Which AI solution fits your business best?

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If your problem is unique, your AI solution likely needs to be as well. But not every business has the time, budget, or tech team to start from scratch. So what’s the right move?

There are three main ways to approach AI implementation in your company:

• Build a fully custom solution tailored to your workflows
• Buy something off the shelf that solves a general problem
• Or find off-the-shelf solutions with limited customizations that gives you some flexibility

If you’re asking yourself “Should we build?”, “Should we buy?” or “Is there something in between?” - this article will help you figure it out before you start making any decisions.

A quick overview before we get into it

• Custom AI solutions: Algorithms, models, data pipelines, infrastructure - every piece is under your control. It’s a heavier lift, but it’s built around your exact needs.
• Off-the-shelf: AI tools built to serve a variety of different customers and generalized for common use cases. Easy to get started, but most of the time, it lacks flexibility.
• Tailored off-the-shelf: Solutions in between the two. It’s a solution that starts with a base model, then is fine-tuned or extended with your own data and logic.

Let’s talk through what you actually get with each one.

Custom AI solutions

Developing a custom AI solution means creating and training machine learning models tailored to your own data and designing needed algorithms, APIs, and infrastructure from scratch.​

This means you have the highest level of control when it comes to AI models and their outcomes, as well as their full ownership. By developing a custom artificial intelligence solution, you own the AI solution. Of course, once implemented, you have to consider maintenance and updates to your AI system.

Custom AI development typically involves:

• designing the right system architecture
• gathering high-quality data - for this part it’s important to understand where the data comes from, do you have the permission to use it, data cleaning and analysis, distribution as well as its complexity
• annotation - it’s crucial to organize it well - in-house or externally, inter-annotator agreements, education if needed, checking process
• experimenting and selecting the best model approach - fine-tune, train from scratch, open source or custom
• export and optimization for usage
• testing the model accuracy
• deploying and integrating with your existing stack
• testing through usage and user-feedback
• setting up ongoing monitoring, improvements, updates, retraining, adding new data

Benefits	Drawbacks
Tailored to your business needs and actual workflows	Higher upfront costs
Full control over models, infrastructure and data	Development takes longer
Aligned with domain-specific constraints	Requires strong data foundations

​​This is a rather strategic approach to implementing AI in your company. If your use case involves domain-specific logic, unique data, privacy constraints or workflows that no third-party platform supports, a custom AI system is the best solution.

Done well and with an experienced team, custom AI becomes a long-term business asset, not just a temporary fix. The upfront investment in custom AI development may seem high, but the value and the long-term benefits easily outweigh the cost. It’s a flexible, evolving system that grows with your business.

Our AI and ML team at Velebit AI, handles everything from scoping and data strategy to experimentation, deployment and maintenance. You get senior AI expertise, clear estimates and a long-term partnership. That’s why we start with a Discovery Sprint - a process where we explore your use case, check your data and figure out the right solution for your business, how long it’ll take and what it might cost.

AI projects aren’t easy to estimate down to the last sprint or euro. But experienced teams will get you close and flag what might change early on.

Off-the-shelf AI products

Ready-to-use products are pre-built AI tools with pre-trained models that serve the same needs of different customers and are integrated into their business’s processes.

They typically involve minimal development - the user configures the tool and feeds it data via an interface or API, but does not need to build the underlying AI models.

For example, off-the-shelf AI tools are built for common use cases such as document classification, chatbots, image tagging, speech-to-text, etc. If your problem matches one of those, you’re likely going to find a good enough pre-built AI solution. These tools can work out of the box with minimal setup.

But if your use case is even slightly outside the norm, expect limitations. Maybe the data format doesn’t match. Maybe you can’t retrain the model. Maybe the output logic needs adjusting, but the vendor doesn’t support it.

Benefits	Drawbacks
Quick to implement	Limited configuration
Lower upfront costs	Risk of mismatch with your workflow
Little to no in-house AI expertise needed	Doesn’t offer fine-tune control over data

Tailored off-the-shelf solutions

A tailored off-the-shelf AI solution combines ready-made AI tools with custom-built components - offering flexibility without the full cost of building from scratch.

You can fine-tune pre-trained models with your own data or extend modular tools to fit your workflows.

May involve some development effort, but significantly less than a fully custom AI solution - making it faster to deploy while still tailored to your certain business needs.

Benefits	Drawbacks
Faster development than custom AI	Still requires some internal technical skill
More relevant than generic AI tools	Costs more than off-the-shelf solutions
Easier to scale

This approach works well for companies with a few engineers in-house, a trusted tech partner and enough data to make model fine-tuning worthwhile.

A good example of this solution is our Product categorization solution. It is a pre-built AI model, but it can easily be fine-tuned on your own data and your categories, so you get as relevant outputs as possible. It’s a significantly less development effort and costs than a custom AI solution, while still being tailored to your business.

So, which AI solution should you use?

That mainly depends on three things:

1. Your data – is it messy, domain-specific or sensitive? The messier or more unique it is, the less likely a plug-and-play tool will work.
2. Your resources – do you have internal teams or partners who can build or adapt models? If not, you’ll need something easier to deploy.
3. Your use case – is this something lots of companies need, or something only you deal with?

If your data is clean, your problem is common, and your team is small – off-the-shelf is probably enough. If you have a tech team in place and your domain is niche, you are better off with a custom AI solution, and for some in between cases your best fit can be tailored off-the-shelf solution.

	CUSTOM AI SOLUTIONS	OFF-THE-SHELF AI SOLUTIONS	TAILORED OFF-THE-SHELF AI SOLUTIONS
DEVELOPMENT	Requires model training on your data, customized to your existing systems	Plug-and-play	Customization is limited to additional training of the existing AI model with your data
TIME-TO-LAUNCH	1-3 months	Instant	2-3 weeks (depends on the data quality)
DATA REQUIREMENTS	Custom datasets and collaboration	No additional data needed	Your data required for training
FLEXIBILITY	Maximum flexibility	Low to none	Selective customization
SCALABILITY	Highly scalable with full control	Scales on demand immediately	Scalable after model training
PRICING	High (time and materials based)	Low (subscription / pay as you go)	Medium (setup fee + monthly fee)

How Velebit AI supports all three approaches?

Custom AI solutions

We specialize in fully custom AI solutions across industries, with 10+ years of experience and more than 50+ projects we have successfully completed across many different industries. Take a look at our case studies to get a better idea of the projects we’ve worked on. Industries we’ve developed successful AI solutions for range from e-commerce, biotech, medicine, chemical industry, creative industries, media, banking, automotive and many more.

Off-the-shelf APIs

Color Detection & Fabric Pattern Detection APIs – tag color and fabric patterns in product images. You can try them out for free directly in our dashboard once you register.

Tailored off-the-shelf AI solutions

Our modular off-the-shelf solutions, such as Product Categorization and Recommender Systems, adapt to your platform and data. This way, you get the best out of both worlds - an affordable solution that is fast to deploy and tailored to your business.

Final thought

Start small. Scale fast. But don’t waste time with tools that won’t grow with you.

If you want real clarity on what kind of AI your business actually needs - talk to us. We’ve done it 50+ times across industries, and we’ll tell you what makes sense for your business.