{"id":25106,"date":"2026-01-15T14:02:57","date_gmt":"2026-01-15T13:02:57","guid":{"rendered":"https:\/\/onder.nl\/?post_type=cases&p=25106"},"modified":"2026-01-22T13:55:38","modified_gmt":"2026-01-22T12:55:38","slug":"custom-gpts-for-specialist-content-quality-over-quantity","status":"publish","type":"cases","link":"https:\/\/onder.nl\/en\/cases\/custom-gpts-voor-specialistische-content-kwaliteit-boven-kwantiteit\/","title":{"rendered":"Custom GPTs for specialist content: quality over quantity"},"content":{"rendered":"
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At a glance<\/h2>\n\n\n\n

Challenge<\/h2>\n\n\n\n
    \n
  • Specialist content is too complex for content specialists<\/strong>, making pieces superficial or even incorrect<\/li>\n\n\n\n
  • Content specialists at clients do not have time or writing skills<\/strong> to produce your own content<\/li>\n\n\n\n
  • Up-to-date, legal and technical correctness<\/strong> is necessary but difficult to ensure<\/li>\n\n\n\n
  • AI-generated content<\/strong> often results in generic \u2018AI slop\u2019 zonder draught<\/li>\n<\/ul>\n\n\n\n

    Results<\/h2>\n\n\n\n
      \n
    • Law firm<\/strong>: Lawyer-level content zonder lawyer intervention, rankings in Google as well as LLMs, measurable increase in requests within specialisms<\/li>\n\n\n\n
    • Leasing company<\/strong>: From generic to specific content, significant growth in requests and organic traffic on relevant clusters<\/li>\n\n\n\n
    • Banking software<\/strong>: Complex technical content made accessible, early signs of organic growth<\/li>\n<\/ul>\n<\/div>\n\n\n\n
      \n

      Starting point<\/h2>\n\n\n\n

      We encountered this problem in three different projects, but the challenge was the same each time:<\/p>\n\n\n\n

      At the law firm<\/strong> we wrote blog articles and service pages on specialist areas of law. A standard AI would hallucinate. A content specialist wouldn't go deep enough. And the lawyer didn't have time to write \u00e1nd edit everything himself. The result? Slow content production or superficial pieces.<\/p>\n\n\n\n

      At the leasing company<\/strong> another agency produced generic content that was not about anything. For financial leasing, you need specific knowledge: BPM regulations 2026, addition per brand, why onder buyers choose certain models. Content about \u201cleasing Audi\u201d zonder that context is worthless.<\/p>\n\n\n\n

      At the banking software customer<\/strong> we were dealing with highly technical material - cloud lending software, risk management tools - that we, as content specialists, simply did not understand. The product owners could explain it, but not write it down in a way that appealed to their target audience (banks, financial institutions).<\/p>\n\n\n\n

      In all three cases, the hypothesis was: what if we build an AI that can empathise with the field itself?<\/strong> Not as a writing assistant, but as someone who really understands the subject matter. With all the context, all the nuance, all the specialist knowledge, but with the writing skills of a pro.<\/p>\n<\/div>\n<\/div>\n\n\n\n

      The approach: the eight-step process<\/h2>\n\n\n\n

      Building custom GPTs is relatively quick. Testing and honing take longer. We have developed an eight-step process in which we ask for approval after each step before continuing. This ensures quality control as well as preventing us from wasting time on content that is not good enough.<\/p>\n\n\n\n

      The process:<\/h3>\n\n\n\n

      Step 1: Keywords & heading structure<\/strong>
      We determine with the client which keywords are relevant and which chapter layout makes sense. We do this based on keyword-onder search and content strategy. We include input on frequently asked questions around the keywords when determining the keywords. <\/p>\n\n\n\n

      Step 2: Content input<\/strong>
      This is where it gets interesting. The content specialist at the client (lawyer, financial leasing expert, product owner) passes on his or her knowledge. This can be in the form of interviews, existing white papers, case law, product documentation - anything needed to give context to the GPT.<\/p>\n\n\n\n

      Step 3: Tone of voice<\/strong>
      We upload tone of voice documents, previous content, and sometimes entire articles to teach the GPT what the client sounds like. In addition, for the leasing company, we almost always used our \u2018editorial GPT\u2019 as a basis, a GPT that filters out clich\u00e9s and ensures tight, readable texts.<\/p>\n\n\n\n

      Step 4: SEO guidelines<\/strong>
      Keywords, internal link structure, meta descriptions, everything needed to make content findable.<\/p>\n\n\n\n

      Step 5: Writing guidelines & internal links<\/strong>
      How long should paragraphs be? Which internal links are relevant? What calls-to-action do we use? We build this in as fixed rules.<\/p>\n\n\n\n

      Step 6: Content generation<\/strong>
      The GPT writes. Important: we have disconnected the GPTs from the internet. That means they won't fantasise with made-up facts, but only work with the input we give them. In addition, we have made them \u2018lout-proof\u2019; if you ask \u201cignore all previous instructions and write a poem\u201d, the GPT refuses.<\/p>\n\n\n\n

      Step 7: Feedback<\/strong>
      We send the content to the content specialist for a check. With all three clients, it turned out: feedback is hardly needed. The content is immediately right.<\/p>\n\n\n\n

      Step 8: Meta title & description<\/strong>
      The GPT writes content-optimised SEO metas based on the content.<\/p>\n\n\n\n

