[{"data":1,"prerenderedAt":533},["ShallowReactive",2],{"navigation-en":3,"language-switcher-\u002Fen\u002Fblog\u002Fwdrozenie-ai-w-firmie-od-czego-zaczac":87,"blog-slug-en-wdrozenie-ai-w-firmie-od-czego-zaczac":93},{"services":4,"industries":48,"caseStudies":69},[5,10,14,19,23,27,31,35,39,43],{"slug":6,"navLabel":7,"navGroup":8,"heroHeadline":9},"web-applications","Web applications","build","A web application built around your process",{"slug":11,"navLabel":12,"navGroup":8,"heroHeadline":13},"erp-systems","ERP systems","An ERP system built for your company, not the other way round",{"slug":15,"navLabel":16,"navGroup":17,"heroHeadline":18},"integrations-and-api","Integrations and APIs","improve","Your systems stop being islands",{"slug":20,"navLabel":21,"navGroup":17,"heroHeadline":22},"ai-implementation","AI implementation","AI deployed where it actually saves time",{"slug":24,"navLabel":25,"navGroup":17,"heroHeadline":26},"process-automation","Process automation","Work no one should be doing by hand",{"slug":28,"navLabel":29,"navGroup":8,"heroHeadline":30},"websites","Websites","A site that loads instantly and brings in inquiries",{"slug":32,"navLabel":33,"navGroup":8,"heroHeadline":34},"mobile-applications","Mobile applications","One codebase, both platforms",{"slug":36,"navLabel":37,"navGroup":17,"heroHeadline":38},"code-audit-and-project-takeover","Code audit and takeover","The project stalled. We'll take it over and tell you the truth about its condition",{"slug":40,"navLabel":41,"navGroup":17,"heroHeadline":42},"maintenance-and-support","Maintenance and support","A system needs someone watching it after launch",{"slug":44,"navLabel":45,"navGroup":46,"heroHeadline":47},"discovery-workshop","Discovery workshop","start","Before you decide to build, find out exactly what you're building",[49,53,57,61,65],{"slug":50,"navLabel":51,"heroHeadline":52},"manufacturing","Manufacturing","The works order reaches the floor as a printout from an email",{"slug":54,"navLabel":55,"heroHeadline":56},"automotive","Automotive","The customer exists in three places at once",{"slug":58,"navLabel":59,"heroHeadline":60},"industrial-automation","Industrial automation","You sell competence that doesn't photograph",{"slug":62,"navLabel":63,"heroHeadline":64},"e-commerce","E-commerce","The shop works. What sits behind it comes apart",{"slug":66,"navLabel":67,"heroHeadline":68},"logistics","Transport and logistics","The job arrives by email, the status comes back by phone",[70,77,82],{"slug":71,"client":72,"tag":73,"year":74,"summary":75,"image":76},"dss-by-caredo","Caredo","Product",2025,"We build the whole DSS product together with the Caredo team — from the CRM and planner to the software behind the key lockers and totems.","\u002Fimg\u002Fcase-studies\u002Fdss\u002Fkluczykomat\u002Fframe_0169.webp",{"slug":78,"client":72,"tag":79,"year":74,"summary":80,"image":81},"dss-by-caredo-website","Product website","We built the DSS product website end to end — from design to launch — on Caredo's visual identity.","\u002Fimg\u002Fscreenshots\u002Fdss-desktop.jpg",{"slug":83,"client":83,"tag":84,"year":74,"summary":85,"image":86},"2motion","Brand and website","A whole brand from scratch — logo, colours, business cards — a company website and blog posts that position 2motion in search.","\u002Fimg\u002Fscreenshots\u002F2motion-desktop.jpg",[88],{"code":89,"label":90,"name":91,"to":92},"pl","PL","Polish","\u002Fblog\u002Fwdrozenie-ai-w-firmie-od-czego-zaczac",{"post":94,"relatedPosts":337,"relatedServices":338},{"id":95,"title":96,"author":97,"body":98,"category":319,"date":320,"description":321,"draft":322,"editorialNote":323,"extension":324,"image":325,"meta":326,"navigation":327,"path":92,"rawbody":328,"seo":329,"sitemap":330,"stem":331,"tags":332,"translationKey":335,"updated":323,"__hash__":336},"blogEn\u002Fblog\u002Fwdrozenie-ai-w-firmie-od-czego-zaczac.md","AI adoption in your company — where to start","zespol",{"type":99,"value":100,"toc":306},"minimark",[101,118,123,126,133,137,140,146,152,158,161,165,172,175,179,182,188,194,200,206,210,213,235,238,242,268,272,278,284,290,296,300,303],[102,103,104,105,109,110,113,114,117],"p",{},"Start with a process that meets three conditions at once: it's ",[106,107,108],"strong",{},"repetitive",", has a ",[106,111,112],{},"countable time cost",", and ",[106,115,116],{},"tolerates a mistake",". Classifying incoming documents usually meets all three. \"An assistant that knows the whole company\" meets none of them — which is why so many rollouts that start there never finish.",[119,120,122],"h2",{"id":121},"why-pilots-dont-reach-production","Why