{"id":29366,"date":"2024-01-14T17:52:00","date_gmt":"2024-01-14T16:52:00","guid":{"rendered":"https:\/\/www.web-systems.pl\/choose-an-ai-application-company-for-your-business\/"},"modified":"2024-01-14T17:52:00","modified_gmt":"2024-01-14T16:52:00","slug":"choose-an-ai-application-company-for-your-business","status":"publish","type":"post","link":"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/","title":{"rendered":"How to choose an AI application company for your business?"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">The AI market is moving at breakneck speed. Only a few years ago nobody expected companies to be hunting en masse for vendors to build <a href=\"https:\/\/www.web-systems.pl\/en\/development-of-artificial-intelligence-based-applications\/\">applications based on language models<\/a>, automations or recommendation systems. Yet here we are. And that is where the problem starts, because picking a vendor for an AI project is not the same as buying a tool off the shelf. It is an architectural and business decision that will weigh on the organization for years. Pick the wrong team? Burned budget, a solution nobody can maintain or, worse still, dependence on a single supplier with no way out. At Web Systems we have been in this market since 2006 and we have seen more than once how companies lost months of work because they never verified the vendor&#8217;s technical skills. That is why we put together a concrete guide: which criteria to apply, what to look for in a proposal and which mistakes to avoid before you sign a contract for an artificial intelligence project.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_86 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Spis tre\u015bci<\/p>\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/#Why_choosing_an_AI_application_vendor_is_not_the_same_as_ordering_regular_software\" >Why choosing an AI application vendor is not the same as ordering regular software<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/#Key_technical_skills_you_should_demand\" >Key technical skills you should demand<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/#Architecture_and_scalability_the_questions_you_have_to_ask_before_signing\" >Architecture and scalability: the questions you have to ask before signing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/#Data_security_and_compliance_in_AI_projects\" >Data security and compliance in AI projects<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/#Engagement_model_and_costs_what_to_watch_out_for_in_proposals\" >Engagement model and costs: what to watch out for in proposals<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/#Frequently_asked_questions_FAQ\" >Frequently asked questions (FAQ)<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/#How_much_does_building_an_AI_application_cost\" >How much does building an AI application cost?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/#How_long_does_an_AI_application_rollout_take\" >How long does an AI application rollout take?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/#Can_I_integrate_AI_with_my_current_system\" >Can I integrate AI with my current system?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.web-systems.pl\/en\/choose-an-ai-application-company-for-your-business\/#Summary_a_technology_partner_not_just_a_vendor\" >Summary: a technology partner, not just a vendor<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Why_choosing_an_AI_application_vendor_is_not_the_same_as_ordering_regular_software\"><\/span>Why choosing an AI application vendor is not the same as ordering regular software<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Classic development projects &#8211; portals, CRM systems, online stores &#8211; rely on known patterns and predictable technologies. AI is a completely different story. Here you need skills that sit at the intersection of data engineering, machine learning, integration with external language models and a solid backend able to handle complex processing pipelines. A team that has so far been putting up WordPress sites or simple CRUD applications will not deliver that.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The risks of a poor vendor choice are specific to AI projects. And far more serious than with ordinary software. Vendor lock-in? It appears when a company ties the solution to a single cloud provider or a specific API with no abstraction layer. The lack of scalability shows up the moment the number of model requests grows and the architecture provides for neither queuing nor response caching. API token costs can surprise even experienced organizations: if nobody planned usage monitoring, the invoice can make your head spin. And then there are model hallucinations, generated nonsense that, without validation and grounding mechanisms, goes straight to end users.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"><p>Gartner research, which combines in-depth proprietary studies, analysis of industry best practices, quantitative modeling and trend analysis, makes it possible to develop innovative approaches that support stronger and more sustainable business results.