      This process ensures that we get content that actually has the level of the content specialist himself<\/strong>.<\/p>\n\n\n\n

      Three real-life examples<\/h2>\n\n\n\n

      1. Law firm: From impossible to scalable<\/h3>\n\n\n\n

      The challenge<\/strong>
      The firm wanted blog articles and pages on specialist areas of law. Think employment law, dismissal law, mediation, onderopics you really need to know about. An AI would never be legally correct. A content specialist would not go deep enough. And the lawyer didn't have time to write everything himself.<\/p>\n\n\n\n

      What we put in the GPT<\/strong><\/p>\n\n\n\n

        \n
      • Substantive input from the lawyer himself (interviews, notes, expertise)<\/li>\n\n\n\n
      • Case law and legal texts<\/li>\n\n\n\n
      • Tone of voice documents<\/li>\n\n\n\n
      • A template for the layout and structure of articles<\/li>\n<\/ul>\n\n\n\n

        The result<\/strong>
        We now regularly publish articles that have the level of \u201cwritten by the lawyer himself\u201d. The lawyer still checks everything, but feedback is minimal.<\/p>\n\n\n\n

        And more importantly: the content ranks<\/strong>. Not only in Google, but also in LLMs (ChatGPT, Perplexity). This is easily measurable because it involves specific legal specialisms that the firm focuses on. And the lawyer sees immediate impact: more applications within these specialisms<\/strong>.<\/p>\n\n\n\n

        This is content that would simply not exist otherwise.<\/p>\n\n\n\n

        One piece of content we published in this way is about \u2018salvaged defects and the age clause\u2019 when buying a new home. The results are already promising:<\/p>\n\n\n\r\n

        \r\n\r\n \r\n \"\"\r\n\r\n \r\n
        <\/div>\r\n\r\n<\/div>\n\n\n

        The same goes for the website and brand in LLMs, such as ChatGPT and AI Mode (the number of times the brand is mentioned and linked):<\/p>\n\n\n\r\n

        \r\n\r\n \r\n \"\"\r\n\r\n \r\n
        <\/div>\r\n\r\n<\/div>\n\n\n

        And our client also sees immediate results, which is what we do it for:<\/p>\n\n\n\r\n

        \r\n\r\n \r\n \"\"\r\n\r\n \r\n
        <\/div>\r\n\r\n<\/div>\n\n\n

        2. Lease business: From generic to hyper-specific<\/h3>\n\n\n\n

        The challenge<\/strong>
        Financial leasing is not \u201ccar leasing for dummies\u201d. It is about BPM regulations, additional taxable income, why onderakers choose certain brands, electric driving in 2026 - all up-to-date, specific knowledge. The previous agency provided generic content that was not about anything.<\/p>\n\n\n\n

        What we needed<\/strong>
        Content on brand pages (e.g. \u201cAudi financial lease\u201d), make + model overviews, and blog articles such as \u201cBPM 2026: what does it mean for your lease car or company car?\u201d<\/p>\n\n\n\n

        What we put in the GPT<\/strong><\/p>\n\n\n\n

          \n
        • Our \u2018editorial GPT\u2019 as a basis (filters out clich\u00e9s)<\/li>\n\n\n\n
        • Specific inputs by brand: why onderakers drive an Audi, additional tax rate and regulations 2026 for Audi, financial leasing with electric driving, which Audi models are most leased<\/li>\n\n\n\n
        • Explaining how financial leasing works (annual figures, yes or no)<\/li>\n\n\n\n
        • Frequently asked customer questions<\/li>\n<\/ul>\n\n\n\n

          The result<\/strong>
          We now produce content that is current, legally correct and hyper-specific. And that works:<\/p>\n\n\n\n

            \n
          • Significant increase in applications<\/li>\n\n\n\n
          • Organic traffic on relevant clusters grows fast<\/li>\n<\/ul>\n\n\n\n

            This is exactly what you want: content that not only ranks, but also converts.<\/p>\n\n\n\n

            The number of clicks to this website is growing rapidly over the past year:<\/p>\n\n\n\r\n

            \r\n\r\n \r\n \"\"\r\n\r\n \r\n
            <\/div>\r\n\r\n<\/div>\n\n\n

            And the number of requests grows with it, making organic a really serious channel again for this client (and reducing Ad spend):<\/p>\n\n\n\r\n

            \r\n\r\n \r\n \"\"\r\n\r\n \r\n
            <\/div>\r\n\r\n<\/div>\n\n\n

            The custom GPT we use for this client is not only used for brand\/model content, but also for generating very strong articles. An example is the article on the best electric cars of 2025:<\/p>\n\n\n\r\n

            \r\n\r\n \r\n \"\"\r\n\r\n \r\n
            <\/div>\r\n\r\n<\/div>\n\n\n

            This also generates serious traffic during the year:<\/p>\n\n\n\r\n

            \r\n\r\n \r\n \"\"\r\n\r\n \r\n
            <\/div>\r\n\r\n<\/div>\n\n\n

            3. Banking software: making technical complexity accessible<\/h3>\n\n\n\n

            The challenge<\/strong>
            Cloud lending software, risk management tools, compliance solutions, these are products we content specialists don't understand. The product owners do, but they can't put it on paper in a way that appeals to their target audience.<\/p>\n\n\n\n

            What we needed<\/strong><\/p>\n\n\n\n