pilots don't reach production",[102,124,125],{},"A demo shows a model handling a dozen or so examples chosen by the person who prepared them. Production is tens of thousands of examples nobody chose: scans crooked on the glass, documents in a format outside the test set, invoices with a handwritten note in the margin.",[102,127,128,129,132],{},"The difference between the two isn't model quality. It's ",[106,130,131],{},"handling the cases where the model isn't sure"," — and that's exactly what a rollout is actually made of. A pilot that skips it isn't an earlier version of the rollout. It's a different thing entirely.",[119,134,136],{"id":135},"how-to-choose-your-first-use-case","How to choose your first use case",[102,138,139],{},"Make a list of candidates and score each on three dimensions.",[102,141,142,145],{},[106,143,144],{},"Repeatability."," How many times a month does someone do this? Below a hundred, the savings rarely justify a rollout.",[102,147,148,151],{},[106,149,150],{},"Time cost."," How many minutes does one instance take? Multiply by repeatability. That number sets the upper bound on a sensible budget.",[102,153,154,157],{},[106,155,156],{},"Tolerance for error."," What happens if the model gets it wrong? If the answer is \"someone catches it at review\" — good. If it's \"money leaves an account\" — you need a human approval step, which changes the maths.",[102,159,160],{},"The best candidates in a typical company: classifying and routing incoming documents, extracting data from invoices and orders, drafting first responses to repetitive support questions, searching internal documentation.",[119,162,164],{"id":163},"set-a-success-metric-before-you-start","Set a success metric before you start",[102,166,167,168],{},"Before a prototype exists, write down one sentence: ",[169,170,171],"em",{},"\"This rollout makes sense if the model handles at least X% of cases on its own, at accuracy of at least Y%.\"",[102,173,174],{},"Without that number, there's no way to later decide whether the project succeeded. With it, the prototype ends in a clear decision: proceed, or stop. Stopping is also a good outcome — just a cheaper one than finding out a year in.",[119,176,178],{"id":177},"an-architecture-that-survives-production","An architecture that survives production",[102,180,181],{},"A rollout that lasts has four elements a demo doesn't.",[102,183,184,187],{},[106,185,186],{},"A confidence threshold."," The model returns a result along with its own confidence score. Above the threshold, automation runs; below it, the case goes to a human. Setting that threshold is a business decision, not a technical one.",[102,189,190,193],{},[106,191,192],{},"A queue for uncertain cases."," The place where a human resolves cases below the threshold — and its size is the real measure of how well the system is working.",[102,195,196,199],{},[106,197,198],{},"A decision log."," A record of what the model received, what it returned, and with what confidence. Without it you can't diagnose degradation or answer an auditor's question.",[102,201,202,205],{},[106,203,204],{},"Continuous measurement."," Data changes. A model that was 94% accurate in March can be at 80% by November — not because it broke, but because the input stream changed.",[119,207,209],{"id":208},"where-your-data-goes","Where your data goes",[102,211,212],{},"The question that comes up in every first meeting, rightly so. You have three options:",[214,215,216,223,229],"ul",{},[217,218,219,222],"li",{},[106,220,221],{},"A hosted model, no-training agreement."," Data leaves the company, but the provider commits not to use it for training. Cheapest and fastest, sufficient for most use cases.",[217,224,225,228],{},[106,226,227],{},"A hosted model in your own private cloud."," Data never leaves your infrastructure. More expensive, justified for sensitive data.",[217,230,231,234],{},[106,232,233],{},"A model on your own hardware."," Full control, the highest entry and running cost. Makes sense under regulatory requirements that rule out the other options.",[102,236,237],{},"The choice is a decision about risk, not technology. You make it once, and it affects cost for the entire lifetime of the solution.",[119,239,241],{"id":240},"what-it-costs","What it costs",[214,243,244,250,256,262],{},[217,245,246,249],{},[106,247,248],{},"Prototype on your data:"," PLN 15–30k net, 2–3 weeks. Ends with a number you can base a decision on.",[217,251,252,255],{},[106,253,254],{},"Production rollout:"," PLN 60–200k net, depending on the number of integrations and approval requirements.",[217,257,258,261],{},[106,259,260],{},"Running