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“We built CustomGPT.ai so businesses could get real value from AI, faster and without technical expertise.”
Use CustomGPT.ai Custom Actions (in-chat) or an automation trigger (out-of-chat). In-chat actions call an external endpoint when the user asks (e.g., “create a ticket”). Automation triggers fire when events happen (e.g., “new conversation”). Both routes let you POST structured JSON to your tools. In CustomGPT.ai, Custom Actions are designed to “turn conversations into action” by […]
Create a GDPR-compliant AI chatbot by designing it around data minimization, lawful processing, access control, and auditability. The chatbot should only process necessary data, rely on a clear legal basis, respect user rights (access, deletion), and ensure personal data is not retained or reused without consent. GDPR compliance is not about adding a disclaimer, it’s […]
Debug it by tracing the answer backward through the RAG pipeline: (1) what sources were retrieved, (2) how they were ranked, and (3) whether the model generated claims not supported by those sources. Most wrong answers come from retrieval or governance gaps not the model itself. Start by inspecting which documents/chunks were used (or should […]
You can keep your AI chatbot synchronized by connecting your XML Sitemap to an AI ingestion system that regularly crawls the sitemap, detects changes, and re-indexes updated pages automatically. Instead of manual reuploads, the chatbot stays aligned with your latest website content as pages are added, updated, or removed. An XML Sitemap acts as a […]
To find trusted AI solutions for secure document analysis, start with a security baseline (data handling, access controls, certifications), then validate each vendor with evidence (docs, contracts, tests) and a short pilot using your own sensitive documents. Prefer tools that provide citations, auditability, and configurable retention. Security teams comparing vendors can also review the best […]
Most teams don’t train a new model from scratch, they connect their content to an AI agent (RAG) so it answers from your docs with citations. If you’re trying to get reliable answers from policies, manuals, product docs, or an internal wiki, “training” usually means “make the AI read what we already wrote.” With CustomGPT.ai, […]
Gemini chatbot can power a strong support experience, but betting your chatbot on one provider is a reliability and governance risk. A model-agnostic support agent lets you keep the same knowledge, guardrails, and integrations while switching models, and failing over automatically during outages. Support automation is unforgiving: you feel failures when traffic spikes, not when […]
You can make Google Drive and Notion searchable through one AI interface by connecting both sources to a retrieval-augmented AI system that securely ingests, indexes, and semantically searches across them. Instead of switching tools, users query once and receive grounded answers synthesized from both platforms. Most organizations store operational documents in Google Drive while managing […]
Integrate it by putting a thin “chat API” layer between your mobile app and your RAG provider: the app sends messages to your backend, your backend calls the RAG chat-completions endpoint, then streams or returns the response to the app with citations. This keeps API keys off devices and improves security and control. Related: web […]
Build it as a retrieval + verification system, not just “chat over documents.” Use clean ingestion (versions + permissions), strong retrieval (hybrid + reranking), grounded generation (citations per claim), and continuous evaluation. Reliability comes from controls: what can be answered, which sources win, and how every claim is checked. Accuracy collapses when you treat embeddings […]