<\/p><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">That illustrates nicely why technology decisions call for solid analysis rather than following a trend. Because there is a fundamental difference between a company that wraps a ready-made ChatGPT API in a simple interface and a team designing solutions with their own RAG architecture, answer quality evaluation and control over data flow. The first approach works at a hackathon. The second works in a production system serving customers. When you are looking for a vendor, ask directly: do you build your own pipelines, or do you just paste prompts into somebody else&#8217;s API? The answer will tell you more than a portfolio full of vague project descriptions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Key_technical_skills_you_should_demand\"><\/span>Key technical skills you should demand<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Before you start comparing price quotes, prepare a list of skills the vendor has to prove. Not declare in a slide deck: demonstrate on concrete examples. An AI project is no place for trial-and-error learning funded by your budget. Below are the minimum technical requirements every serious candidate should meet.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Experience with large language models (LLM)<\/strong> &#8211; including fine-tuning, prompt engineering and the ability to match a model to a specific use case<\/li>\n<li><strong>Retrieval-Augmented Generation (RAG) and vector databases<\/strong> &#8211; designing semantic search pipelines, document chunking, indexing and optimizing result relevance<\/li>\n<li><strong>API integrations and middleware<\/strong> &#8211; connecting AI models with existing company systems, databases, CRM systems and e-commerce platforms<\/li>\n<li><strong>Data security<\/strong> &#8211; encryption, access control, anonymization of personal data before it is sent to external models<\/li>\n<li><strong>DevOps and MLOps<\/strong> &#8211; deployment automation, model performance monitoring, version management for prompts and pipelines<\/li>\n<li><strong>Answer quality evaluation<\/strong> &#8211; measuring groundedness, coherence, fluency and other metrics that make it possible to assess generated content objectively<\/li>\n<\/ul>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"><p>The retrieval mechanism in a RAG architecture plays a critical role. You need the best semantic search on top of a carefully curated knowledge base so that the retrieved information is genuinely relevant to the user&#8217;s query. If the retrieved data turns out to be inadequate, the generated answer may be grammatically correct yet factually wrong or completely off topic.<\/p><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Tip:<\/strong> during a conversation with a prospective vendor, ask directly how they measure the quality of generated answers. A company that cannot name specific metrics &#8211; groundedness (support in the sources), coherence (logical consistency), fluency (linguistic smoothness) &#8211; most likely does not evaluate its solutions systematically. That should raise a red flag.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At Web Systems we use a RAG Ops approach built on iterative optimization of every element of the pipeline. What does that mean in practice? Continuous improvement of document chunking strategies, parsing of various source formats and prompt tuning based on real quality metrics. Not a one-off configuration that loses its effectiveness after a month because the input data has changed. A gradual increase in answer relevance driven by hard data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Architecture_and_scalability_the_questions_you_have_to_ask_before_signing\"><\/span>Architecture and scalability: the questions you have to ask before signing<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The architecture of an AI application determines not only what the system can do today, but also whether it can be developed tomorrow. A well-designed system separates the individual layers: user interface, business logic, data layer and the domain layer responsible for integration with AI models. Without that separation you end up with a monolith in which touching one component forces a rewrite of the whole thing. And in AI projects that hurts even more, because models, APIs and data processing strategies change far more often than in classic software.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Two architectural principles are worth keeping on your radar when you assess a vendor. The first is Single Source of Truth. Every type of data in the application should have one unambiguous source of truth. That way you avoid situations where different parts of the system operate on inconsistent versions of the same information (and believe us, debugging that kind of mess is a nightmare). The second is Unidirectional Data Flow. Application state flows in one direction, from the data source to the interface, and user events travel back to the source. This drastically reduces data synchronization errors and makes debugging easier. In AI systems it is especially useful, because model answers are non-deterministic by nature.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Tip:<\/strong> ask a prospective vendor for an architecture diagram of the solution before the quote. A serious technology company will gladly prepare one, because it needs that document itself in order to estimate reliably. If the vendor avoids this step or claims the architecture will emerge along the way, walk away.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Before you sign, ask a few concrete questions about the infrastructure. Where will the models be hosted: public cloud, private cloud, the client&#8217;s own servers? What does the fallback mechanism look like when the main model API goes down? How is the data processing pipeline built, from the moment a user query arrives to the moment an answer is returned? And how does the solution scale as the number of users and the data volume grow? A lack of clear answers to these questions means one thing: the vendor has not thought the architecture through well enough to guarantee stable operation in production.