# Custom LLM Applications > Custom LLM applications by JM Websites: back-end AI workflows that classify documents, summarize text, extract structured data or generate content based on your sources. Not a chatbot, but AI as an invisible engine inside your existing processes. Source: https://jmwebsites.nl/en/automatisering/llm-applicaties --- Custom LLM Applications | AI Back-End Workflows | JM Websites | JM Websites[Skip to content](#main-content)Automation · LLM Applications # LLM applications for work that eats your time structurally back-end AI for analysis, extraction and content Not every AI solution is a chatbot. Sometimes it's a piece of software that runs in the background, analyzing documents, classifying content, summarizing reports or automating recurring writing tasks. We build those applications bespoke, with your data as the source. [Request free design](/en/gratis-ontwerp)[← Back to all automations](/en/automatisering)Backgroundruns quietly, no separate UI requiredYour sourcesworks on your documents and dataBatch or liveone-off processing or continuous01 ## Who is this for? Three typical situations where this automation pays for itself within months. 01 ### Document classification Incoming emails, PDFs or forms are automatically sorted by type, urgency and owner. The right team gets the right message, without anyone needing to read everything first. 02 ### Summarization and insight extraction Long interviews, reports or meeting notes are automatically turned into concise summaries with the key points, action items and follow-up questions. 03 ### Content assistant for structured data Product descriptions, SEO headings or translations are generated from structured input (e.g. product data), in your tone, ready for publishing after a short review. 02 ## What you gain ### Tuned to your domain Not generic prompts, but logic that knows your field, terminology and customer context. Results land ready-to-use inside your existing workflow. ### Fits your existing stack We connect via API or webhook to the tools you already use. Output lands exactly where you expect it: email, CRM, Notion, database, spreadsheet. ### Cost-efficient at volume We pick the right model per task: a small, fast model where it suffices, a larger and pricier one only where it's truly needed. ### Traceable and auditable Every run is logged, including input, prompt, model, output and cost. So you always know what the AI did with a given input. 03 ## How we build it From first call to live in production. Transparent, predictable, no surprises. 01 ### Sharpen the use case Which decision or output should the application deliver? That drives model choice, data pipeline and prompts, not the other way around. 02 ### Prototype on real data In roughly a week we stand up a working version on your actual inputs, so we can see immediately where it works and where it doesn't. 03 ### Integrate and monitor We wire it into your existing systems and add logging so we can measure how the application performs in production. 04 ### Tune on real traffic The first weeks always surface edge cases. We use those to refine prompts, validation and model choice. Built withOpenAI / Claude / Groqn8n or custom Python/NodeVector database (optional)API and webhook integrationLogging and audit trailWhat does it cost? Price on request, depending on scope: a one-off batch task is very different from a continuous pipeline. We quote concretely after a short intake. Combining with other automations or a website? We often bundle these together, it genuinely saves in practice. [Request a custom quote](/en/gratis-ontwerp)[Or send a message](/en#contact)04 ## Frequently asked questions Is this the same as a chatbot?No. A chatbot is a user-facing conversational interface. An LLM application typically runs in the background without anyone actively chatting, think of a service that classifies every incoming PDF, or summarizes new articles for your team daily.Which model do you use?We pick per use case. For simple classification, faster and cheaper models often suffice. For nuance (legal summaries, complex analysis) we bring in a bigger model. During prototyping we test which combination works best for your task.How do you handle sensitive data?We default to models that don't use input for training (OpenAI API, Claude API or Azure OpenAI in the EU). For truly sensitive use cases we can run locally or in your own cloud, so the data never leaves your infrastructure.How do you prevent errors and hallucinations?By grounding each prompt in your own sources where relevant, validating output formats strictly (JSON schemas, checks) and auto-flagging anomalies for human review. For critical decisions we always build in a review layer.Does this become a black box, or can I see what happens?Every run is logged: input, model, prompt, output and cost. So you can always trace back exactly what the AI did with a given input, useful both for debugging and for audit or compliance. ## Goes well with this [ ### AI Chatbot Not a generic ChatGPT widget. A chatbot trained on your content, in your tone of voice, answering questions evenings and weekends included, and only based on information you provide. ](/en/automatisering/ai-chatbot)[ ### Reporting Automation Few things eat up time like building and merging the same Excel exports every week or month. Built once properly, it's done, and you see live what you want to see, whenever. ](/en/automatisering/rapportage) ## Want to see how this looks at your business? Request a free design, during the follow-up call we'll also discuss which automation would have the biggest impact at your business. [Request free design](/en/gratis-ontwerp)[Or send a message](/en#contact)