How-to guides and feature walkthroughs for Human dnAI workflows, knowledge bases, automations, and content output.
Marketing teams get reliable Agent Chat results by centralizing approved knowledge, separating temporary sources, reusing existing research, and grounding every asset in one measurable campaign brief. Pin key decisions, review outputs with responsible employees, and save only approved work to maintain accuracy and consistency.
Read Post →dnAI’s Multi and Composition Workspace turn one campaign brief into coordinated headlines, long-form copy, and visuals in a single review flow. Teams can refine assets individually, approve each slot, and then save, export, or publish the completed kit without juggling separate chats.
Read Post →Inline workflows turn chat into an operational control surface by letting users run, confirm, monitor, reload, save, and schedule automations without leaving the conversation, reducing friction and making repeatable work easier to manage. Built-in Telegram support closes the loop by delivering workflow outputs directly to users’ DMs or even triggering workflows from Telegram, creating a faster chat-to-action-to-delivery workflow.
Read Post →Choose MCP when an AI assistant like Claude needs to discover and use dnAI tools conversationally, and choose the REST API when your app, automations, or backend needs deterministic content generation or deep research. Both paths use the same shared dnAI foundation—knowledge bases, brand voice, templates, and rules—so you avoid duplicating prompts and context across tools.
Read Post →Brand Monitor replaces unreliable AI rank tracking with probabilistic visibility metrics that measure how often your brand appears across repeated queries, turning unstable snapshots into statistically grounded insights. With Visibility Confidence and Prompt Coverage tests, it shows your true presence in AI consideration sets and provides clear, prioritized actions to improve discoverability.
Read Post →Multi-Source Research transforms a single prompt into a structured, cited intelligence report by deploying parallel agents that cross-check sources, flag contradictions, and synthesise findings into decision-ready insights. It delivers transparent, evidence-backed analysis instead of surface-level summaries, making it suitable for high-stakes strategic decisions.
Read Post →dnAI’s Competitor Tracking automatically monitors pricing, messaging, and offering changes, delivering real-time alerts, side-by-side diffs, and historical insights in a single dashboard. It helps teams turn competitor shifts into structured, actionable intelligence through manual checks, automated workflows, and role-specific alerts.
Read Post →dnAI’s Multi‑Knowledge Bases keep brands, assets, and context fully isolated by letting you work in one clearly defined active Knowledge Base at a time. By selecting the right active KB before chatting, uploading, saving, or running workflows, teams create and automate content with confidence without cross‑contamination.
Read Post →This workflow helps users generate fresh content ideas by selecting a past article from their knowledge base, analyzing the current market conversation around the topic, and creating a new blog plan, which is then delivered via email. Ultimately, this system helps users maintain continuity, confidence, and forward motion in their writing.
Read Post →Deep Research helps you create high-confidence blog posts by grounding them in market insights, eliminating guesswork, and saving research for future content alignment. By following a step-by-step process, you can write faster, with more confidence, and reduce revisions.
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