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Data_security_and_compliance_in_AI_projects\"><\/span>Data security and compliance in AI projects<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Every AI application processes data. Often sensitive, confidential or legally regulated data. Company documents, customer correspondence, financial data, employee information: all of it goes into language models as query context. Which raises the question: who controls that flow? Because if the vendor uses external APIs without proper safeguards, your company&#8217;s data can end up on the model provider&#8217;s servers. Under a retention policy you have no control over. A responsible technology partner will propose an intermediate layer that filters and anonymizes data before it is sent to an external model.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The GDPR imposes specific obligations on every organization that processes personal data, and integration with AI models adds further complications. Personal data should not reach external APIs in plain form: anonymization or pseudonymization before sending is a must. The vendor should state clearly which data leaves the client&#8217;s infrastructure, on what legal basis and what the deletion-on-request process looks like. Also check whether the model provider (OpenAI, Anthropic, Google and so on) offers an API variant that does not train on submitted data. That is standard in enterprise solutions, but not every vendor mentions it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Cloud or on-premise? It depends on the specifics of the organization. The cloud gives you flexibility and lower costs at the start, but it involves transferring data outside the company&#8217;s infrastructure. Self-hosting means full control over the data, but you need your own maintenance team and higher upfront spend. For regulated industries &#8211; finance, healthcare, the public sector &#8211; on-premise is sometimes the only acceptable path. There is no point pretending otherwise. A good vendor will present both scenarios with an honest analysis of costs and risks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Auditability is another dimension of security that plenty of suppliers simply forget. An AI system should log every query, every model answer, the sources used and the prompt version that produced a given result. Without that you will not diagnose why the system gave a wrong answer, nor prove to regulators that the solution works the way you claim. Prompt versioning and source tracking (grounding) should be built into the architecture from day one. Not bolted on as a patch after an incident.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Engagement_model_and_costs_what_to_watch_out_for_in_proposals\"><\/span>Engagement model and costs: what to watch out for in proposals<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">AI projects also differ from classic software when it comes to the billing model. The two most popular approaches &#8211; time and material as well as fixed price &#8211; carry different implications here than in standard development. A rigid upfront quote? Often a trap. AI projects contain exploratory elements by nature: model selection, prompt optimization, iterative improvement of answer quality. No honest vendor will name an exact price for reaching a specific level of model accuracy, because it depends on data quality, the specifics of the domain and dozens of variables that only surface during the work. A time and material model with defined milestones and regular reviews gives both sides more flexibility and more honesty.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Beyond the visible development cost, AI projects generate expenses many clients do not anticipate at the start. API tokens, the fee for every request to a language model, can grow explosively at high volume. The infrastructure needs maintenance: servers, vector databases, queuing and monitoring systems. Over time models require retraining or prompt updates, because source data and user expectations change. Drift monitoring, meaning the gradual degradation of answer quality, is a continuous process rather than a one-off. A vendor who does not account for these costs in the proposal? Either does not understand them, or is deliberately lowballing the estimate.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A sensible way to start is an MVP: a minimum working product that validates the business idea before you invest in the full solution. At Web Systems we recommend this path to most clients. We start with one well-defined use case, build a working prototype, measure its effectiveness and only then, based on real data, decide about further development. It protects the budget. It lets you verify quickly whether the direction makes sense.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ownership of code and models deserves its own paragraph in every contract. Make sure that once the project ends, the source code, the configured pipelines, the trained model adapters and the technical documentation remain your property. The vendor should not hold back elements without which you cannot develop or maintain the system on your own. The absence of such a clause is a straight road to supplier dependence. And getting out of that situation can cost more than the project itself.