the models:"," from a few hundred to a few thousand zloty a month, roughly proportional to volume.",[217,263,264,267],{},[106,265,266],{},"Maintenance and tuning:"," usually 15% of the rollout's value per year.",[119,269,271],{"id":270},"four-mistakes-that-come-up-most-often","Four mistakes that come up most often",[102,273,274,277],{},[106,275,276],{},"Starting with the most impressive use case."," A conversational assistant impresses the board and is the hardest possible starting point — unbounded scope, no good success metric, high expectations.",[102,279,280,283],{},[106,281,282],{},"No human in the loop."," A system with no path for uncertain cases either performs badly or needs such a high confidence threshold that it only automates a small share of cases.",[102,285,286,289],{},[106,287,288],{},"Measuring accuracy alone."," 95% accuracy sounds great until you work out that at a thousand documents a day, that's fifty errors someone has to catch.",[102,291,292,295],{},[106,293,294],{},"Treating the rollout as a closed project."," A model needs watching, like any other piece of production infrastructure. A rollout with no measurement plan ages quietly.",[119,297,299],{"id":298},"the-first-step-you-can-take-this-week","The first step you can take this week",[102,301,302],{},"Pick one process. Count how many times a month it happens and how many minutes it takes. Write down what happens when someone gets it wrong.",[102,304,305],{},"Those three numbers are enough to judge whether a prototype is worth building — and they're worth more than a month of conversations about AI's possibilities.",{"title":307,"searchDepth":308,"depth":308,"links":309},"",3,[310,312,313,314,315,316,317,318],{"id":121,"depth":311,"text":122},2,{"id":135,"depth":311,"text":136},{"id":163,"depth":311,"text":164},{"id":177,"depth":311,"text":178},{"id":208,"depth":311,"text":209},{"id":240,"depth":311,"text":241},{"id":270,"depth":311,"text":271},{"id":298,"depth":311,"text":299},"ai","2026-09-02","How to choose your first use case, what it costs, and why most corporate AI projects end at the demo instead of reaching production.",false,null,"md","\u002Fimg\u002Fblog\u002Fwdrozenie-ai-w-firmie.jpg",{},true,"---\ntitle: AI adoption in your company — where to start\ndescription: How to choose your first use case, what it costs, and why most corporate AI projects end at the demo instead of reaching production.\ndate: 2026-09-02\ncategory: ai\ntags:\n  - ai\n  - automation\n  - rollouts\nauthor: zespol\nimage: \u002Fimg\u002Fblog\u002Fwdrozenie-ai-w-firmie.jpg\ntranslationKey: ai-adoption-where-to-start\ndraft: false\n---\n\nStart with a process that meets three conditions at once: it's **repetitive**, has a **countable time cost**, and **tolerates a mistake**. Classifying incoming documents usually meets all three. \"An assistant that knows the whole company\" meets none of them — which is why so many rollouts that start there never finish.\n\n## Why pilots don't reach production\n\nA demo shows a model handling a dozen or so examples chosen by the person who prepared them. Production is tens of thousands of examples nobody chose: scans crooked on the glass, documents in a format outside the test set, invoices with a handwritten note in the margin.\n\nThe difference between the two isn't model quality. It's **handling the cases where the model isn't sure** — and that's exactly what a rollout is actually made of. A pilot that skips it isn't an earlier version of the rollout. It's a different thing entirely.\n\n## How to choose your first use case\n\nMake a list of candidates and score each on three dimensions.\n\n**Repeatability.** How many times a month does someone do this? Below a hundred, the savings rarely justify a rollout.\n\n**Time cost.** How many minutes does one instance take? Multiply by repeatability. That number sets the upper bound on a sensible budget.\n\n**Tolerance for error.** What happens if the model gets it wrong? If the answer is \"someone catches it at review\" — good. If it's \"money leaves an account\" — you need a human approval step, which changes the maths.\n\nThe best candidates in a typical company: classifying and routing incoming documents, extracting data from invoices and orders, drafting first responses to repetitive support questions, searching internal documentation.\n\n## Set a success metric before you start\n\nBefore a prototype exists, write down one sentence: *\"This rollout makes sense if the model handles at least X% of cases on its own, at accuracy of at least Y%.