AI Integration Services

AI that earns its keep, not just applause

Implementing AI in a company makes sense when it shortens a specific process, lowers the cost of handling work or surfaces knowledge you didn't have. We work with AI every day: we integrate models with databases, run AI analyses and design products with AI inside. That is exactly why we know AI gets things wrong – you have to recognise when it errs and hold a clear vision of the result to steer it. That vision comes from 22 years of design and delivery practice.

We don't sell a revolution. We sell a working process, measured before and after the implementation.

Problems we solve

Your team drowns in repetitive tasks: we automate enquiry handling, product descriptions, reports and document flows.
The company has data but no knowledge from it: we connect AI models to your databases so they answer business questions in seconds, not weeks.
Competitors "already have AI" and you don't know where to start: a workshop ranks the ideas by return on investment and picks the pilot.
A previous AI attempt ended as a demo with no result: we take over after failed attempts and carry projects to production.

What we actually do

AI integrations with company data

We connect language models to your databases, documents and systems (CRM, ERP, helpdesk). The result: your people ask about data in plain language and the answers come from your sources, with references. We design the architecture so data never leaks outside the company.

Process automation

We pick the processes where AI genuinely shortens work: lead qualification, proposal preparation, e-commerce product descriptions, contract analysis, meeting summaries. Every automation is designed with a human checkpoint wherever the cost of an error matters.

AI agents for business

We build agents that carry multi-step tasks from start to finish: answering customers based on your offer, watching deadlines, preparing reports. An agent gets a clearly described scope and permissions – it knows what it may do alone and what needs a human sign-off.

AI analyses

One-off or recurring analyses of large datasets: customer reviews, support tickets, sales data, competitor content. Where an analyst would need weeks, AI with a well-designed process delivers in days – and we make sure the result is true, not a generated guess.

Team enablement included

The best tool earns nothing if nobody uses it. After every implementation we show your team how to work with it: a live session, concise instructions, good practices. We implement and we teach you to use what we implemented – no separate training products to buy.

How we work together

AI workshop: a review of processes and data, a list of use cases ranked by return, pilot selection. You leave with a plan you could deliver even with another vendor.
Pilot: one process, a clear success metric, a working implementation in weeks, not quarters.
Measurement: we compare the result with the starting point: time, cost, quality. The numbers decide whether we scale.
Rollout and integrations: we extend the solution to further processes and connect it with your systems.
Enablement and care: we train your team on the implemented tools, monitor answer quality and grow the solution on a support plan.

Why Sweet Lava

Because we combine two things that rarely go together around AI: integration engineering and experience design. We treat AI as a tool steered by design experience – we know when a model is wrong, how to communicate uncertainty and where to place a human in the process. And if the workshop shows AI won't pay off for you, you'll hear it from us plainly.

AI training for companies – when a workshop is enough, and when you need an implementation

Training alone rarely changes how a company works. We explain when a workshop makes sense, when it's better to implement the tools and teach the team on their own processes, and how to work out which pays off.

Implementing AI in a company, step by step – what our own projects taught us

How AI implementation works in practice – from a data audit, through a pilot with a measurable goal, to team enablement. With concrete price ranges and a list of situations where AI isn't worth it.

AI agents for business – what they are and what they cost

An AI agent completes multi-step tasks on its own – answering customers, qualifying leads, preparing reports. We explain how it differs from a chatbot, what it costs and where to start.

UX/UI/AX

AX, or AI Experience – how to design products with built-in artificial intelligence

AI is turning interfaces from deterministic into conversational and probabilistic. AX is the discipline of designing this new material – here are its principles.

Frequently asked questions

How much does an AI implementation cost?

A pilot implementation – one process with a measurable result – usually costs €3,500–10,000. Larger integrations with your systems and databases typically land between €10,000–35,000, depending on the number of processes and integrations. Every quote follows a workshop that leaves you with a list of use cases ranked by potential return.

What is the hardest part of implementing AI in a company?

Not the technology – the data and the process. Today's AI models are mature, but they need well-organised data and a clearly described process to plug into. That is why we start with a data audit and a single pilot process, instead of "implementing AI everywhere".

What is an AI agent and how much does it cost?

An AI agent is a program that completes multi-step tasks on its own – for example answering sales enquiries based on your price list, qualifying leads or preparing reports from your data. A simple agent built on company data starts at around €5,000; complex agents integrated with multiple systems are quoted after a workshop.

Is our data safe when working with AI?

Yes, provided the architecture is designed consciously – and that is what we design. Your company data does not feed public model training; we use commercial APIs with privacy guarantees or models running in your own infrastructure. The scope of AI access to data is written down in black and white before we start.

Will you train our team to use the implemented tools?

Yes – it is a standard part of every implementation, not a separate service. We show your team how to use the tools we set up, hand over instructions and good practices, and answer questions on the support plan as new use cases appear.

When is an AI implementation not worth it?

When the process is rare, the data is scattered, or the cost of an error is high and human verification is needed at every step anyway. In those situations we say plainly that a simple automation or a better form will pay off sooner. That recommendation is a workshop result too.