If you're a founder, marketer, or creator, you need the best AI tools for the job. That usually means ChatGPT for writing, Claude for strategy, Perplexity for research, image generators for creative work, and video tools for content production. The problem is that stack gets expensive fast. But honestly, the bigger issue isn't even the cost. It's the constant tab switching, copy-pasting context between tools, and rebuilding the same workflow over and over again.
So I wanted to test whether one platform can actually replace all of that. I took a competitor's resource, reverse engineered their strategy, built an entire counter campaign, and ran the whole thing inside a single workspace. The tool I used is called i10x. The team reached out about sponsoring a video, but before agreeing I ran it through a few different workflows to see if it could genuinely replace the tools I normally use. Once I saw the potential, I asked if we could get a discount for the channel, and they came back with 50% off your first two months. So if you end up wanting to try it yourself, there's a code I'll drop later.
Let me walk through the entire process step by step, from competitor analysis to a reusable intelligence agent, so you can decide if consolidating your AI stack into one platform makes sense.
The real problem isn't cost, it's context switching
What first caught my attention about i10x is that it doesn't lock you into a single model. Inside one workspace, you get GPT, Claude, Gemini, Grok, Perplexity, image generation models, video generation models, and a bunch of specialized agents. For most founders, marketers, and creators, the real friction isn't paying for the subscriptions. It's constantly jumping between them. You do your research in one tab, writing in another, image generation somewhere else, and by the end of the project half your workflow is scattered across ten different sites.
The interface is straightforward. If you've used ChatGPT before, you'll feel at home immediately. On the left panel you've got chat, images, videos, agents, workflows, and a few other tools we'll use throughout this walkthrough.
Reverse engineering a competitor's strategy
For this experiment, I grabbed a competitor resource, could be an ebook, a lead magnet, a website export, anything that represents how they're positioning themselves in the market. I headed over to the document analysis agent and uploaded it.
Normally, understanding a competitor's messaging, audience, and strategy means spending hours manually reading everything. Instead, I basically turned that document into something I could chat with. The first thing I wanted to know was who exactly this company is targeting. That's critical because if you're going to compete, you need to understand who they're speaking to before you can figure out where they're vulnerable.
I asked i10x to break down the target audience, the primary pain points, and the core messaging themes throughout the document. Right away, it identified a few recurring themes. They're heavily focused on convenience, automation, and saving time. Makes sense. But what's more useful is what they're not talking about. One weakness the AI spotted: they spend a lot of time talking about features, not a lot of time talking about outcomes. They're telling people what the product does, but they don't do a great job explaining why that actually matters.
Then I asked it to identify gaps in their positioning and opportunities a competitor could use to differentiate themselves. This is where document analysis becomes seriously useful. Instead of manually reading dozens or hundreds of pages and trying to piece everything together yourself, you get a structured breakdown almost instantly.
The results showed a few clear opportunities. We could position ourselves as the simpler solution, the faster solution, or the more results-focused solution. For this example, I went with the results-focused angle. Now we have something actionable. We know who they're targeting, how they're positioning themselves, and where the gaps are. Next step: build a campaign around those weaknesses.
Claude vs GPT: which model builds better campaigns?
This is where the Chat Arena inside i10x gets interesting. Instead of guessing which model might give the best answer, you run the exact same prompt against two different models at the same time and compare the outputs side by side. I opened Chat Arena and put GPT on one side and Claude on the other.
I fed both models the competitor analysis we just generated and asked them to create a marketing campaign that directly attacks the gaps we identified. Let's see what came back.
Claude gave a really detailed strategic breakdown. It explained why the competitor's positioning is weak, what emotional triggers we should focus on, and how to structure the campaign overall. Claude feels more like a strategist.
On the GPT side, the response was a lot more direct. Actual headlines, hooks, ad angles, and messaging I could almost copy into a campaign immediately. GPT feels more like a copywriter.
Honestly, both are pretty good, but they solve slightly different problems. If I was trying to understand the market, I'd lean toward Claude. If I needed to launch something quickly, GPT's output is a little more usable right away. What I actually like here is that I don't have to choose one model forever. I can take the strategic thinking from Claude, combine it with stronger copy from GPT, and build something that's better than either individual output. That's exactly what we did. I pulled the strongest parts from both responses and turned them into a single campaign concept.
The campaign angle is simple. Instead of focusing on features like our competitor does, we're going to focus entirely on outcomes and results.
Visuals and video: bringing the strategy to life
Now that we have the strategy and messaging figured out, we need the creative assets to actually bring this campaign to life. I headed over to the image generation section. For the first pass, I used Nano Banana Pro with a simple prompt: a frustrated founder sitting in front of multiple dashboards, browser tabs, and analytics tools.
The result was actually pretty good. Composition looks solid, the character is believable, and the overall message is clear. You can immediately tell this person is overwhelmed by the number of tools they're managing. For social media graphics, ad creatives, and landing page visuals, that's more than usable.