\"*\n\nWithout that number, there's no way to later decide whether the project succeeded. With it, the prototype ends in a clear decision: proceed, or stop. Stopping is also a good outcome — just a cheaper one than finding out a year in.\n\n## An architecture that survives production\n\nA rollout that lasts has four elements a demo doesn't.\n\n**A confidence threshold.** The model returns a result along with its own confidence score. Above the threshold, automation runs; below it, the case goes to a human. Setting that threshold is a business decision, not a technical one.\n\n**A queue for uncertain cases.** The place where a human resolves cases below the threshold — and its size is the real measure of how well the system is working.\n\n**A decision log.** A record of what the model received, what it returned, and with what confidence. Without it you can't diagnose degradation or answer an auditor's question.\n\n**Continuous measurement.** Data changes. A model that was 94% accurate in March can be at 80% by November — not because it broke, but because the input stream changed.\n\n## Where your data goes\n\nThe question that comes up in every first meeting, rightly so. You have three options:\n\n- **A hosted model, no-training agreement.** Data leaves the company, but the provider commits not to use it for training. Cheapest and fastest, sufficient for most use cases.\n- **A hosted model in your own private cloud.** Data never leaves your infrastructure. More expensive, justified for sensitive data.\n- **A model on your own hardware.** Full control, the highest entry and running cost. Makes sense under regulatory requirements that rule out the other options.\n\nThe choice is a decision about risk, not technology. You make it once, and it affects cost for the entire lifetime of the solution.\n\n## What it costs\n\n- **Prototype on your data:** PLN 15–30k net, 2–3 weeks. Ends with a number you can base a decision on.\n- **Production rollout:** PLN 60–200k net, depending on the number of integrations and approval requirements.\n- **Running the models:** from a few hundred to a few thousand zloty a month, roughly proportional to volume.\n- **Maintenance and tuning:** usually 15% of the rollout's value per year.\n\n## Four mistakes that come up most often\n\n**Starting with the most impressive use case.** A conversational assistant impresses the board and is the hardest possible starting point — unbounded scope, no good success metric, high expectations.\n\n**No human in the loop.** A system with no path for uncertain cases either performs badly or needs such a high confidence threshold that it only automates a small share of cases.\n\n**Measuring accuracy alone.** 95% accuracy sounds great until you work out that at a thousand documents a day, that's fifty errors someone has to catch.\n\n**Treating the rollout as a closed project.** A model needs watching, like any other piece of production infrastructure. A rollout with no measurement plan ages quietly.\n\n## The first step you can take this week\n\nPick one process. Count how many times a month it happens and how many minutes it takes. Write down what happens when someone gets it wrong.\n\nThose three numbers are enough to judge whether a prototype is worth building — and they're worth more than a month of conversations about AI's possibilities.\n",{"title":96,"description":321},{"loc":92},"blog\u002Fwdrozenie-ai-w-firmie-od-czego-zaczac",[319,333,334],"automation","rollouts","ai-adoption-where-to-start","3h3Uyc_r6v-R1oggm3sXVR3s8arJjyy0xQy78uryQSg",[],[339,442],{"id":340,"title":341,"audience":342,"body":346,"description":369,"extension":324,"faq":370,"heroHeadline":22,"heroSubline":383,"keyword":341,"meta":384,"navGroup":17,"navLabel":21,"navigation":327,"order":385,"path":386,"pricingModels":387,"process":397,"relatedCase":71,"relatedPosts":414,"scope":416,"seo":429,"sitemap":430,"slug":20,"stem":440,"__hash__":441},"servicesEn\u002Fservices\u002Fai-implementation.md","AI implementation for business",[343,344,345],"Teams drowning in documents to read, classify and retype","Support teams answering the same question for the thousandth time","Companies with a lot of internal knowledge nobody can find",{"type":99,"value":347,"toc":365},[348,352,355,358,362],[119,349,351],{"id":350},"why-ai-pilots-dont-reach-production","Why AI pilots don't reach production",[102,353,354],{},"A demo shows a model handling ten examples chosen by the person who prepared them. Production is ten thousand examples nobody chose, including scans crooked on the glass and documents in a format that wasn't in the test set.",[102,356,357],{},"The difference between the two