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AI & Automation

Your Team is Doing Work a Machine Should Handle

Your team answers the same questions, copies data between systems, and updates spreadsheets nobody reads. Every week. The best people burn out. Mistakes creep in. We build AI and automation that handles it, so your team focuses on work that matters.

How It Works

How AI Works for Business

AI is not magic. Here is what it actually does and how businesses use it.

Large Language Models (LLMs)

LLMs like GPT-4, Claude, and Gemini are AI models trained on massive amounts of text. They understand language, can follow instructions, and generate human-quality text. For businesses, this means you can build systems that understand customer questions, summarise documents, extract structured data from unstructured text, and generate content. The models are accessed through APIs, meaning you pay per use rather than building your own AI from scratch.

RAG (Retrieval-Augmented Generation)

RAG is the technique behind AI assistants that answer questions from your own data. Your documents are converted into embeddings (mathematical representations of meaning) and stored in a vector database. When a user asks a question, the system finds the most relevant document chunks and passes them to the LLM, which generates an answer grounded in your content. This is how you build a chatbot that knows your products, policies, and processes without training a custom model.

Workflow Automation

Automation connects your tools and eliminates manual, repetitive work. Using platforms like n8n and Make, or custom API integrations, you can build workflows that trigger automatically. A new order in your e-commerce store creates an invoice in Xero, updates your inventory, and sends a confirmation email. A support ticket gets classified, prioritised, and routed to the right team. The most effective systems combine automation (for the structured, predictable parts) with AI (for the parts that need understanding and judgment).

When to Use AI vs. Simple Automation

Use simple automation when the logic is predictable: "when X happens, do Y." Use AI when the task requires understanding, classification, or generation. For example, sending a notification when a form is submitted is automation. Reading an uploaded invoice and extracting the vendor name, amount, and due date is AI. Routing a support ticket based on a dropdown field is automation. Reading a customer's message and understanding whether they are asking about billing, a technical issue, or a feature request is AI. Most real-world projects use both together.

Real Results

AI in Action

What AI actually does for businesses like yours.

Customer Support on Autopilot

AI chatbots trained on your documentation handle the repetitive questions your team answers 50 times a day. Order status, return policies, pricing, hours. The bot answers instantly in any language. Your team only gets involved when a question actually needs a human. Most businesses see 60-80% of incoming queries resolved without human intervention within the first month.

Reduce support costs by 40-60%

Kill Manual Data Entry

AI reads invoices, contracts, and forms the same way a human would, but in seconds. It extracts the relevant fields (dates, amounts, names, line items), validates them against your existing data, and pushes them into your accounting or CRM system. No more copy-paste between tabs. No more fat-finger errors in spreadsheets.

90% faster document processing

Connect Systems That Do Not Talk

Your CRM knows about the deal. Your accounting system does not. Your website takes an order but your inventory system finds out tomorrow. We build the connections so data flows between systems in real time. When one system updates, every connected system updates instantly.

Zero manual data transfer between tools

Instant Answers for Your Team

Your company has years of knowledge buried in documents, wikis, and shared drives. AI turns all of that into a searchable assistant your team can ask questions in plain English. "What is our return policy for international orders?" "How do I set up a new vendor in the system?" Instant answers, sourced from your own docs.

Faster onboarding and fewer repeat questions
60-Second Check

Is AI Right for You?

Answer a few questions. Find out exactly where AI fits in your business.

AI Readiness Assessment

Question 1 of 4

What repetitive tasks consume most of your team's time?

Built With

Technology Behind It

Proven AI providers and platforms that power your project.

OpenAI (GPT-4)
Anthropic Claude
Google Gemini
n8n
Custom API Integrations
Malta

AI Built for Malta Businesses

In-person AI workshops. We identify real opportunities, not hype. Up to 60% EU funding for Malta SMEs.

iGaming AI

Malta's gaming industry is adopting AI for fraud detection, personalisation, and customer service within regulatory requirements.

Practical, Not Hype

AI that delivers real business value. Not demos that impress but do not work in production.

In-Person Workshops

AI projects need close collaboration. Face-to-face workshops help identify the right opportunities.

Up to 60% EU Funding

AI qualifies as digitalization under the Digitalise Your SME grant for eligible Malta SMEs.

