AI for business — where it actually pays off.
Not with the tool. With where it's leaking.
Most AI rollouts start with the question "which service should we plug in" — and end with a subscription nobody uses. We start from the other end: we look for the most expensive routine in the business. How many hours a week go into copying requests by hand. How many orders are lost while the manager sleeps. What it costs to answer the same question for the hundredth time. Then we do simple arithmetic: what it costs now, and what it would cost if a machine did it. If the difference isn't worth the work, we say so.
What's working right now.
Communication & clients
- AI chatbots in messengersTelegram, WhatsApp, Instagram Direct, Messenger, web chat
- AI processing of inbound inquiries and emailsclassification, auto-replies, routing
- AI voice agentstake calls, consult, book appointments
- Call & meeting transcriptionplus conversation analytics
- Automated review repliesGoogle, social media, marketplaces
Content & copy
- Text generationwebsite, ads, newsletters — ChatGPT, Claude, Gemini
- Translation & localisationDeepL, GPT
- Image generationMidjourney, DALL·E, Stable Diffusion, Flux
- Video generationSora, Runway, Kling
- Voiceover & voice cloningElevenLabs
- SEO content & optimisationpages, metadata, structure
Sales & marketing
- Personalised product recommendations
- AI lead scoringwho to sell to first
- Automated nurture in email & messengers
- AI ad targeting & creatives
Data & decisions
- Analysing tables and financial reports in plain language
- Forecastingsales, demand, churn
- Automated dashboards and reports"what changed this week"
- Search across your knowledge basein natural language
Team work
- AI assistant for the teamgrounded in your internal documents
- Automating routinefilling forms, reconciling, moving data between systems
- Automatic meeting noteswith tasks and owners
- Code generation and code reviewfor engineering teams
Operations
- Document recognitionreceipts, invoices, IDs — into CRM
- OCR and auto-entry into accounting
- Content moderation and spam filtering
- Fraud and anomaly detection
Special
- AI agentsthat carry out sequences of actions: book, order, compare
- Synthetic video avatars
- CV and interview review
- Search and data collection from open sources
One process, a few weeks, a measurable result.
Find the costliest part
We work out where routine eats the most time and money. We pick one process, not ten at once.
Prepare the data
The most common reason rollouts fail isn't the model — it's data scattered across chats and spreadsheets. We fix that first, or nothing else works.
Pilot
We launch on a single process. A few weeks, limited scope, clear metrics: faster or not, cheaper or not.
Scaling
If the pilot delivers, we extend to adjacent processes. If it doesn't, we don't. That's an answer too.
Training the team
So people actually use it instead of working around it. Otherwise any rollout dies within a month.
Support or handover
We stay alongside — or hand it over with instructions.
Honestly: sometimes it isn't worth it.
AI works well where there's repetition, clear rules and a high cost of manual work. It works badly where the process is vague, responsibilities are undefined and the data is dirty. In that case automation simply speeds up the mess — you end up paying for chaos to happen faster. So sometimes we say: put the processes in order first, and we'll plug in AI after. It takes longer, but it's the only thing that works.
One team instead of hunting for a unicorn.
No AI specialist to hunt for
The market is overheated, salaries are high, and judging real skill at an interview is nearly impossible.
Experience instead of experiments
We've already seen where rollouts like this fall apart.
No hiring for an unknown outcome
The pilot shows whether it's worth doing at all.
No payroll burden
No salaries, workplaces or equipment.
Flexible volume
Need more — we scale up; need less — you don't pay for idle time.