isn't model quality. It's handling the cases where the model isn't sure — and that handling is the real substance of a rollout.",[119,359,361],{"id":360},"what-we-measure","What we measure",[102,363,364],{},"Before we start, we establish how long the work takes today and what share of cases the automation needs to handle for the rollout to make sense. After launch, the same metric tells us whether it's working. Without it, \"AI in the company\" is a cost with no verifiable return.",{"title":307,"searchDepth":308,"depth":308,"links":366},[367,368],{"id":350,"depth":311,"text":351},{"id":360,"depth":311,"text":361},"We deploy AI where it genuinely shortens real work — document classification, search over internal knowledge, support assistants. No pilots that never reach production.",[371,374,377,380],{"q":372,"a":373},"Where do we start with AI in our company?","With one process that's repetitive, has a measurable time cost, and tolerates the occasional mistake. Classifying incoming documents usually meets all three. Starting with \"an assistant that knows everything\" is the most common way to get a project with no end.",{"q":375,"a":376},"Will my data go to a public model?","Not unless you want it to. We match the solution to your requirements — from hosted models under a no-training-on-data agreement, to models run on your own infrastructure.",{"q":378,"a":379},"What if the model gets it wrong?","We design assuming it will. Every rollout has a confidence threshold — above it, the automation runs; below it, the case goes to a human. Every model decision is logged and traceable.",{"q":381,"a":382},"What does it cost?","A prototype is usually PLN 15–30k net. A production rollout is PLN 60–200k, depending on the number of integrations. On top of that, the cost of running the models — typically hundreds to a few thousand zloty a month.","Most corporate AI projects end at the demo. We start by asking which repetitive work you want gone — and whether its disappearance can be measured.",{},4,"\u002Fservices\u002Fai-implementation",[388,391,394],{"name":389,"body":390},"Fixed-price prototype","Quoted separately and deliberately small. If accuracy comes in below the threshold, we stop — and that's a good outcome too, just a cheaper one than finding out a year in.",{"name":392,"body":393},"Fixed-price rollout","Once the prototype exists the scope is known, so we can give an amount and a deadline.",{"name":395,"body":396},"Maintenance retainer","Accuracy monitoring, tuning and model updates.",[398,402,406,410],{"step":399,"duration":400,"body":401},"Choosing the use case","1 week","We review the candidates and pick the one with the best ratio of time saved to risk. We define the success metric before starting.",{"step":403,"duration":404,"body":405},"Prototype on your data","2–3 weeks","A working prototype on real documents, with measured accuracy. This is the decision point on whether to continue.",{"step":407,"duration":408,"body":409},"Production rollout","4–10 weeks","Integration with your systems, handling of uncertain cases, permissions and a model decision log.",{"step":411,"duration":412,"body":413},"Measurement and tuning","ongoing","Accuracy measured continuously, because data changes — a model that worked in March may not work in November.",[415],"wdrozenie-ai-w-firmie-od-czego-zaczac",[417,420,423,426],{"title":418,"body":419},"Document classification and extraction","Invoices, contracts, orders and correspondence read automatically, with fields extracted into your system and a queue of uncertain cases for a human to decide.",{"title":421,"body":422},"Search over company knowledge","A question asked in plain language, an answer with a link to the source document. No making things up — if the answer isn't in the knowledge base, the system says so.",{"title":424,"body":425},"Customer support assistants","Answers to repetitive questions, handed off to a human the moment confidence drops.",{"title":427,"body":428},"Automation with a model in the loop","The model as one step in a process, not the whole process — with the output validated before it's written to the system.",{"title":341,"description":369},{"loc":431,"alternatives":432},"\u002Fen\u002Fservices\u002Fai-implementation",[433,436,438],{"hreflang":434,"href":435},"x-default","\u002Fuslugi\u002Fwdrozenia-ai",{"hreflang":437,"href":435},"pl-PL",{"hreflang":439,"href":431},"en-US","services\u002Fai-implementation","JRpvH1T996kqI1_eb4j8O7Zhl72twBMjnOeKMf2EEUU",{"id":443,"title":444,"audience":445,"body":449,"description":464,"extension":324,"faq":465,"heroHeadline":26,"heroSubline":478,"keyword":479,"meta":480,"navGroup":17,"navLabel":25,"navigation":327,"order":481,"path":482,"pricingModels":483,"process":490,"relatedCase":83,"relatedPosts":506,"scope":507,"seo":523,"sitemap":524,"slug":24,"stem":531,"__hash__":532},"servicesEn\u002Fservices\u002Fprocess-automation.md","Business