"We worked with Solveita to make a software that helps us convert eBooks. Our team did lots of repetitive work before, but the tool they built us uses AI to generate images with different styles. It took some months to find the right AI models, but now our workflow is much simpler and we can deliver faster."
Daniel Benchimol
CEO, Proyecto451
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AI & Automation Questions

Automation follows fixed rules: "when X happens, do Y." For example, when a new order comes in, send a confirmation email. The logic is predictable and repeatable. AI handles tasks that require judgment. Understanding what a customer is asking in their own words, extracting data from a messy PDF, deciding which support tickets are urgent. The key difference is that automation needs you to define every rule upfront, while AI can handle ambiguity and variation. In practice, the best systems combine both. AI handles the parts that need intelligence (reading a document, understanding a question), and automation handles the structured workflow around it (routing the data, triggering actions, sending notifications). See our automation services for rule-based workflows.

RAG stands for Retrieval-Augmented Generation. It is the technology behind AI assistants that can answer questions from your own documents. Here is how it works: your documents (PDFs, web pages, internal wikis, policy docs) are split into chunks and converted into mathematical representations called embeddings. These embeddings are stored in a vector database. When someone asks a question, the system finds the most relevant document chunks, passes them to the AI model (like GPT-4 or Claude) along with the question, and the model generates an answer based on your actual content. The result is an AI that answers from your data, not from its general training. This means it can answer questions about your specific products, policies, or processes. The accuracy depends on the quality and coverage of your source documents.

AI chatbots start from around 2,500 EUR for a basic support bot trained on your FAQ content. Multi-source chatbots with CRM integration and human handoff start from around 4,000 EUR. Workflow automation projects (connecting systems, automating processes) range from 1,000 to 5,000 EUR depending on the number of systems involved. Complex AI systems with custom models, multiple integrations, and advanced logic range from 5,000 to 15,000+ EUR. We always start with a scoping session to understand what you actually need. We will tell you if something is overkill for your situation.

Simple automations connecting 2-3 systems: a few days to 1 week. AI chatbots with a single knowledge source: 2 to 3 weeks. Advanced chatbots with multi-source RAG, integrations, and human handoff: 4 to 6 weeks. Complex AI systems involving custom model training or large-scale document processing: 6 to 10 weeks. We ship a working prototype within the first week or two so you can test early and give feedback before we build the full system.

This is the most common concern we hear and it depends on your requirements. Standard builds use OpenAI or Anthropic APIs. Your data is sent to their servers for processing, but under their business data processing agreements, it is not used to train their models. For stricter requirements (financial services, healthcare, legal), we use AWS Bedrock or Azure OpenAI Service. With these options, your data stays within your own cloud environment, is encrypted at rest and in transit, and never leaves your infrastructure. For the strictest requirements, we can deploy open-source models (like Llama or Mistral) entirely on your own servers. No data ever leaves your premises. Each approach has different cost and capability tradeoffs, and we help you choose the right one based on your regulatory requirements.

No. This is a common misconception. Modern large language models (LLMs) like GPT-4 and Claude already understand language, reasoning, and general knowledge. You do not need to train them. For most business applications, you just need your existing content: product documentation, FAQ pages, policy documents, internal wikis. RAG-based systems work with whatever documents you have, even just a few PDFs. The AI reads your content and answers questions based on it. The more complete your documentation, the better the answers. But you do not need thousands of data points or years of historical data to get started.

The simplest way to calculate it: count how many hours per week your team spends on the task you want to automate, multiply by the hourly cost of that person, multiply by 52 weeks. That is your annual cost of doing it manually. Compare that to the one-time build cost plus ongoing maintenance. Automation that saves 5 to 10 hours per week typically pays for itself within 3 to 6 months. But the real value goes beyond time savings. Automated processes run at 2am, on weekends, and during holidays. They do not forget steps, they do not make typos, and they process work in seconds instead of hours. Use our automation ROI calculator for a quick estimate based on your numbers.

AI has three types of ongoing costs. First, API usage: if your system uses OpenAI or Claude, you pay per API call. For a typical chatbot handling 1,000 conversations per month, expect 20 to 100 EUR per month in API costs. High-volume systems cost more. Second, infrastructure: hosting the vector database, the application server, and any middleware. Typically 30 to 100 EUR per month depending on scale. Third, maintenance: updating the knowledge base when your content changes, monitoring for issues, and improving the system based on usage patterns. We offer maintenance plans or can train your team to handle updates themselves. We give you transparent cost breakdowns before you commit so there are no surprises.

Yes, if your tools have an API, which covers most modern software. We regularly integrate with CRMs (HubSpot, Salesforce, Pipedrive), accounting tools (Xero, QuickBooks), email platforms (Mailchimp, SendGrid), payment systems (Stripe, PayPal), and communication tools (Slack, Microsoft Teams). For older systems without APIs, we can build adapter layers that expose the data through a modern interface. The AI or automation system connects to the adapter, which translates requests into whatever format the legacy system understands.
Related Services

Other Problems We Solve

Other ways we help businesses like yours get more customers and save time.

Your team deserves better than manual work

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