We build AI agents and chatbots for business on a RAG architecture with a confidence filter. They handle up to 70% of requests hands-off and reply in about 30 seconds. Live in 4–6 weeks, self-hosted or cloud.
An AI agent pays off where a task repeats and needs access to several systems. Six areas where we consistently see measurable ROI with clients across the EU, the UK, and the US.
A support chatbot answers questions, finds the answer in your knowledge base, escalates the hard cases to a human, and updates the CRM — customer support automation from the first message to the closed ticket. It's conversational AI for customer service that works on your site, in Telegram, and on WhatsApp.
A website chatbot asks qualifying questions, scores fit against your ICP, and hands the lead to the CRM with tags. In effect, AI works your inbound leads.
Answers users inside the product (SaaS, mobile app), helps with setup, explains features, and logs bug reports.
Answers employee questions about company policies, generates HR letters and employment confirmations, opens tickets in the IT support system, and finds the right procedure in the knowledge base.
Parses incoming documents (invoices, contracts, resumes, applications), extracts structured data, validates it, and pushes it into your systems of record.
Advises a store visitor, clarifies what they need, picks products from their history and context, and builds the cart.
Follows a rigid if-then script. Cheap and fast where the questions are predictable.
An LLM answers in free form and remembers the conversation. A helper, not a doer.
An autonomous system: plans actions, uses tools, and makes decisions within set boundaries. Works toward a goal, not along a script.
Every agent is built from four layers: perception (inputs), memory (context and knowledge), reasoning (LLM + planning), action (tools and APIs). A RAG architecture with a confidence filter is the default on every project. We pick the exact stack to fit the task, the budget, and your regulatory requirements.
The agent's memory runs on RAG: when it needs to make sense of unstructured data — a query, a document, a ticket — it searches the knowledge base and answers from what it found, not from its training data. For business logic and operational data: PostgreSQL, MySQL, MongoDB, Redis.
The biggest risk with an AI agent is a confident wrong answer to a customer. Our architecture ships with four layers of defense by default — in the base build, at no extra cost. Per the OWASP Top 10 for Agentic Applications (2026), the top three risks all come down to managing the agent's boundaries.
Every answer is checked: is there enough data to answer? Below a set threshold (configurable, usually 85%) the case goes to a human — with a note on what information was missing.
Legal, financial, and medical questions always escalate, with no AI-generated answer. We set the list of topics at the start of the project.
Content filters block attempts to override the agent's instructions through user input. For external sources we use a domain allowlist.
Every action the agent takes is logged: query → sources used → decision made → result. You get the logs in real time.
Self-hosted deployment on your servers. Data never leaves the agreed infrastructure. A DPIA at the start of the project comes by default.
Obligations for general-purpose AI apply from August 2025, and for high-risk systems from August 2026. For each project we map the use case to a risk class and prepare the technical documentation.
Three AI-agent case studies with measurable results. The full project catalog lives in Case studies.
Three shipped agents, in charts: where AI takes the load off and moves the numbers that matter.
30 sec reply · €1,800/mo saved · RU · UA · EN
Versus 22 minutes by hand · 4 languages · self-hosted
Deal in the CRM 24/7 · ROI in 30–60 days
Numbers from our projects: Support AI Agent, HR AI Assistant, AI Sales Funnel.
Open a phase to see the details.
We look at your current processes, request volumes, knowledge bases, and CRM. We form a hypothesis: what the AI agent will handle, what ROI to expect, what the risks are. If there's nothing worth automating yet, we say so — an agent won't fix a messy process.
We agree on the LLM provider, vector store, channels, and integrations. We write down the agent's boundaries: what it does, what it doesn't, how it escalates. We lock in the success metrics we'll check 2 months after launch.
We build the agent, train the RAG layer on your knowledge base, wire up integrations with the CRM and channels, and test against historical conversations. We tune the prompts iteratively.
We launch to a limited audience — say, 10% of traffic. We read the metrics, adjust the agent's behavior, and gradually grow the share of requests the AI handles. Bugs get caught in a controlled loop, not across the whole stream.
