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Frequently asked questions

Everything you need to know about working with Dixeam. Can't find your answer? Ask us directly

AI agents & generative AI

What is an AI agent?

An AI agent is software that uses a large language model to plan and complete a task. Unlike a chatbot, it can take actions: call APIs, update records, send messages and check its own results. Risky actions can require human approval.

Which AI models do you use?

We're model-agnostic. We work with managed models such as Claude, GPT and Gemini and self-hosted open-weight models such as Llama and Qwen. We choose per workload based on quality, privacy, latency and cost, and design integrations so models can change later.

Can AI work with our existing software?

Yes. Most of our AI work plugs into systems clients already use: CRMs, ERPs, helpdesks, databases and custom Laravel, Django or Node apps. If an integration doesn't exist yet, we build it.

What's the difference between RAG and fine-tuning?

RAG lets the AI look up answers in your own content and cite sources; fine-tuning changes the model's behaviour through training. For business knowledge, RAG is usually the right start.

Pricing & timelines

How much does it cost to build an AI agent?

It depends on the number of integrations, the risk level of the actions and volume. Most clients start with a fixed-price 2–4 week proof-of-concept sprint, so you see real results before committing to a full build.

How long does a typical project take?

Discovery takes about a week, an AI proof of concept 2–4 weeks, and a production build typically 6–12 weeks. A SaaS MVP is usually 8–16 weeks after discovery and design.

What are your payment terms?

We accept 100% advance for projects under $500. Larger projects can be paid in milestones. See our terms and conditions for details.

Which engagement models do you offer?

A fixed-price AI proof-of-concept sprint, fixed-scope projects with milestones, and dedicated teams billed monthly. See ways to work with us.

Security & ownership

Is our data safe? Will it be used to train AI models?

Data handling depends on the architecture agreed with you. Sensitive workloads can use self-hosted models and private RAG inside your infrastructure; other workloads can use approved managed AI APIs under their applicable enterprise data controls. We document where data is processed and keep access scoped.

Do you sign an NDA?

Yes. We sign an NDA before discussing any confidential details of your project.

Who owns the code and designs?

The design and code we create for you belong to you. See our terms for details on source file delivery.

Working with Dixeam

Do you still build regular web, mobile and SaaS products?

Absolutely. Custom web, mobile and SaaS development has been our core since 2013, and it's what makes our AI work production-ready.

How do you keep us updated?

We provide daily and weekly progress reports, demos at the end of each sprint, and a single point of contact for your project.

What happens after launch?

We monitor quality, uptime and AI costs, fix issues quickly and keep improving the product. Support and maintenance plans are available for every project.

Let's talk

Tell us what you want to automate or build

  1. We reply within 1 business dayA real engineer reads every message.
  2. A 30-minute discovery callWe discuss your goals, data and constraints.
  3. A clear proposalRecommended approach, timeline and cost.
What do you need?

Protected by NDA on request.