the same tests run before every later change.
Why does it matter that you also handle models and infrastructure?
Because cost and limits are decided at those layers. Should a feature call for a model hosted in your EU account, or for a small model trained on one high-volume task, that work becomes part of the same project rather than a separate one.
Do you also automate business processes?
Yes. Agents for complex cases in your operations, such as claims or invoice checks, are covered on their own page.
Which frameworks do you build with?
The ones your team will maintain after we leave, such as LangGraph, the OpenAI Agents SDK, the Claude Agent SDK, Google's Agent Development Kit or Mastra. Where the control flow is simple, we write plain code instead.
When is more than one agent worth it?
When separate parts of the work belong to different owners, run on different tools or come from different vendors. Each hand-off adds latency, cost and one more thing that can fail, so we start with a single agent and split only where such a boundary really exists. A2A is useful at exactly those points.
Are you tied to one vendor's platform?
No, and no vendor pays us. Your current models, clouds and frameworks are the starting point, and we suggest alternatives only after they have done better on your tasks.
Can you work with our current model and provider instead of replacing them?
Yes. We start from your current architecture and test the model you already use on real requests. If it meets your quality, latency and cost targets, it stays. If it falls short, we show the measured trade-offs against alternatives and you decide whether switching is worth it.
What determines the scope and price of AI integration?
Four things: the systems involved and how we get access, the risk of the actions the AI may take, the evaluation work needed, and the operating work after launch. We agree scope after a call with an engineer. The first 30-minute conversation is free.
Related services
AI automation
Business processes automated step by step with rules, models and agents, tested on past cases and reported as cost per case.
AI chatbots
Chatbots and assistants for customer service and internal support that answer from your data, act through approved tools and hand over to people.
AI consulting
AI strategy, use case assessment and readiness checks from engineers who also build and measure the first system.
AI engineers for your team
Experienced AI engineers who join your team part-time or for longer and build AI features, agents and platforms with you.
AI evals engineering
Evals for AI products, agents and RAG, built from your own traces, with a test set and release gate that stay in your repository.
Enterprise AI
Assistants, agents and private AI built into your systems, with your permissions, audit logs and a platform team that can run them.
MCP server development
MCP servers that connect approved internal APIs to AI assistants, with narrow permissions, approval for changes and tests.
RAG development
Assistants that answer from your internal documents, with permissions, citations and tests on your own questions.
From the blog
AI pilot to production
What has to be in place before a successful AI pilot goes live: regression tests, versioning and rollback, monitoring, an owner, a handover and a staged rollout.
LLM latency budget
How to find out why an AI feature is slow: split each request into queueing, retrieval, model and tool spans, then fix the largest one you can change and measure the tail under load.
Self-hosted AI or managed API
How to decide whether an AI workload should run on your own infrastructure or stay on a managed model endpoint, using access requirements, operating capacity and measured costs.
Talk to an engineer
Bring one piece of work you would like to improve. The call takes 30 minutes and needs no access to your systems or your data.
Look for the draft in your email program
Send it from there, and an engineer replies in person with times for a 30-minute call. If no draft has opened, write to us at miki@cloudsail.com.