ZenABM

ZenABM is a LinkedIn Ads AI analyst that lets marketers create and launch, understand, optimize and report on their LinkedIn advertising from Claude, ChatGPT, Perplexity, Gemini and other AI tools through the ZenABM MCP server, or natively from ZenABM's own AI agent, Zena. Zena can plan, manage, analyze and optimize LinkedIn Ads, and the site presents it as a way to build, manage and optimize LinkedIn campaigns with AI. The product is aimed at people who run LinkedIn Ads and ABM campaigns and who would rather work inside a conversational AI client than operate each step by hand. The Product Hunt listing describes the underlying annoyance plainly: ditch copy-pasting into Campaign Manager. Instead of assembling campaigns field by field in LinkedIn's own interface, teams can ask an AI client for what they need and have ZenABM handle the mechanics. Reporting has a similar problem. Rather than exporting numbers and assembling a slide deck every week, ZenABM produces written reports that pair insights with action items, and it cross-references advertising performance with pipeline data. ZenABM also positions itself around company-level insights, so campaign results connect to the accounts and revenue behind them rather than living as detached impressions and clicks. Inside Zena, campaign building starts with a description. You describe the campaign you want and Zena builds it end to end: the campaign, its ad sets, targeting, and the ads themselves. Ad copy is written for you, and creatives are pulled from your media library. Before committing, you can check the audience size, reuse saved audiences and lead forms, and duplicate campaigns that already work. Crucially, nothing goes live until you confirm it, so the AI drafts and prepares while the human approves. The site illustrates this with Claude generating four document ads for a ZenABM workshop in London, showing how a request turns into a set of draft ads inside the tool. The ZenABM MCP server extends the same campaign work into whichever AI client a team already uses. You ask Claude, ChatGPT, Perplexity or Gemini for the ads you need; the MCP server generates them, pushes them into a new ad set with the objective, budget and bidding you asked for, and hands you a link to review and approve in Campaign Manager. Again, nothing launches until you approve it. The server is described as letting you build, manage and optimize LinkedIn ads and campaigns directly from Claude or any AI tool, and it can be connected during a free signup. An illustration on the page shows the ZenABM MCP server connected to Claude. Under the hood, the MCP server ships with 15 ready-made ABM skills that you run as slash commands. The site lists audits, monthly reports, strategy planning, ad decay checks and sales handoff lists, all graded against ZenABM benchmarks. Beneath the skills sit 96 read and write tools spanning LinkedIn Ads, ABM, CRM and revenue data, so an AI client can both inspect and act on the data rather than only summarise it. Because the skills come prebuilt, users do not have to design prompts for common ABM jobs; they invoke a command and the underlying tools do the work against benchmarked expectations. Automated reporting is one of the headline capabilities. Zena produces weekly, monthly and quarterly reports written for you and sent straight to your inbox, containing insights and action items that Zena can carry out on your approval, rather than a raw data dump. You can also ask for a report on the spot. In that case, performance is cross-referenced with your pipeline, top and low performers are surfaced, and the result is shareable in seconds. The Product Hunt description adds company engagements and revenue attribution to the reporting scope, produced by ZenABM's AI agents on a weekly and monthly cadence. Optimization happens without leaving the chat. Zena finds and fixes underperforming LinkedIn ads and campaigns for you: it surfaces your lowest and best performing assets and then acts on them. Actions include pausing inefficient ad sets and campaigns, changing bids and budgets, and building retargeting audiences from ad engagement and CRM events. Every change waits for your approval, so optimization stays reviewable rather than automatic. A screenshot on the site shows Zena pausing underperforming LinkedIn ad sets, which illustrates the intended flow: the analyst identifies the problem, proposes the fix, and the marketer signs off. Zena also acts as an advice channel. The agent is trained on knowledge from more than 30 ABM and LinkedIn Ads experts — the site cites Tim Davidson, Ali Yildirim, Max Herzeg and many more — drawn from their own posts. When you ask about list building or ABM strategy, the answer comes back tied to your own data and to benchmarks from other accounts, so the comparison is real rather than generic. Benchmarking material shown on the site compares ad performance to industry benchmarks, reinforcing that answers are grounded in data rather than opinion alone. Zena, the MCP server and the API are all powered by the same company-level ABM data. That shared foundation is what ties the three surfaces together: the AI agent for conversational analysis, the MCP server for bringing ZenABM into AI clients such as Claude, ChatGPT and Cursor, and the API for connecting LinkedIn Ads data anywhere and building your own dashboards. The API lets you pull LinkedIn Ads engagement, campaign performance and intent stages wherever you need them. Because the AI layer runs on the same data as the rest of the platform, users can ask Zena to analyse LinkedIn Ads performance, find top engaged companies, and surface or pause underperforming ads, then take the same data into their own systems. Concrete workflows the site describes include building a full campaign from a short description, generating a batch of document ads inside an AI client, and approving prepared ads in Campaign Manager before launch. Reporting runs as a recurring workflow: weekly, monthly and quarterly reports land in the inbox, and ad hoc reports answer point questions. Optimization workflows cover auditing spend, pausing inefficient ad sets, adjusting bids and budgets, and assembling retargeting audiences from ad engagement and CRM events. For ABM teams, ZenABM supports identifying top-engaged accounts and producing sales handoff lists, and the API supports pulling campaign and intent data into external dashboards. The product is built for the people who run LinkedIn Ads and ABM programs. The FAQ addresses readers asking whether they need to be technical, which AI clients the MCP server works with, whether ZenABM AI can take actions or only read data, how data is secured, which ZenABM plans include the AI features, and whether the AI can be tried before paying. Integrations named in the content include Claude, ChatGPT, Perplexity, Gemini and Cursor, plus LinkedIn Ads, ABM, CRM and revenue data. On pricing, the site offers a Start for Free button and a Book a Demo option, and a three-minute walkthrough is available. ZenABM's pitch is that LinkedIn Ads should be created, optimized and reported on where marketers already think and write — inside an AI tool. Zena and the MCP server carry campaign building, expert skills, optimization actions, benchmarking and reporting into that conversation, all on top of the same company-level ABM data, with human approval before anything goes live.