{"id":282,"date":"2026-01-21T13:40:09","date_gmt":"2026-01-21T12:40:09","guid":{"rendered":"https:\/\/www.genexio.net\/blog\/?p=282"},"modified":"2026-01-21T15:23:57","modified_gmt":"2026-01-21T14:23:57","slug":"from-web-lists-to-real-customers-building-my-customers_research-app-with-filemaker-python-and-ai-just-for-fun","status":"publish","type":"post","link":"https:\/\/www.genexio.net\/blog\/2026\/01\/21\/from-web-lists-to-real-customers-building-my-customers_research-app-with-filemaker-python-and-ai-just-for-fun\/","title":{"rendered":"From Web Lists to Real Customers: Building my customers_research App with FileMaker, Python, and AI (Just for Fun)"},"content":{"rendered":"\n

<\/p>\n\n\n\n

Note:<\/strong> I do not build software as my main job. I do it because I enjoy it. I like small wins: a clean import, a good AI score, a ready-to-send email. This article explains how I built my customers_research<\/em> app using FileMaker as the core, plus a bit of Python and AI via API.<\/p>\n\n\n\n

Why I built the customers_research app<\/h4>\n\n\n\n

I needed a practical way to find good B2B prospects for CNC machining services. Finding companies is easy. Finding the right ones is not.<\/p>\n\n\n\n

So I built a workflow that helps me to:<\/p>\n\n\n\n

    \n
  • import lead lists from the web (trade fairs, directories, PDF\/Excel sources)<\/li>\n\n\n\n
  • avoid duplicates and keep research history visible<\/li>\n\n\n\n
  • browse and evaluate company websites directly inside FileMaker<\/li>\n\n\n\n
  • use AI to score leads and explain their relevance<\/li>\n\n\n\n
  • prepare emails (first contact and follow-up) and log every action<\/li>\n<\/ul>\n\n\n\n

    FileMaker is the core: database, UI, and logic<\/h4>\n\n\n\n

    FileMaker is the foundation of the system. It stores leads, manages the process, and provides a fast and flexible user interface. It is also the tool I know best.<\/p>\n\n\n\n

    A very practical feature: when I import a new list, contacts already present in the database are immediately highlighted in red<\/strong>. This is done using a Set Script Trigger<\/strong>, so the research history is visible at a glance: what is new, what was already evaluated, and what was already contacted.<\/p>\n\n\n\n

    Importing data by drag & drop into a FileMaker container<\/h4>\n\n\n\n

    The import process is intentionally simple. I just drag and drop a PDF or Excel file into a FileMaker container field<\/strong>.<\/p>\n\n\n\n

    From there:<\/p>\n\n\n\n

      \n
    • FileMaker detects the file<\/li>\n\n\n\n
    • a script extracts or processes the content<\/li>\n\n\n\n
    • data is normalized and prepared for analysis<\/li>\n<\/ul>\n\n\n\n

      This approach avoids complex import dialogs and keeps the workflow fast and intuitive.<\/p>\n\n\n\n

      Deduplication and history checks with ExecuteSQL<\/h4>\n\n\n\n

      To keep the database clean, FileMaker checks if a company (or an email\/domain) already exists. For fast checks I often use ExecuteSQL<\/strong>.<\/p>\n\n\n\n

