--- title: A platform that improves every day — alluvo description: How alluvo keeps developing for staffing agencies: from feedback, error reports and run times. Learn how self enhancement works. url: https://alluvo.ai/en/self-enhancement --- Self enhancement # Gets better every day. On its own. alluvo improves itself for your day as a staffing agency, every day, from error reports, run times and your ratings. Never from your business data. What flows in describes the platform. Not your company. Five mechanisms do the work, four of them while you sleep or are on the phone. Below, one day runs through. Drag it into shape. Self-healing Errors sort themselves out Fewer moves Shorter routes to the goal Ratings feed in Your thumb counts Feedback on the desk Two sentences are enough Nightly agents The second shift A day of self enhancement ## 24 hours. Ready to drag. Night on the left, night again on the right. Between them lies your working day, and alongside it, alluvo's. Drag the handle across the bar, play the day or step through with the arrow keys. What happens at night, what you notice of it in the morning and what two sentences of feedback turn into, one after another. Each entry belongs to one of the five mechanisms. Day log · Self enhancement An example day[1] 00:00 Drag, click or use the arrow keys. 00:00 Nightly agents ### Your day is over. Nobody clicks any more, no list is opened. For alluvo the second shift starts now. Nightly agents in detail 02:00 Nightly agents ### The agents read along. Run times, error patterns, aborts: agents go through the technical traces of the day and look for the spots where alluvo sticks. Nightly agents in detail 04:30 Nightly agents ### Optimization delivered. What the night found goes out as a version. No maintenance window, no announcement, no update date anyone would have to agree with you. Nightly agents in detail 07:00 Nightly agents ### All you notice: faster. You switch on your computer. The list that hung briefly yesterday is there at once. Nothing more happens for you, and that is exactly the point. Nightly agents in detail 09:12 Self-healing ### An error comes up. An error report is created, before you report it. alluvo catches it, classifies it and checks whether this case has happened before. Self-healing in detail 09:15 Self-healing ### Three minutes later: resolved. To a certain degree alluvo resolves such cases itself. So the error you are noticing right now may already be history. Self-healing in detail 11:30 Ratings ### A thumb carries weight. Somebody rates what the AI delivered: thumb, category, a sentence. The rating goes into the evaluation, not into an archive. Ratings in detail 14:20 Feedback ### Feedback lands on the desk. From the web app or through MCP, sent in two sentences. Without a ticket number, form or waiting queue, straight to the product team. Feedback in detail 17:45 Feedback ### Live the same day. If we are having a good day and the request is relevant for everyone, the feature may be delivered the same day. Feedback in detail 21:00 Fewer moves ### alluvo counts the moves. At the end of the day alluvo looks at the day's routes: where were there seven steps, where would three have done? That is what the tools are sharpened from. Fewer moves in detail The mechanisms ## Five ways. One goal. None of them needs you. Four run anyway, one waits for two sentences from you, and is therefore the most effective. 01 Self-healing ### Errors that sort themselves out. alluvo catches error reports on its own, classifies them and resolves them to a certain degree itself. Not every case, but the kind that would otherwise cost you a call, a ticket number and three days of patience. So an error you are noticing right now may be resolved within minutes. Without you ever having reported it. 09:12 09:15 The incident from the day bar above: caught, classified, resolved. 02 Fewer moves ### Fewer moves. Same goal. Every day alluvo analyzes certain activities and derives from them how to sharpen its own tools: plugins, MCP servers, the assistants themselves. Not so that you click faster, but so that there is less to click. Train board · example[2] One goal · two routes "Who is on the bench from Monday, and who do I offer them to?" Yesterday · seven moves 1. Open the employee list 2. Filter by availability 3. Sort by qualification 4. Check the distance to the client 5. Lay the client list next to it 6. Match them up by hand 7. Write the offer Today · three moves 1. Say what you want to achieve 2. Review the suggestion 3. Approve 7 3 Moves to the same result Checkmate. 03 Ratings feed in ### A thumb is an order. Wherever AI works in alluvo, you can rate it: thumbs up, thumbs down, a category, a sentence. These ratings are evaluated and feed into improvements, from semi-automatically to automatically. They do not end up in a statistic nobody reads, but in the list we build from. 1. Rating Thumb, category, a sentence 2. Evaluation bundled instead of one by one 3. Pattern what repeats counts 4. Improvement in one of the next versions 04 Feedback on the desk ### Two sentences. One desk. Feedback works any time, from the web app or through MCP, straight from your assistant. No ticket system, no number, no waiting queue: it lands with the product team immediately. If we are having a good day and your request is relevant for everyone, the feature may be live the same day. Feedback · demo Nothing is sent Your feedback The desk · product team 1 slip Still empty. Write something in, or take one of the suggestions. - When booking I miss the desired end date. Web app 14:20 **With the product team** This is what the route looks like. This page is a demo and sends nothing. In alluvo this slip is one click away, in your assistant one sentence. Mechanism 05 · Nightly agents ## Work happens at night. Does something feel slow for you? It may be gone by tomorrow morning. When nobody is at a computer on your side, agents analyze and optimize alluvo in various places, partly fully automatically. They read run times, error patterns and aborts, look for the spots that stick and sharpen them. Not every night brings something. But every night is spent searching. - 00:00 The application goes quiet. Nobody clicks any more, and the second shift begins. - 02:00 Agents go through the technical traces of the day: what was slow, what aborted, what was tried twice. - 04:30 What is worth it goes out as a version. With no maintenance window and no appointment anyone would have to agree with you. - 07:00 You switch on your computer and notice nothing. Except that it is faster. Data boundary ## Learns from itself. Not from you. The daily improvement is fed by what the platform knows about itself: how fast it was, where it stumbled, what you wanted from it. Your business is not part of that calculation. **What flows in.** - Error reports - Performance signals - Ratings of AI results - Submitted feedback - Usage patterns of the tools Technical signals and what you tell us explicitly. None of it carries a name from your database. **What never flows in.** - Your employee data - Your client data - Your contracts - Your documents - And no training of AI models with your data The improvement of alluvo is separate from your business. How this separation is anchored in the product is on [security](https://alluvo.ai/en/security). The proof ## This is not roadmap romanticism. This is our changelog. We have been counting since März 2026. Every number here is calculated, none is typed in. **versions[3]** 210 **releases per day** 1.1 **since the first version** März 2026 Which of these came from a night, from a rating or from two sentences of feedback is not listed there. The versions are, and they can be counted. [To the changelog](https://alluvo.ai/changelog) ## No ticket. No waiting. The days of drawn-out feature requests and months-long bug fixes are over. alluvo was built that way from the ground up, and the speed does not stay stuck in the platform. It lands in your company. As a head start. [Start for free](https://app.alluvo.ai/register?lang=en&market=en&utm_source=website&utm_medium=market&utm_campaign=en) Get to know alluvo ## Footnotes 1. The course of the day illustrates the five mechanisms; it is not an excerpt from a log. The times are chosen, not measured. ↩ 2. An example sequence for illustration. How many steps a goal actually takes depends on the case, the data and the connected tools. ↩ 3. As of 4. Oktober 2026, counted from the release tags of the alluvo platform, the same source as the changelog. ↩