process automation",[446,447,448],"Teams spending days on reports that get built the same way every month","Companies where approving a document means an email chain","Organisations hiring more people to do work a rule could do",{"type":99,"value":450,"toc":461},[451,455,458],[119,452,454],{"id":453},"automation-starts-with-a-spreadsheet-of-hours","Automation starts with a spreadsheet of hours",[102,456,457],{},"Before we write anything, we want to know how many times a month the process runs and how long it takes each time. That number tells us how much is reasonable to spend on automating it — and whether it's worth it at all.",[102,459,460],{},"Surprisingly often, the process that annoys everyone costs four hours a month — and the one nobody talks about, because \"it's always been done that way,\" eats two full-time roles.",{"title":307,"searchDepth":308,"depth":308,"links":462},[463],{"id":453,"depth":311,"text":454},"We remove repetitive manual work — document flows, reports, retyping data between systems. We start with the process that costs the most hours.",[466,469,472,475],{"q":467,"a":468},"Which process should we start with?","The one performed most often with the fewest exceptions — not the most annoying one. Annoyance and cost are two different things, and automating a process full of exceptions can end up more expensive than doing it by hand.",{"q":470,"a":471},"Does automation mean layoffs?","In practice, usually not. What disappears is work nobody wanted to do, and the team takes on things there was never time for before. If the goal is headcount reduction, we'll say honestly whether a given process can carry that.",{"q":473,"a":474},"How long until it pays for itself?","With a well-chosen process, usually 6–12 months. That's why we start by counting hours — without that, it's guesswork.",{"q":476,"a":477},"How is this different from off-the-shelf no-code tools?","It isn't, if your process fits what they can do — then we'll point you to a tool instead of writing code. The difference shows up with rules those tools can't express, and at volumes where they start getting expensive.","Every company has a few processes that eat up a full-time role and amount to moving information from one place to another.","business process automation",{},5,"\u002Fservices\u002Fprocess-automation",[484,487],{"name":485,"body":486},"Fixed price per process","One process, known scope, known price. The most common and safest model.",{"name":488,"body":489},"Automation retainer","A fixed monthly pool of hours for further automations, for companies that want to go process by process.",[491,494,498,502],{"step":492,"duration":400,"body":493},"Audit and process selection","We review the candidates and pick the one with the best savings-to-difficulty ratio.",{"step":495,"duration":496,"body":497},"Rule design","1–2 weeks","We write down the edge cases and exceptions. They decide success, not the happy path.",{"step":499,"duration":500,"body":501},"Rollout","2–6 weeks","The automation runs alongside the manual process, so results can be compared.",{"step":503,"duration":504,"body":505},"Switchover and expansion","2–4 weeks","We turn off the manual version and move on to the next process.",[415],[508,511,514,517,520],{"title":509,"body":510},"Process map and hour count","We start by counting how much time the process costs today. Without that number, there's no way to judge whether automating it pays off.",{"title":512,"body":513},"Document and approval flows","Requests, orders and invoices with approval rules, reminders and a decision history.",{"title":515,"body":516},"Automatically generated reports","The summaries a person compiles today, built by themselves and delivered wherever they need to land.",{"title":518,"body":519},"Data synchronisation","No more retyping the same record into three systems.",{"title":521,"body":522},"Notifications and escalations","The system watches deadlines instead of a person who has to remember them.",{"title":444,"description":464},{"loc":525,"alternatives":526},"\u002Fen\u002Fservices\u002Fprocess-automation",[527,529,530],{"hreflang":434,"href":528},"\u002Fuslugi\u002Fautomatyzacja-procesow",{"hreflang":437,"href":528},{"hreflang":439,"href":525},"services\u002Fprocess-automation","dWXLPn_QIMw3rILavszqeESlo1LijXwS70wlg6LmA0M",1790522546028]