AI development for support, data analysis and business automation
AI becomes valuable for a business when it is not just a showcase tool, but actually makes part of the work easier, faster or more accurate. Tiny Developers designs AI solutions around your real data, needs and workflows.؛ From intelligent assistants and chatbots to automated support systems, data analysis, smart search and AI integration with apps or websites.
Why does AI matter for your business?
Many businesses deal with a high volume of repetitive questions, scattered information, the need for fast support, data analysis and internal knowledge management. When this information is organized inside an intelligent system, users receive answers faster and the human team can focus on more important work.
Our goal at Tiny Developers is to bring AI into your product in a practical way.؛ This means designing a solution that works with your data, your workflows and the real needs of the project, not a generic tool detached from the system.
AI becomes effective when it is connected to the product, the data and the user journey,؛ not when it is just a simple chat widget in the corner of a website.
What types of AI solutions do we develop?
Depending on the needs of the project, we can design different intelligent systems,؛ from a simple chatbot to a professional assistant connected to internal data, including smart search, text analysis, response generation, request classification and AI integration with an app, website or custom system.
Chatbots and smart assistants
Designing a responsive assistant for a website or app that can answer user questions and make the service journey easier.
Responses based on internal data
Using documents, files, FAQs, rules, product information or organizational data to generate more accurate responses.
Smart search
Designing a search system that understands the meaning of a user request instead of simply matching words, and returns more relevant answers.
Request analysis and classification
Analyzing user messages, requests or data to identify the topic, priority and type of need, then route it to a more suitable path.
AI integration with apps and websites
Implementing the API and infrastructure required to use AI capabilities inside a mobile app, website or admin panel.
Automation of repetitive processes
Reducing repetitive tasks such as initial support, user guidance, text generation, information organization or data-based recommendations.
AI project implementation process
AI projects usually do not produce accurate and reliable results if they start without understanding the data and the real need. That is why we first examine the problem, the information sources, user types, access levels, response flow and limitations.
Understanding the problem and automation goal
We define exactly what the AI should do: support, guidance, analysis, search, recommendations or automation.
Reviewing data and internal resources
Documents, text, files, product information, rules and available data are reviewed to define what knowledge the system should use.
Architecture design and API implementation
The response infrastructure, model, database, cache, queue, API and connection to the website or app are designed and implemented.
Testing, optimization and response quality control
Responses are reviewed, corrected and optimized so the system performs better in real conditions and produces more reliable output.
AI-related portfolio examples
Tiny Developers has used intelligent solutions in different projects for support, user guidance, knowledge management and improved user experience. These examples show that AI can become a real advantage when connected to an app, website or custom system.
Hoshino
A business AI assistant for automated support, internal-data usage and integration with websites or apps.
Vamino
A finance app with a smart guide that helps users review loans, terms, documents and make better financial decisions.
Erena
A team collaboration and management platform that can be expanded with smart features for notifications, follow-up and organizational coordination.
Hooshmak
A language-learning product that can be expanded with smart learning paths, practice recommendations and learner progress analysis.
What information is needed to start an AI project?
For a more accurate estimate, it is best to provide the type of problem, system goal, information sources, user types, data volume, need for website or app integration, the expected accuracy level and a sample of the desired output.
If you are not sure which part of your business can be made smarter, simply describe your workflow,؛ and we will suggest suitable paths.
Do you want to make your business smarter with AI?
Send your need, data or idea to the Tiny Developers team. After review, the proposed implementation path, required features, estimated timeline and cost estimate will be shared with you.