Aisle Task Builder: build complex AI automations rapidly

Hey guys, Mitch from Aisle here. I was just in the middle of running a bit of an experiment with our tasks feature, specifically the AI builder that is a part of the tasks feature, and I thought I'd start recording and take you on the journey with me. I wanted to put together a competitor deep dive research task, something that I can run with the inputs of a company name and a company website, and start to create context files that I can use in my other automations to understand the product positioning and marketing and feature set and pricing of other tools out there. And so, in one of my projects, I've gone back and forth until I've landed on quite a complex spec that I liked. You can see here that it runs through product positioning, feature overlap with Aisle, pricing and packaging, ICP and GTM, market signals including social's deep scan, strengths and weaknesses, and positioning wedge, and really pulling it apart.

And the output here is a synthesized chat, and also writing a context file that I can use elsewhere. And instead of me trying to build this, I thought it'd be a cool experiment just copy and paste this entire spec into the AI builder, which is something I hadn't done before. And so, I started doing that, as you can see here on the screen, we're kind of halfway through this run. So, on the right-hand side, if I scroll to the top here, you can see literally copied and pasted in that entire spec, right? And the AI builder has refreshed itself on the Aisle SDK.

It's looked at a bunch of my other tasks and inline prompts that have been built before, and now it has gone and it has written seven prompts that do the kinds of analysis that I want to do. And now it's written the main Python script and put that in there and fully built it. The summary of what it's built, says it's built a competitive intelligence analyzer complete build, six-phase pipeline, seven inline analysis prompts. So, pre-flight, it looks context from the competitive intelligence memory folder. >> >> Phase one, it does a scrape.

So, it's looking for a bunch of pages. If it doesn't find those pages, fail fast. Phase two, it's doing a web search and a Twitter search for news. Phase three to seven, >> >> there are a bunch of pillars that I have in in context about Aisle that it's looking for information in regards to. Phase four, it's kind of starting to summarize.

>> >> Phase five, it's creating the chat that has the output. And phase six, it's writing that context file. These are the prompts that it's created. This is how it wants to handle errors, right? These are the inputs that it requires from me.

>> >> And that's where I landed. This looked to my eye to be quite a long-running task, and so I just said add in some checkpoints and retries, >> >> right? It's done that, updated the script. Cool. So, we're going to hit accept.

>> >> Right. And this is the entire task here. I'm going to get out of the AI assistant. Okay. Memory folder competitive intelligence is not bound to this task.

You can see on the right here, it is pretty proactive in in finding errors. Memory folder competitive intelligence is not bound to this task. Okay. I actually don't think that memory folder exists. Let me check.

Right. So, if we come in here, it'll go to the tools tab. You can go to memory, we can look for competitive intelligence. It does not exist. So, we can create one.

I'm going to open this, right? And I create a new folder, competitive intelligence. Create that folder. Right, that exists. >> >> Come back in here.

Let's see if it shows up. Probably going to need a refresh, which means I'm going to have to save it, which is okay. If I go create task, saves it, >> >> come back to edit. Let's fix this, competitive intelligence. >> >> Bang.

Save. And add a note here. Added folder. >> >> Excellent. Just to go through the overall package that's been created here, right?

So, as you can see, you have the main script that we were looking at before. We have all of these inline prompts. What you'll find is the AI builder has a a pretty good a way of figuring out what models should go with each prompt. One way to think about it is it's really looking at the sizing of a prompt in the job, and it will determine like the appropriate model capabilities based on what it thinks it needs to do. But, you can see here for this product positioning prompt where it's it's written a system message and the user message.

It's >> >> looking at this. These are variables, by the way. So, it's saying, "This is the competitor." And then per run, the company name and the company website. This is the content that was scraped. >> >> This is our context that it's injecting.

And running it through this prompt. That all looks pretty good to me. You can see that it has put together a bunch of different things here. It has the Aisle product documentation memory folder as a tool. So, that's all the context that it needs.

You know what? Let's just try and run it. See how that goes, right? I'm going to hit run. It needs these two inputs, so that's great.

Try and hit enter. And we'll try https colon slash runtask. Right, this is the test run mode, so we kind of have a bit of a live debugger here. This is running. Don't know how long this is going to take.

Uh so, I will probably pause the video here and then come back with the end um the end results. Okay. So, I think we're back. All in all, it took about 8 minutes to run through the entire pipeline for this competitor. Let me see.

So, it says here down the bottom it's created a new knowledge base document competitor N8N. Great. It says that it quoted a chat. So, let's start by looking in the inbox. If I open this in a new tab, go to my inbox.

Cuz we're in test mode, I don't think it's going to be there. Look in memories. Should have the same output. All right, competitive intelligence one competitor N8N. Great.

It has their one-line positioning, their elevator pitch, claim differentiators, marketing tone and audience, primary audience technical practitioners developers DevOps engineers, automation model, classification canvas with code escape hatches, their primitives map to our primitives, key product pages found. It's great. Analyst notes. Uh very detailed feature overlap. Some gaps.

We win. We have strengths and they have strengths and all that kind of stuff. Pricing analysis. Usage metering. This is a very very fullsome document that I'm actually going to have to go through with a fine-tooth comb.

But, I think the interesting thing to think through is that the experiment kind of worked. Putting in a very very complex pipeline spec to the builder has worked. Where I'd usually go from here, where my brain's kind of going is like >> >> what I've proven is like structurally this thing kind of works and it works in the way that I want the automation to work, meaning like I'm giving a simple output and there is a a long quite fullsome pipeline >> >> that is producing very very in-depth document at the end. I think my next step would actually be to start manually maybe tuning these prompts. So, going through the output document, reading that, noting what I like and what I don't like, and then going through these prompts and shooting them manually, and maybe running again and again, and doing some other different outputs.

>> >> But anyway, fun experiment. If you haven't tried Tasks yet, please do. It's really really good at quite simple kind of automations, and obviously it's it's very adept at quite complex automations as well. Thanks.