Grok Bots have been out a few weeks and the demos are everywhere. Here's what actually survives contact with a real business: the use cases that keep proving out, the bots we run Widgetsflow on, and three free templates you can add to your own account in one click.
Three free templates, built and used by us. Add any of them to your own account in one click. A shared bot never carries our logins, files or data.
Grok Bots landed in August and the timeline filled up within days — lawn mowers, surf reports, Tamagotchi eggs. Genuinely fun, and almost none of it tells you whether this is useful for a business.
We have been running ours on real work since launch: our company inbox, and the FlutterFlow community groups. This is what has actually held up, what we deliberately kept human, and three free templates you can copy off this page and point at your own business today.
Worth being precise, because the name undersells it. A Grok Bot is not a chat window with a longer memory. It is an always-on agent with its own cloud computer — its own browser, its own logged-in sessions, its own files. It signs into the tools you already use and works multi-step jobs on its own schedule, coming back to you when it needs a decision.
The part that changes how you work is the routine. Do a job once with the bot watching, and it saves the steps and runs them itself from then on. You are not writing prompts any more, you are training a colleague. Several bots can run at once and hand work between them, which is why so many people end up with a small org chart rather than one assistant.
Strip away the novelty bots and the same five shapes keep appearing, because they share one trait: the work is mostly reading, and the reading is what eats your week.
Give it a list of accounts, or a search it should run, and let it work while you sleep. It reads the company, the people, the recent news, scores what it finds against your criteria, and leaves you a shortlist. The value is not that it writes better than you — it is that it read forty things and you only have to read six.
Most inboxes are three inboxes wearing a trench coat: real leads, support questions, and noise. A bot that reads each new message and sorts it into those three piles — drafting a reply for the first two and ignoring the third — removes the single most fragmenting task of the day. You stop context-switching to decide; you only handle what survived triage.
Anyone running a group of real size knows the join-request queue is a tax. Spam accounts, bots, people who read the name and nothing else. Screening is pure pattern-matching against rules you can write down, which makes it close to ideal work to hand over — and the same pass that rejects spam can flag the people worth talking to.
Deals are lost to silence far more often than to a competitor. The problem is never that you forgot the client — it is that the promise you made forty minutes into a call never made it onto a list. A bot that reads the transcript and extracts every commitment with a date closes that gap completely.
Buyers now ask an assistant before they open a search engine, and the answer names two or three businesses. If you are not one of them you will never know, because there are no impressions to look at. Something has to run those questions on a schedule and tell you who is being recommended instead of you.
Two bots, both pointed at work that was quietly costing the most hours. Neither replaced a person; both removed a queue. That distinction is the same one we drew when we turned a manual, one-to-one process into a product.
Our support address handles client questions, new enquiries and everything in between. The bot reads what comes in, separates genuine leads from support, drafts the reply, and keeps a running list of who is owed a follow-up and when.
The unlock was not the drafting. It was never again opening the inbox to discover a fortnight-old enquiry that quietly went cold.
We run the largest free FlutterFlow community on Facebook, around 70,000 members. The bot reviews join requests and pending posts against the rules, rejects the obvious spam, and — the part that actually matters — flags people describing a problem we solve, with a DM already drafted.
Community work is mostly a filtering job, and filtering is exactly the shape of task worth handing over. What it is not is a relationship job, which is why the bot stops at the draft.
Nothing we run sends an email, posts in a group, or messages a lead on its own. Not because the drafts are bad — they are usually fine — but because the cost of a wrong send is a relationship, and the cost of a wrong draft is ten seconds.
All three are free, and all three are bots we actually run. Adding one copies the instructions and routines only, never our logins, files or data.
Manages any Facebook community: reviews joins and posts, rejects spam, and flags in-market leads for drafted DMs you send yourself. This is the bot behind the community section above.
You manage a Facebook community for me. Your job is to keep the group clean and surface the people worth talking to.
For every join request: 1. Check the profile against our rules — real account, relevant to the group’s topic, entry questions answered. 2. Approve a clear yes. Reject obvious spam, bot accounts and off-topic joins. Leave anything ambiguous for me with a one-line reason.
For every pending post: 1. Approve posts that are on-topic and follow the rules. 2. Reject promos, referral links, and reposts of the same question inside 24 hours. 3. Flag anything borderline rather than deciding it yourself.
While you review, watch for in-market leads — people describing a problem we solve, asking for a recommendation, or saying their current setup is broken. For each one, draft a short, non-salesy DM that references what they actually said.
Never send a DM, never post as me, never message anyone in the group. Draft it and leave it for me.
Report once a day: approved, rejected, flagged, and the leads worth a reply.
Turns a call transcript into a recap, every promise made, and a dated follow-up list. Works from a paste or an upload, and never sends a follow-up without you.
You turn call transcripts into a recap, a list of every promise made, and a dated follow-up plan.
When I paste or upload a transcript: 1. Write a five-line recap — who was on the call, what they want, budget or timing if either came up, and the single most important thing they said. 2. List every commitment, split into “I promised” and “They promised”. Quote the line it came from. 3. Build a dated follow-up list. Use the dates discussed on the call. Where no date was given, propose one and mark it [proposed]. 4. Draft the follow-up message. Plain and direct, no filler, no “hope this finds you well”.
Never send anything. Draft it and wait for me.
If the transcript is ambiguous about who owns a commitment, ask me instead of guessing.
Checks whether AI assistants and Google recommend you, and who they name instead. Starts from a handful of questions your buyers actually ask.
You check whether AI assistants and search engines recommend my business, and who they name instead.
Start by asking me for two things: my business in one line, and the five questions a buyer would actually type before hiring someone like me.
Then for each question: 1. Run it and record whether I am named, where in the answer, and in what context. 2. List every competitor named ahead of me, with the reason the assistant gave. 3. Note which sources were cited — the pages actually doing the recommending.
Report as a table: question, named yes/no, who ranked above me, source cited. Finish with the three highest-leverage gaps — the specific pages or mentions most likely to get me named.
Re-run weekly and tell me only what changed.
Do not assert ranking factors you cannot observe. If a result might be personalised, say so rather than presenting it as universal.
The mistake we watched people make in the first fortnight was starting with the most interesting task rather than the most repetitive one. Interesting tasks are interesting precisely because they need judgement. The same test applies to any automation, which we went through in more depth in the five processes worth automating first.
Hand over work that looks like this:
Keep these for yourself:
Start with one bot on one boring job for a week. The second one is obvious once the first is running, and it is never the one you would have guessed on day one.
