Free AI automation ranking
The best AI automation platforms
27 AI automation platforms compared for hands-off work: what they can execute, how independently they run, their reliability controls, and how much setup they need.
Last measured 31 August 2026
This month's results
Our pick
Notis for hands-off work
The top 3
The highest-scoring award-eligible products. Rank badges match the full table.
Zapier
Strongest on reliability
98.3/ 100
Overall score
Activepieces
Strongest on execution
97.5/ 100
Overall score
Make
Strongest on execution
93.9/ 100
Overall score
Podium and bonus awards require at least 90% of the category matrix checked, at least 80% of every scored feature group checked, and a known setup level. Other scores are provisional. Unknown is not treated as no.
Bonus awards
Picks and standouts beyond the overall score. Attention combines visibility in AI answers with audience reach; gap awards also account for funding.
Our pick for hands-off work
Notis
For delegating everyday tasks in plain language, without building workflows step by step.
Overall score: 75.7 / 100 · #12 of 27
Strong score, less attention
Activepieces
The biggest hidden gem: its score outpaces its attention, after accounting for funding.
Score rank: #2 of 27 · Attention rank: #3
The ranking
Every tracked product by score, next to its documented capabilities, measured attention and disclosed funding. Sort or filter the table to explore the field. Provisional scores remain visible but cannot win awards.
26 of 27 tracked platforms.
| #Position on the overall score, 0-100: execution 35%, reliability 15%, ready to use 15%, autonomy 35%. Provisional scores cannot win awards. The rank itself stays put when you re-sort or filter. | Platform | Verdict | |||
|---|---|---|---|---|---|
1 | Workflow automation platform | 95— | 67— | $1M— | Earns itThe attention and the score line up. Gap +3.8 points of the field. |
2 | AI workflow automation platform | 100— | 12.3— | Undisclosed— | UnderratedScores far higher than the market talks about it, without buying the attention. Gap +15.4 points of the field. |
3 | Visual AI automation platform | 100— | 35.8— | Bootstrapped— | Earns itThe attention and the score line up. Gap +3.8 points of the field. |
4 | AI assistant with a companion workflow-automation builder | 85— | 0— | $30M— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
5 | No-code app builder with scheduled/triggered workflow automation and an autonomous cross-tool AI agent (Superagent) | 70— | 0.4— | Bootstrapped— | Earns itThe attention and the score line up. Gap +11.5 points of the field. |
6 | AI workspace assistant and automation platform | 75— | 0.7— | $343M— | Earns itThe attention and the score line up. Gap -19.2 points of the field. |
7 | AI customer-support agent platform | 65— | 0— | Undisclosed— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
8 | Personal AI assistant | 60— | 0.7— | $25M— | Earns itThe attention and the score line up. Gap -11.5 points of the field. |
9 | Cloud AI agent platform for teams | 85— | 0— | $42M— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
10 | Open-source self-hosted personal AI agent | 75— | 0— | Undisclosed— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
11 | Cloud AI coworker for recurring operations and internal app building | 50— | 0— | $5M— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
12 | Personal AI assistant | 55— | 0— | Bootstrapped— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
13 | AI work assistant ("Townie") with built-in routine automation | 70— | 0— | $73M— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
14 | AI employee and AI coworker platform | 55— | 0— | Undisclosed— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
15 | Slack-native AI teammate | 40— | 3.6— | $50M— | OverhypedTalked about far more than it scores — and the money it raised is part of why. Gap -46.1 points of the field. |
16 | Managed AI agent workforce | 50— | 0— | Undisclosed— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
17 | Agent-native company operating system | 35— | 0— | $9M— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
18 | Enterprise AI agent platform | 30— | 0.4— | $16M— | Earns itThe attention and the score line up. Gap -42.3 points of the field. |
19 | Open-source AI agent workforce orchestration platform | 44— | 0— | Undisclosed— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
20 | Enterprise AI agent infrastructure and automation platform | 28— | 0— | Bootstrapped— | Under the radarAppeared in no tracked answer, so there is no attention to judge yet. |
Score = execution 35%, reliability 15%, ready to use 15%, autonomy 35%. How each component is measured.
Loud is not the same as capable
One chart, one line. Up is where a product stands on the score. Across is its measured AI-answer attention, adjusted for the money behind it, because money buys attention and a bootstrapped brand at the same volume has proved something a funded one has not. On the line, the attention is earned. The interesting ones are the distance off it.