We hand over the documentation and train your team to run the agent. From there you can edit the logic yourself. Support runs on the package you pick.
Every price is locked the moment you book. Anything beyond that is agreed scope only. The ROI calculator estimates payback on your numbers in a minute.
Optional on Start, standard from Business up. We own the bugs, new scenarios, and updates.
at 6 min per request, €12/hr, AI handles 50% · An estimate, not an offer
Most failed AI-agent rollouts in 2025–2026 repeat the same five mistakes. They're rarely about the technology. It's the approach. We spot them, name them for the client up front, and work around them together.
If a process isn't documented and doesn't work with people, an AI agent won't fix it. Before automating we check: is the process repeatable? are there metrics? is there data? If not, we put it in order first, then bring in the AI.
A single agent told to do everything tops out around 60–65% accuracy. Three specialized agents, each on a specific task, hit 90%+ apiece. We always break the problem down.
Without an explicit hard-block on sensitive topics and without a confidence filter, the AI will eventually give a customer a confident wrong answer. That's the biggest risk. So we build defense in by default.
A project that goes straight to production on 100% of traffic catches bugs out in the open. We always start the pilot at 10% of traffic, measure, adjust, then scale.
An agent with no documentation turns into a black box within six months. By contract you always get the full technical documentation, access to all the code, and team training in the final phase.
The nine questions we get most. Don't see yours? Message the CTO or CEO directly.
RAG (retrieval-augmented generation) is an architecture where the AI agent searches your knowledge base (docs, ticket history, CRM) for relevant data before answering, then builds the answer from what it found. This sharply reduces the main LLM problem: hallucination. Without RAG, an agent only knows what was in its training data. With RAG, it answers from your company's current data.
Build: from €2,500 (START) to €20,000+ (ENTERPRISE). Support after launch: from €250/mo (monitoring) to €1,800–2,500/mo (partnership). You pay for LLM tokens directly to the provider (OpenAI, Anthropic, Google) — usually €50 to €500/mo depending on conversation volume. For self-hosted models, GPU infrastructure cost takes the place of tokens.
Three layers: a content filter on input (blocks suspicious patterns), a domain allowlist for external sources (the agent won't visit arbitrary sites), and validation of the answer before it reaches the user. For sensitive cases we add a guard agent that checks the main agent's actions before they run.
Yes. A self-hosted LLM is the standard setup for regulated industries and clients who need full data isolation. We support Llama, DeepSeek, and Mistral. We deploy on your servers or on agreed private infrastructure. One caveat: self-hosted models need GPU resources and usually run 2–4 times slower than cloud models at comparable quality.
There's no hard limit — it scales horizontally. In practice: at 10,000 conversations/month, basic infrastructure is enough. From 50,000 conversations we add caching for common answers and routing across specialized agents. Above 200,000 we discuss a separate architecture with dedicated capacity for the load.
The built-in defenses (confidence filter + hard-block on sensitive topics) minimize the risk, but don't drop it to zero. By contract, for the first two weeks after launch we stay on high alert and fix any errors we find within 24 hours. The incident-handling documentation goes to your team in the final phase.
We lock the metrics in the architecture phase and check them 2 months after launch. The standard set: share of requests handled autonomously, average reply time, user CSAT, tier-1 payroll savings, and revenue from the conversion lift. We build a dedicated dashboard for each project.
No, and that isn't the goal. The goal is to close 60–70% of routine requests autonomously so your people can work the hard cases and build the product. Fully replacing humans with an AI agent in 2026 is impossible for regulated industries and a bad idea for most B2B scenarios. The best result comes from the pairing: AI handles the routine, a human makes the hard calls.
A chatbot is cheaper and faster when the job is to answer FAQs and follow a simple script. You need an AI agent when it has to plan actions, reach into several systems, and make decisions. Plenty of projects start with a website chatbot and grow it into an agent later — that's a normal path. If the job isn't a conversation but an end-to-end process across systems, that's business process automation.
We'll go through your processes, pin down where an AI agent pays back fastest, and send a short report with an architecture hypothesis.