# AI & automation Less copying between tools. More time for work that needs your team. For companies where people do by hand every day what could run on its own, and that want to use AI where the result can be checked. ## Who this is for ### Operations teams with repetitive work The same data typed into the CRM, a spreadsheet and the accounting system. Recurring reports assembled by hand from several sources. Requests copied from email into the ticketing system. Everyone knows it is wasted time, but nobody has the time to change it. ### Companies with a growing queue of documents and enquiries Invoices, contracts, forms, customer messages. The queue grows faster than the team, and answers and decisions depend on who happens to have time to read the document. ### Organisations that want to adopt AI with control over the result The board asks about AI, the team worries about data and quality. You need a first deployment that can be measured, explained and switched off if necessary, not a demo on slides. ## What we usually find The same patterns come up in most companies we talk to: - One person is the manual link between two systems that both have an API. - The weekly report is built by copying cells and takes half a day. - Requests land in a shared inbox and are sorted by hand by topic and urgency. - Quick automations in a no-code tool work until one field changes. - Nobody knows what happens when the automation hits an exception, so someone checks everything by hand anyway. - Data goes to external AI tools without anyone deciding what may be sent there. Most of these can be fixed with one well-described process. The last point is also a compliance question: which data goes to which tools and on what basis, which is part of our [digital compliance](https://kol.ski/#areas) work. ## What we work on ### Integrations & workflows We connect APIs, forms, databases and business tools. We account for duplicate requests, failed steps and retries so the workflow can be followed and maintained. ### AI-assisted processing Classification, extracting information, summarising documents and preparing drafts for review. We assess the output on representative examples before extending the scope. ### Everyday operations Recurring reports, notifications, data synchronisation and moving tasks between systems. We use no-code tools or custom code depending on complexity and maintenance needs. Automations run in an environment the team can maintain, see [cloud & infrastructure](https://kol.ski/en/cloud-infrastructure). ## What we have done before The [founders’](https://kol.ski/en/founders) experience from projects delivered before the company was set up, directly and in cooperation with agencies. More under [achievements](https://kol.ski/#experience). - More than 25 in-house tools and automations: static publishing of sites, contact forms, consent and cookie handling, monitoring, panels for managing machines, task synchronisation. - More than 120 analytics properties and tag containers brought into one consistent pattern of names, structure and permissions. - Access for many organisations at once: the client, agencies and media houses in one model of roles, granted and reviewed in one place. - A growing number of steps supported by AI: from content analysis to automating repetitive work, always with a human check. - A team of a few people running dozens of sites. Scale comes from automation, not headcount. ## Example: publishing without manual work A typical starting point and what we built from it. The client’s name, figures and configuration details are something we discuss in a meeting. ### Before Every publication on a site needed an administrator: clear the cache, check that the site works, switch the machine on and off. Cookie consent and analytics configured by hand, separately for each site, each one slightly different. ### After The editor clicks “publish”. The automation builds a static version of the site, checks it, publishes it and switches the machine off. Consent, tags and analytics deployed from one template. Monitoring reports a problem before the client does. ### Result Dozens of sites run by a team of a few people with no manual steps at publication. More than 120 analytics properties in one layout. Changes to consent and tags made once for the whole portfolio of sites. ## Three steps, each one you can stop after ### 1. Free consultation 30 minutes online. You tell us what the team does repeatedly and with which tools. We tell you what can be automated, where AI makes sense and where it does not, and where we would start. No preparation, no commitment. ### 2. Pilot of one process Fixed scope and a fixed price agreed before we start. One process, a representative data sample with sensitive information removed, a working automation checked on ordinary cases and exceptions. We review the result with your team. ### 3. Implementation The process goes into daily work, further processes follow in stages, with acceptance criteria and a price agreed for each stage. After every stage you decide whether we continue or your team takes over. ## What the pilot covers - A process map: inputs, decisions, exceptions, the owner and the points where a person checks the result. - Data rules: what may be processed, where it may go, who has access; a choice of local, private or cloud tools. - A working automation for the agreed task, with logging, error handling and a manual fallback. - Evaluation on representative examples: accuracy, time, the number of cases that need a person. - Instructions for the team and the criteria you will use to judge the process after a month. This is what a fragment of a pilot’s process map looks like. The figures are illustrative, the layout is real: | Step | Today | After the pilot | Human check | | --- | --- | --- | --- | | Receiving a request | Email to a shared inbox, read by hand | The automation reads the email and attachments | None, full log | | Classification | An employee decides the topic and urgency | AI proposes a category and urgency with a rationale | Low confidence goes to a manual queue | | Extracting data | Typed into the system | The automation fills the fields and flags missing ones | An employee approves before saving | | Reply | Written from scratch | A draft from a template and the request’s content | Always sent by a person | | Exceptions | Lost in the inbox | A separate queue with a reason and a deadline | Reviewed once a day | The pilot is also the foundation for further processes: the map and the data rules stay with the company whether or not we continue. ## What stays on your side Automations run in your accounts and your environment. Code, configuration and access stay with the company, not with the contractor. - The process map and data processing rules, written down and ready to show an auditor. - The automation’s code or configuration in your repository or your no-code tool account. - A log of every run: what came in, what went out, where a person stepped in. - Operating instructions and knowledge transfer to your team, as [workshops](https://kol.ski/en/training) where useful, or an agreed monthly scope of continued maintenance. ## Data and responsibility stay visible Before choosing tools, we agree which data may be processed, where it may go and who can access it. Depending on those requirements, we assess local, private or cloud-based options. Actions that need approval keep an explicit review step. When the process concerns publishing content, we connect it to the [platform](https://kol.ski/en/digital-products) where the content is created, and when no ready-made tool exists, we build it as [custom R&D](https://kol.ski/#areas). ## FAQ ### Do we have to use AI? No. Many processes only need an ordinary integration. We use AI where the task involves reading, classifying or writing and where the quality of the result can be checked. ### Will our data go to external models? Only if we agree so. Before we start we define which data may leave the company and choose tools accordingly: local, private or cloud-based. The pilot runs on a sample with sensitive data removed. ### What happens when the automation makes a mistake? Every process has a human check where a mistake is costly, a separate queue for exceptions and a manual fallback. The logs show what came in and what went out. ### We already have automations in a no-code tool. Do we have to throw them away? No. We tidy what works, document it and add error handling. We rewrite in code only where no-code stops being maintainable. ### How do you charge? First consultation: free, 30 minutes online. Pilot: fixed price. Implementation: stages with acceptance criteria and a price agreed for each stage. Operations: a monthly scope agreed after handover. ### What about confidentiality? We sign a non-disclosure agreement and a data processing agreement before receiving a sample or access. Access is personal, limited to the scope of work and revoked when it ends. How we handle data from the contact form is described in the [privacy policy](https://kol.ski/en/legal). ## Tell us what your team repeats Describe one task, the tools involved and the result you need. We will propose a time. [Discuss your project](mailto:hi@kol.ski) · [Book 30 minutes](https://calendar.app.google/CJxZPq3FqabCDMS59) · [Main areas](https://kol.ski/#areas)