AI visibility
What is Recommendation Ops, and why did we create it?
Updated October 2026 · Manoj Surya
Quick answer
Recommendation Ops is the work of getting your brand recommended wherever buyers ask: AI answers, communities, news, video and search. It has three parts: watch every place buyers decide, rank the gaps where competitors are named and you are not, and close each gap with your team or your agents. MarketHQ is built for that loop.
Key points
- Monitoring tells you what was said. Recommendation Ops ends in a move someone finishes.
- AI answers repeat the threads, articles and videos they cite, so the two have to be worked together.
- MarketHQ watches social, web and AI answers for you and every competitor, and ranks the gaps into a weekly action plan.
- MarketHQ hands each move to your team with a draft, or to your agents through its MCP server and REST API on paid plans.
Founder of MarketHQ. Building market intelligence for companies that sell to developers.
Buyers no longer find brands in one place. They ask ChatGPT, read a Reddit thread, skim a 'best of' list, watch a comparison video, then shortlist two or three names. Each of those places has its own tool category, and none of them tells you what to do on Monday. We think the job deserves its own name, so we gave it one.
What problem does Recommendation Ops solve?
It closes the gap between knowing where competitors are recommended and actually getting recommended there yourself.
Most teams already have a listening tool, an SEO tool and, lately, an AI-visibility tracker. Each one produces a dashboard. Someone still has to read all three, decide which thread, article or answer matters, and do something about it. That reading is where weeks go.
The places also feed each other. An AI answer that names your competitor usually cites a forum thread, a roundup or a review. Fixing the answer means getting into those sources. A tool that only watches the answer, or only watches the posts, sees half the loop.
How is Recommendation Ops different from brand monitoring and AI visibility?
Brand monitoring watches posts, AI visibility watches answers, and Recommendation Ops works both into a ranked list of moves.
The categories overlap, and some AI-visibility tools now suggest actions too. The difference is scope and the finish line: one list across every channel, and each item done by a person or an agent.
| Brand monitoring | AI visibility | Recommendation Ops | |
|---|---|---|---|
| Watches | Social posts, news, blogs | AI answers and the pages they cite | Both, for you and every competitor |
| Main output | A mention feed and alerts | A visibility score by prompt | A ranked weekly action plan |
| Done when | Someone has read the feed | The score is reported | The move is shipped and checked |
| Who does the work | Your team | Your team | Your team or your agents |
What does a Recommendation Ops week look like?
Track, find the gaps, work the moves, then prove what changed.
The cycle is weekly because AI answers and the sources behind them change over weeks, and a single daily answer is mostly noise.
- Track: your buyer questions, competitors and keywords across AI answers, communities, news, video and search.
- Find: every answer, thread or article that names a competitor and not you, with the quote and link.
- Win: a ranked list of moves, such as replying in a thread an answer cites, pitching a roundup author, publishing a missing comparison page or fixing your own FAQ.
- Prove: next week's check shows which answers and threads now name you.
Where do agents fit in?
Agents take the moves that need no relationship, like drafting a page or fixing an FAQ, and report back when they are done.
Some moves need a human: a reply in your own voice, a pitch to a journalist. Others are well-defined writing or editing tasks that an agent can finish. Recommendation Ops puts both on the same list with the same evidence, so a person and an agent never work from different facts.
MarketHQ exposes the list through an MCP server and a REST API on paid plans. An agent in Claude, Cursor or any MCP client can list open tasks, claim one, read the evidence and report the outcome.
Why did MarketHQ name a new category?
Because buyers asked us which box we fit in, and neither existing box described the job.
On demo calls, people asked whether MarketHQ was a listening tool or an AI-visibility tool. The honest answer was both, plus the part neither covers: deciding what to do and getting it done. Naming the work makes it easier to own. A team can say 'Recommendation Ops is Priya's job this quarter' in a way it cannot say 'monitoring'.
What Recommendation Ops does not cover yet
It works from public posts and AI answers, so private channels and some platforms are out of reach.
MarketHQ does not track Instagram, TikTok or Facebook yet, and it cannot see private communities or a buyer's personal AI chats. AI answers vary between runs, so treat single results as samples and judge the trend over several weeks.
FAQ
- Who should own Recommendation Ops?
- Usually the marketing or growth lead, with community, PR and SEO people working the moves. Founders often own it at first. Agencies run it for each client.
- Is Recommendation Ops the same as GEO or AEO?
- No. GEO and AEO are about shaping AI answers. Recommendation Ops includes that, plus the threads, articles and videos those answers cite, and the weekly work of closing each gap.
- Do I need AI agents to do Recommendation Ops?
- No. Your team can work the whole list in the app with drafted replies and outlines. Agents are optional and take over the moves that need no human relationship.
Related
- Gap analysis on AI answers — turning lost questions into fixes
- Build an AI visibility scoreboard — measuring the Prove step
- Weekly AI visibility routine — a founder's version of the weekly loop
- Competitor monitoring — watching where competitors are named