      \/\/ Example (concept): check if an email already exists\nLet ( [\n  _email = leads::email ;\n  _sql   = \"SELECT COUNT(*) FROM Contacts WHERE email = ?\" ;\n  _cnt   = ExecuteSQL ( _sql ; \"\" ; \"\" ; _email )\n] ;\n  Case ( GetAsNumber ( _cnt ) > 0 ; \"EXISTS\" ; \"NEW\" )\n)<\/code><\/pre>\n\n\n\n
      This logic supports both deduplication and the red-highlight visual feedback during list review.<\/p>\n\n\n\n
      Fast \u201chuman\u201d research with Find and Web Viewer<\/h4>\n\n\n\n
      Automation is important, but I always want a quick human check.<\/p>\n\n\n\n
      Inside FileMaker I can:<\/p>\n\n\n\n
        \n
      • use Find<\/strong> to filter leads by city, sector keywords, status, or score range<\/li>\n\n\n\n
      • open the company website directly in FileMaker using the Web Viewer<\/strong><\/li>\n<\/ul>\n\n\n\n
        \/\/ Example script steps (concept)\nSet Variable [ $url ; Value: leads::website ]\nSet Web Viewer [ Object Name: \"wv_site\" ; URL: $url ]\n<\/code><\/pre>\n\n\n\n
        This allows me to scan a website quickly and decide whether the lead deserves AI analysis or direct contact.<\/p>\n\n\n\n
        Python for automation (a part I really enjoy)<\/h4>\n\n\n\n
        Python is mainly used for automation:<\/p>\n\n\n\n
          \n
        • downloading website pages<\/li>\n\n\n\n
        • extracting relevant text<\/li>\n\n\n\n
        • detecting email addresses<\/li>\n<\/ul>\n\n\n\n
          I am not a Python developer, but this is the part I really enjoy. AI helped me write, understand, and improve the code step by step.<\/p>\n\n\n\n
          import re\n\nemails = re.findall(\n    r\"[A-Z0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,}\",\n    page_text,\n    re.I\n)<\/code><\/pre>\n\n\n\n
          Simple code, but very effective.<\/p>\n\n\n\n
          What AI really does in this app<\/h4>\n\n\n\n
          AI has a clear and limited role. It reads the website text and returns an evaluation.<\/p>\n\n\n\n
          It does not decide for me. It provides structured output:<\/p>\n\n\n\n
            \n
          • a score (0\u2013100)<\/li>\n\n\n\n
          • a category (strong match \/ possible \/ unclear \/ unlikely)<\/li>\n\n\n\n
          • a short explanation<\/li>\n<\/ul>\n\n\n\n
            A simplified prompt example (conceptual):<\/p>\n\n\n\n
              \n
            • analyze the company website text<\/li>\n\n\n\n
            • estimate the probability that the company may need external CNC machining services<\/li>\n\n\n\n
            • explain the reasoning briefly<\/li>\n\n\n\n
            • do not invent information<\/li>\n\n\n\n
            • return JSON with score, category, and reasoning<\/li>\n<\/ul>\n\n\n\n
              Calling the AI API from FileMaker<\/h4>\n\n\n\n
              AI calls are made directly from FileMaker.<\/p>\n\n\n\n
              The typical flow is:<\/p>\n\n\n\n
                \n
              • build the JSON payload using FileMaker JSON functions (JSONSetElement)<\/li>\n\n\n\n
              • send the request with Insert from URL<\/strong> and cURL options<\/li>\n\n\n\n
              • parse the JSON response and store values in FileMaker fields<\/li>\n<\/ul>\n\n\n\n
                Raw responses are saved in a log table, together with defensive checks for empty or invalid JSON.<\/p>\n\n\n\n
                Email workflow: prepared by AI, sent by FileMaker<\/h4>\n\n\n\n
                When a lead is interesting enough, AI prepares the first contact email. I review it, optionally edit it, and send it using FileMaker\u2019s Send Mail<\/strong> function.<\/p>\n\n\n\n
                The system already includes:<\/p>\n\n\n\n
                  \n
                • a standard email signature<\/li>\n\n\n\n
                • optional attachment support<\/li>\n\n\n\n
                • a second follow-up email prepared in advance<\/li>\n<\/ul>\n\n\n\n
                  If everything looks good, sending the email takes just one click.<\/p>\n\n\n\n
                  Logging: every action goes into a dedicated table<\/h4>\n\n\n\n
                  Every step is logged:<\/p>\n\n\n\n
                    \n
                  • import source (trade fair, directory, PDF, Excel)<\/li>\n\n\n\n
                  • deduplication status (new or existing)<\/li>\n\n\n\n
                  • AI score, reasoning, and raw response<\/li>\n\n\n\n
                  • email draft, send date\/time, follow-up status<\/li>\n<\/ul>\n\n\n\n
                    This builds a complete and reliable research history over time.<\/p>\n\n\n\n
                    From research lead to the official contacts database<\/h4>\n\n\n\n
                    When a company is confirmed as interesting, the lead is moved into the official FileMaker contacts database.<\/p>\n\n\n\n
                    From there:<\/p>\n\n\n\n
                      \n
                    • it enters a dedicated contacts list<\/li>\n\n\n\n
                    • it can be used for future communications<\/li>\n\n\n\n
                    • it becomes part of the company\u2019s long-term knowledge base<\/li>\n<\/ul>\n\n\n\n
                      Small technical wins, big satisfaction<\/h4>\n\n\n\n
                      Even small results are rewarding: a red highlight that immediately shows history, a clean ExecuteSQL check, a working API call, a good AI explanation, or an email ready to send.<\/p>\n\n\n\n
                      That is why I enjoy building tools like this: they are useful, but also genuinely fun.<\/p>\n\n\n\n
                      Final thoughts<\/h4>\n\n\n\n
                      customers_research<\/em> is not a commercial product. It is a practical project built from curiosity, passion for code, and real business needs.<\/p>\n\n\n\n
                      FileMaker is the base, Python supports automation, and AI adds an intelligent layer for evaluation and communication. Step by step, it has become a system I actually use every day.<\/p>\n\n\n\n
                      \n
                      \"IMG<\/figure>\n\n\n\n
                      \"IMG<\/figure>\n\n\n\n
                      \"IMG<\/figure>\n\n\n\n
                      \"IMG<\/figure>\n\n\n\n
                      \"IMG<\/figure>\n\n\n\n
                      \"IMG<\/figure>\n\n\n\n
                      \"IMG<\/figure>\n\n\n\n
                      \"IMG<\/figure>\n<\/figure>\n","protected":false},"excerpt":{"rendered":"
                      Note: I do not build software as my main job. I do it because I enjoy it. I like small wins: a clean import, a […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17,5,15,16],"tags":[],"class_list":["post-282","post","type-post","status-publish","format-standard","hentry","category-ai","category-filemaker","category-node-red","category-python"],"yoast_head":"\nFrom Web Lists to Real Customers: Building my customers_research App with FileMaker, Python, and AI (Just for Fun) - My blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.genexio.net\/blog\/2026\/01\/21\/from-web-lists-to-real-customers-building-my-customers_research-app-with-filemaker-python-and-ai-just-for-fun\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"From Web Lists to Real Customers: Building my customers_research App with FileMaker, Python, and AI (Just for Fun) - My blog\" \/>\n<meta property=\"og:description\" content=\"Note: I do not build software as my main job. 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