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Frequently_asked_questions_FAQ\"><\/span>Frequently asked questions (FAQ)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_much_does_building_an_AI_application_cost\"><\/span>How much does building an AI application cost?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The range is enormous. A simple MVP based on an off-the-shelf language model with a knowledge base and a chat interface costs in the region of several dozen thousand PLN. Extensive enterprise systems with their own RAG architecture, multiple integrations, an admin panel and security mechanisms can cost several hundred thousand or more. The variables? The number of data sources, the required level of model customization, the expected traffic scale and compliance requirements. Instead of asking about price in the abstract, it is better to describe a specific use case: then the estimate will be meaningful.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_long_does_an_AI_application_rollout_take\"><\/span>How long does an AI application rollout take?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A typical project goes through several stages. The discovery and architecture design phase takes two to four weeks for requirements analysis, technology selection and preparing a plan. Building the MVP usually takes six to twelve weeks, depending on complexity. Then come optimization iterations: improving answer quality, tuning the pipeline, testing with real users. A full production rollout with integrations and security measures is a three to six month horizon. But projects run without a clear methodology? Those can drag on much longer.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Can_I_integrate_AI_with_my_current_system\"><\/span>Can I integrate AI with my current system?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. And it is one of the most common needs we come across. Integration usually happens through APIs: the AI layer communicates with the existing system (ERP, CRM, e-commerce platform, document repository) via well-defined interfaces. In more complex cases we use middleware or an orchestration layer that coordinates the flow of data between multiple systems. A modular approach lets you roll out AI gradually, starting with a single business process, without rebuilding the entire IT infrastructure. It does, however, require solid technical analysis up front, so that you avoid version conflicts, duplicated data or performance problems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Summary_a_technology_partner_not_just_a_vendor\"><\/span>Summary: a technology partner, not just a vendor<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Choosing a company that builds AI applications is a decision that goes far beyond comparing hourly rates or lists of technologies in a proposal. You are looking for a partner for months or years: someone who understands not only language models, but also your business domain, regulatory constraints and budget realities. A one-off purchase of a ready-made solution works for simple tools. But AI systems require continuous care: quality monitoring, source data updates, prompt tuning and responding to changing user needs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">What should you look at? Four pillars. First, documented technical skills: experience with LLMs, RAG, vector databases and answer quality evaluation. Second, a transparent architecture with separated layers, a clear data flow and a scalability plan. Third, security: control over data flow, GDPR compliance, auditability and prompt versioning. Fourth, an honest cost model that accounts not only for development, but also for maintenance, API tokens and further system development.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If you are planning an AI application rollout in your organization, building an MVP to verify a business idea, integrating artificial intelligence with existing systems or automating processes, let us talk. At Web Systems we have been combining <a href=\"https:\/\/www.web-systems.pl\/en\/software-development\/\">solid programming craft<\/a> with a pragmatic approach to new technologies since 2006. We will gladly analyze your case and suggest where to start so that an investment in AI brings real results.<\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>The AI market is moving at breakneck speed. Only a few years ago nobody expected companies to be hunting en masse for vendors to build applications based on language models, automations or recommendation systems. Yet here we are. And that is where the problem starts, because picking a vendor for an AI project is not [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":28112,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[816,810,820],"tags":[861,1238,1081,1663,1204,1132,102,1419],"class_list":["post-29366","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-applications","category-artificial-intelligence","category-business","tag-ai-apps","tag-business-en","tag-guide","tag-it-outsourcing","tag-machine-learning-en-2","tag-software-house-en","tag-sztuczna-inteligencja-en","tag-technology"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/posts\/29366","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/comments?post=29366"}],"version-history":[{"count":0,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/posts\/29366\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/media\/28112"}],"wp:attachment":[{"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/media?parent=29366"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/categories?post=29366"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/tags?post=29366"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}