The Founder's Guide to Choosing an AI Automation Platform

I'll be upfront: we built Kaer, so we're biased. But we also spent months evaluating every player in this space before deciding to build, so we have a reasonably informed perspective on what matters and what doesn't.

Here's the framework we wish someone had given us when we started looking.

Start by ignoring the feature list

This sounds counterintuitive, but hear us out. Every AI automation platform has a feature page that reads like a wish list: "AI-powered," "enterprise-grade," "infinitely scalable," "integrates with 500+ tools." These are marketing claims, not product capabilities. The feature list tells you what the product aspires to be. It doesn't tell you what it actually does well.

Instead, start with the thing that matters most: what happens when you run a real task?

Sign up for free trials of your top three candidates. Give each one the same task — something your team actually needs done. A competitive analysis. A research brief. A content draft. Then evaluate the output. Not based on whether it's perfect, but based on whether it's useful enough to save you time.

This one test will tell you more than any feature comparison spreadsheet.

The five things that actually matter

After the initial test, evaluate across these five dimensions. They're roughly in order of importance.

1. Output quality

Does the tool produce results that are genuinely useful, or results that look impressive but need significant editing? The bar is: would you send this output to a colleague without substantial revision?

Pay attention to:

• Accuracy of factual claims (spot-check a few)
• Relevance to your specific request (did it address your constraints, or give a generic response?)
• Completeness (did it cover what you asked for, or skip important parts?)
• Formatting and readability

Quality varies dramatically across platforms — even those using the same underlying models. The difference is in the orchestration, prompting, and tool use, not the model itself.

2. Reliability

Run the same task three times. Do you get consistently good results, or is it a lottery? A platform that produces one amazing output and two mediocre ones is worse than one that consistently produces good-but-not-amazing outputs. Consistency matters more than peak performance.

Also check: what happens when things go wrong? Does the tool crash, hang indefinitely, or handle errors gracefully? Try giving it a task with a dead link or an impossible request and see how it responds.

3. Pricing model

AI automation pricing is confusing by design. Some platforms charge per task, some per minute of execution time, some per "credit" (which maps to token usage in opaque ways), and some per seat.

The question to ask: can I predict my monthly bill based on my expected usage? If the pricing model requires a spreadsheet to estimate costs, that's a red flag. If it uses a credit-based system, make sure you understand exactly what consumes credits and how quickly they deplete.

Watch out for per-seat pricing that makes it expensive to roll out across your team. And watch out for usage-based pricing with no caps — a runaway automation could generate a surprisingly large bill.

4. Flexibility and extensibility

Your needs will change. The platform should be able to change with them. Key questions:

• Can you connect it to your existing tools via APIs or webhooks?
• Can you customize agent behavior beyond basic prompting?
• Does it support scheduled execution and event-driven triggers?
• Can you chain multiple tasks into workflows?
• Does it have an API so you can integrate it into your own products?

A platform that does five things perfectly but can't extend beyond them will become a bottleneck as your automation needs grow.

5. Company viability

This one's uncomfortable but important. The AI tooling landscape is consolidating rapidly. Many of the 200+ platforms that exist today won't be around in two years. Evaluate:

• Is the company funded? By whom?
• How frequently do they ship updates? (Check the changelog.)
• Is there an active community or user base?
• Do they have paying customers, or are they pre-revenue?
• Is the product improving visibly month over month?

Betting on a platform that shuts down in six months is worse than not automating at all, because you'll have built processes and dependencies around it.

Red flags to watch for

In our evaluation process, we encountered a few patterns that consistently predicted a bad experience:

• "Works with any LLM!" — Usually means it works poorly with all of them. The best platforms are opinionated about their model stack.
• No free trial or requires a sales call to try. — If they won't let you test the product, the product probably doesn't hold up to testing.
• Claims of "99% accuracy" or similar. — AI output quality depends heavily on the task. Blanket accuracy claims are meaningless.
• No changelog or public roadmap. — Suggests the product isn't being actively developed, or the team doesn't want you to see how little has changed.
• Pricing that starts at $500/month. — Unless you're enterprise-scale, this suggests the platform is optimized for large contracts, not product-market fit.

Our honest recommendation

Try three platforms. Give each one a real task. Evaluate the output quality, reliability, pricing, and flexibility. Pick the one that produces the best results for your specific use case — not the one with the best marketing.

If that's Kaer, great. If it's not, that's fine too. The important thing is that you actually adopt automation rather than spending six months evaluating and never starting. The best platform is the one your team will actually use.