Both axes are a tool's position in the tracked field rather than its raw number, because a weighted score out of 100 and a long-tail attention measure cannot be subtracted from each other. Across is measured AI-answer attention plus the premium its funding explains, added to attention rather than taken off the score. Above the dashed diagonal a product is better than it is talked about; below it, the reverse. The shaded corridor is within 15 percentile points of the line, which is close enough to call even. The 9 platforms stacked at the left appeared in no tracked answer, raised nothing we could verify, so there is no signal left to tell them apart — they share a position because they share the measurement, and fanning them would invent an order. Every point is a row in the table above.
Does money buy attention?
The same field, split the other way: disclosed funding against measured attention. The dashed line is fitted to the products with known funding in this category; the distance from it shows which brands get more or less attention than that relationship suggests. Unknown funding is shown separately, not treated as zero.
How the score is calculated
Every product is scored for hands-off work using the same documented feature evidence and weights.
- 35%Execution
- Documented ability to act across apps, browsers and workflows, including branches, loops, data transforms and code. This is feature evidence, not a task-success benchmark.
- 15%Reliability
- Documented approvals, error handling, logs, testing, versioning, monitoring and secret management. This measures available controls, not observed uptime.
- 15%Ready to use
- Whether you can sign up and start, need some setup, or must operate the platform yourself.
- 35%Autonomy
- Natural-language building, schedules, app-event triggers, long-running workflows, AI steps, agents and knowledge retrieval.
Comparable research before awards
Podium and bonus awards require at least 90% of the category matrix checked, at least 80% of every scored feature group checked, and a known setup level. Other scores are provisional. Unknown is not treated as no.
Provisional products remain in the full score-sorted table, clearly labelled. Rank badges on the podium retain their positions in that table.
Score ordering
Method hands-off-v2. Exact score ties break on autonomy, then execution, then slug. Popularity and funding never break a score tie.
Money raised, charged against attention
Attention is measured monthly, converted to a position in the tracked field, and then charged for the money behind it — up to 25 points, log-scaled and capped — before the field is ranked again on the result.
Money buys attention. A bootstrapped brand talked about as much as a funded one has proved something the funded one has not.
Overhyped and underrated are what is left: the distance between where a tool ranks on the score and where it ranks on attention after that charge. It is one number, and it is the one the quadrant above plots — the picture and the verdict are the same claim. The chart beside it shows the relationship the charge is drawn from.
The prompt panel
100 neutral prompts per category, fixed for the month, run through DataForSEO across ChatGPT, Claude, Gemini and Google AI Overviews — 400 answers per product. Each brand comes back as AI answer visibility and share of voice, which is the whole of its attention score.
A tool named in none of them scores zero on the panel — that is a finding about these 100 questions, not about the tool. A Google result with no AI Overview is a measured absence; a provider failure is unmeasured and blocks the whole month.
Each tool's own page lists the questions it appears in and its position on every one, so a score can be checked rather than trusted.
The capability matrix behind every score is open, cell by cell, in the AI automation platforms comparison directory.
Questions people ask about this ranking
- How is the ranking calculated?
- One score out of 100, weighted by execution 35%, reliability 15%, ready to use 15%, autonomy 35%. Documented support earns one point, partial support half, and no support zero. Unknown evidence is excluded from the score denominator, with a separate research-completeness gate for awards. Execution and reliability describe documented features, not measured task-success rates or uptime.
- Why is a score marked provisional?
- Podium and bonus awards require at least 90% of the category matrix checked, at least 80% of every scored feature group checked, and a known setup level. Other scores are provisional. Unknown is not treated as no.
- Does AI visibility determine the winner?
- No. The default order comes from the product score. Attention is measured separately using 100 neutral category-specific prompts across ChatGPT, Claude, Gemini and Google AI Overviews. Funding informs the attention verdict, never the product score.
- What does overhyped mean here?
- A product the market talks about far more than its score justifies, once the money behind it is accounted for. Attention and score are each converted to a position in the tracked field, a brand's disclosed funding is charged against its attention by up to 25 points, and anything still at least 15 points louder than it scores is called overhyped. A tool with almost no measured mentions never is — it is not loud, it is unmeasured.
- What does underrated mean here?
- The mirror of overhyped: a product that stands at least 15 points higher on the score than on attention once funding is charged against it, and that was named in at least one tracked answer. A tool that appeared in none is under the radar rather than underrated — there is no attention to judge it against yet. Underrated is the finding the page exists for — a product doing the work without the volume behind it — and it is the only verdict a tool can earn by being good rather than by being loud.
Spotted something out of date?
Every cell here was read off a vendor’s own page on a dated pass, and vendors change their pages between passes. Tell us what moved, or which tool we are missing.
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