Then I switched to Flux for a wider format, closer to what you'd use for a YouTube thumbnail or a hero image. One advantage of having multiple image models in the same workspace is that if one model isn't giving you exactly what you want, you can immediately try another without jumping into a completely different platform. Compared side by side, Nano Banana gave stronger editing and consistency controls, while Flux produced a layout better suited for a thumbnail or marketing banner.
Next: motion. Before jumping into video generation, I went back into chat and asked the AI to create a cinematic video prompt based on the campaign we've been building. I wanted something that tells a simple story: a founder overwhelmed by too many tools, too many tabs, and too many disconnected workflows eventually finding a simpler way to get things done.
The output was solid, camera directions, scene descriptions, lighting instructions, subject actions, and a clear narrative flow. This is a much stronger prompt than most people would write manually. I copied it over into the video generation section and used Kling to render it. The result came out better than I expected. Cinematic movement, good subject consistency, and the tone matches the campaign we're trying to create.
Now, video generation isn't always perfect on the first attempt. Sometimes you need to refine the prompt, adjust scene descriptions, or generate a second version. That's true of every AI video model I've used. But for something that started as competitor research less than an hour ago, having a usable marketing video this quickly is pretty impressive.
This is also where the value of having everything in one place starts becoming obvious. We went from competitor analysis to strategy to copywriting to image generation, and now video production without constantly switching between different tools. If you want to follow along with this workflow yourself, you can check out i10x using the link in the description and use the code Eric50 for 50% off your first two months.
From one-off project to repeatable system with agents
At this point we have enough assets to launch a campaign, but there's still one problem. What happens next month when that competitor changes their messaging, or when you want to analyze a different competitor? You could repeat the entire process manually, or you could build something that helps automate part of it.
i10x has a huge collection of pre-built agents covering document analysis, SEO audits, content generation, research, coding, and business operations. But instead of using a pre-built one, I created my own competitor intelligence agent. The goal is simple: whenever I upload a competitor document, landing page copy, marketing brief, or report, I want the agent to automatically identify their positioning, target audience, strengths, weaknesses, and opportunities.
I gave the agent instructions, defined the output format, selected the model that powers it, and saved it. Then I uploaded a new competitor document and asked for a breakdown. Instead of starting from scratch, the agent already knew exactly how I wanted the analysis performed. It followed the framework we defined and produced a structured report almost immediately.
For founders, marketers, agencies, and creators, this is where things start becoming really useful. You're not just generating content anymore. You're building repeatable systems that can handle recurring tasks for you. This is a very simple example, but you can extend it much further, customer support agents trained on your documentation, research assistants trained on industry reports, internal knowledge assistants, sales assistants, and a lot more.
What i10x gets right, and where it still falls short
After going through this entire workflow, I think the biggest value here isn't any individual model. It's the fact that everything lives in one place. There was no jumping between five different subscriptions, no copying context between tools, no rebuilding the workflow every time we switched tasks.
A few other things worth knowing if you're comparing it to other AI agent tools out there. First, almost everything runs under one bill. For most of the tools inside i10x, you're not bringing your own API keys. They just pass the cost through transparently, so you're not stitching together ten subscriptions and a pile of API billing. Second, it asks for confirmation before it does anything irreversible. That sounds small, but it matters a lot if you're a business owner nervous about turning a team loose on autonomous agents. And third, for common jobs like building presentations, lead generation, and SEO, they claim it comes out roughly 75% cheaper than the tools from the big AI labs, mainly because the architecture leans less on slow browser-based execution.
Now, if you're a hardcore AI engineer building specialized systems directly on top of model APIs, you're probably still going to want dedicated tools for maximum control. But for founders, agencies, creators, marketers, and most people who just want to get work done, i10x makes a pretty strong case for consolidating your AI stack into a single platform.
If you want to try it yourself, there's a link below and you can use the code Eric50 to get 50% off your first two months on any plan.
Key Takeaways
- The real pain with AI tools isn't the monthly cost, it's the constant tab switching, copy-pasting, and rebuilding workflows across separate platforms.
- i10x combines GPT, Claude, Perplexity, image generators, video models, and a document analysis agent all in one workspace, which removes that friction.
- A competitor document can be uploaded, analyzed for audience, pain points, and positioning gaps in minutes instead of hours of manual reading.
- Running the same prompt against two models simultaneously (Chat Arena) lets you pull strategic depth from Claude and ready-to-use copy from GPT, then combine the best of both.
- With multiple image generation models available instantly, you can compare styles and pick the best output without leaving the platform.
- Movie-ready video prompts can be generated from your campaign concept and rendered with tools like Kling, turning a strategy document into an actual ad in under an hour.
- Custom agents turn a one-time campaign into a repeatable system: upload a new competitor doc, and the agent produces the same structured intelligence automatically.
- Consolidating onto a single platform isn't about getting the absolute best model for every niche task; it's about keeping momentum and removing the operational chaos that kills speed.
The whole process, from a blank slate to a full campaign with visuals, a video, and a reusable agent, took less than an hour and didn't force me to jump between half a dozen different apps. If you're tired of stitching together subscriptions and just want to get things done, that alone is worth a closer look.