AI is transforming industries across the board, but here’s the reality: off-the-shelf solutions like ChatGPT aren’t enough for businesses that demand real precision. While these general AI models are powerful for handling broad tasks, they start to fall apart the moment you need them to manage the intricate, specific details that define industries like finance, healthcare, agriculture, or manufacturing.
AI is making a big difference in agriculture, for example. We’ve got farmers using AI to predict crop yields, identify diseases early, and optimize how they use resources like water and soil nutrients. Robots can now sort and harvest ripe crops, while AI-powered weather models are helping farmers figure out the best times to plant and harvest. This data-driven approach is transforming farming, making it more efficient and cost-effective.
In healthcare, AI is revolutionizing diagnostics and treatment. Algorithms can now analyze X-rays and MRIs to provide faster, more accurate diagnoses. AI-driven drug discovery is speeding up the identification of new treatments, and robotic surgeries are more precise and less invasive than ever before. These advancements are literally saving lives and reshaping how healthcare operates.
Finance is another industry where AI has become indispensable. It powers everything from fraud detection to automated trading, sifting through massive datasets in real-time to spot anomalies that humans might miss. Banks rely on AI to detect fraud, assess credit risk, and even advise clients through robo-advisors that can make smarter investment decisions faster than any traditional approach.
But here’s the thing: if you’re operating in any of these sectors—or any industry where accuracy is critical—generic AI models simply won’t cut it. It’s like trying to fit a square peg into a round hole. The moment you need AI to handle specialized tasks or deliver insights specific to your business, you’re going to run into problems. That’s where custom AI solutions come into play.
Let me walk you through how businesses approach this. Some start with something called Retrieval Augmented Generation (RAG), which connects AI to a company’s own internal knowledge base. Instead of relying on the internet for general answers, it uses the company’s proprietary data, making it more accurate for specific queries. RAG can increase accuracy by up to 40%, and it’s a good starting point for companies looking to boost AI performance. But it’s not a magic bullet—RAG can handle basic questions but struggles with more complex decision-making, like optimizing a hospital’s treatment plan or managing real-time supply chain issues.
For businesses looking to go beyond basic, there’s fine-tuning. This is where companies take a general AI model and train it with their specific data. Take Bayer, for example. They’ve fine-tuned AI to give farmers real-time advice on managing crops, using Bayer’s own research and data. The result? Much more accurate and useful recommendations. But even this approach has its limits—you’re still working with a general-purpose AI, just one that’s been reprogrammed to perform better in your field.
Now, if you want AI to truly work for you, custom AI is where the magic happens. This is where companies like Vention come in, building AI systems from the ground up that are specifically designed to meet the unique needs of your business. Imagine an AI that doesn’t just understand your industry but is built to master your company’s specific challenges. Whether it’s processing complex financial data, managing logistics, or optimizing manufacturing, a custom-built AI does what no generic model can. It speaks your language, works with your data, and integrates seamlessly into your workflows.
Let’s take Rocket Companies as an example. They’re creating custom AI for their mortgage business. Their system understands mortgage applications inside and out, from legal terms to property descriptions. It doesn’t just guess—it knows. And that kind of precision can save them millions by cutting down on mistakes and inefficiencies. That’s the level of impact a custom-built AI can have.
And yes, let’s be upfront—custom AI isn’t cheap. You’re looking at a significant upfront investment, anywhere from $500,000 to millions, depending on how complex your needs are. But here’s the thing: what you get in return is a system that’s tailored perfectly to your operations, evolves with your business, and cuts down on the risks of costly mistakes. For industries where accuracy is everything—healthcare, finance, manufacturing, even logistics—it’s an investment that pays off not just in money saved, but in gaining a serious competitive advantage.
This is where Vention excels. With Vention’s custom AI solutions, businesses get more than just technology—they get an AI system that’s designed specifically for them. Vention doesn’t stop at surface-level AI tweaks. They take the time to understand your industry, your data, and your goals. Whether it’s a financial service firm needing better fraud detection, a retailer looking to optimize customer engagement, or a logistics company seeking supply chain efficiencies, Vention tailors AI to fit your business perfectly.
Vention’s approach goes beyond just solving problems. Their AI creates opportunities. A custom AI solution isn’t just about cutting costs—it’s about future-proofing your business. When your AI is built from the ground up, designed to integrate with your systems, and able to grow alongside your company, you’re not just keeping pace with the competition—you’re setting yourself apart. You’re staying ahead. And that’s something you won’t get with a generic AI solution.
To summarize, if you’re in an industry where precision is paramount—whether it’s healthcare, finance, agriculture, or manufacturing—Vention’s custom AI solutions are the way forward. Off-the-shelf models or even fine-tuning can only get you so far. But a custom-built model gives you long-term value, precision, and scalability that simply can’t be matched.
AI is a powerful tool, but it’s only as good as how well it’s designed to serve your needs. And if you’re serious about leveraging AI to its full potential, customization is not just a perk—it’s essential. With Vention’s approach, you’re not just investing in technology; you’re investing in a smarter, more competitive future for your business.
I agree, it would be nice to have task-specific models. And I'd use them with great pleasure- I don't need to ask for cooking recipes while development- it would be much more profitable if some small model helps me on my local machine without sending requests to the third-party providers
I think there are a few factors that are slowing down the development of bespoke AI solutions.
- Training and deployment complexity: Task-specific models will require separate training pipelines, datasets, and updates for each individual task. This can become expensive and complex compared to maintaining a single, larger model that can generalize across multiple domains.
- Data availability: For certain niche tasks, collecting enough high-quality data to effectively train small models can be challenging. However, general-purpose models benefit from large, diverse datasets, allowing them to apply contextual knowledge to many tasks.
- Performance tradeoffs: While small models may be more efficient in terms of inference speed, they often lack the depth of understanding and flexibility of larger models.
- Cost of multi-featured models: As long as these companies with frontier models raise enough money from investors and continue to operate at a loss, the era of omni-models will continue.
I think what we see currently is a search for some glass ceil. As a big research phase to find as many possibilities of LLMs as possible.