Julie Swan
8
min read
Jul 25, 2024
In the modern business era, AI is not only the new cool but also a cornerstone of success. More than 92.1% of businesses have have seen measurable results from Data and AI in investments.
But here is what they are doing wrong: They are using off-the-shelf AI solutions. Many of them (aka. boxes) cannot keep up with current business trends. They are generic, provide limited customization, are hard to scale, inflexible, and pose various security threats.
The better alternative? Custom AI Solutions. These AI applications are tailored for you, provide flexibility, performance boost, and can give you an upper hand in market competition.
Key Takeaways
Unlike the general off-the-shelf AI, Custom AI provides specific, tailored solutions to your business needs and challenges
Investing in Custom AI will give you a competitive edge over other businesses in the market
Custom AI is scalable, flexible, more secure and efficient
AI service company can help you create a secure, high-quality, and tailored AI solutions
Why Must CEOs invest in custom AI now?
Given the competitive market and the need for concrete business strategy, now is the perfect time for CEOs to invest in Custom AI.
An annual global study of around 3000 CEOs from over 30 countries and 26 industries found that 72% of them believe advanced generative AI has a competitive advantage in the market.
Rightly so, if you do not want your business to be left behind in the race, hop into the AI train.
There are other drawbacks to the ‘wait and watch’ approach besides falling behind: your operations would be inefficient, business decisions would be suboptimal, you would miss many opportunities, there would be security risks, and eventually, this would hurt your brand image.
“It’s a delicate balancing act between late adopters who may miss the golden growth opportunities and early adopters who risk making impulsive moves that could backfire.” – Lisa Heneghan, Chief Digital Officer, KPMG International.
What is custom AI development?
Custom AI development basically refers to the creation of tailored AI solutions that will help businesses perform specific tasks and solve problems. These applications may also help with work automation, inventory management, and customer services, to name a few.
Custom AI, as its name suggests, is a completely different entity when you compare it to off-the-shelf AI solutions. Many businesses do not understand the differences between the two types of AI solutions and make mistakes when choosing them.
Let us look at a few differences:
Usability: As I have repeatedly said, Custom AI is specifically tailored for the problems you encounter in your business. So, it might provide an exclusive and helpful solution. However, off-the-shelf AI solutions provide general solutions that may or may not help you.
Scalability: The development of Custom AI solutions depends on your business's needs and, sometimes, its future potential. So they can be scaled according to the needs. Also, you will have complete control over the scaling management. However, off-the-shelf solutions may not provide that benefit.
Cost: While the initial cost of AI development and deployment of Custom AI might be high compared to the other, it will provide you with greater value for money in the long run.
Integration: Custom AIs can be seamlessly integrated with your business's current systems, while off-the-shelf ones will be difficult to integrate.
Security: As Custom AI is tailored for your business, the developers will deploy the required security during development. In off-the-shelf AI, you do not have the control over security.
Adaptability: Custom AI solutions are highly adaptable to your current business needs and change according to the demands. However, off-the-shelf will not provide that benefit.
Now, to build a custom AI, you need to keep in mind the components, which are briefly explained below:
Problem definition: First, you need to identify the problem that prompted the development of Custom AI. Then, create the required Key Performance Indicators (KPIs) and check whether they will solve the specific problem/problems.
Collection of data and their preparation: Gather the data from various sources, such as the aforementioned KPIs and user inputs. Remove the unnecessary noises and factors that will add no value, and then label the data depending on what needs supervised learning and what doesn’t.
Feature selection: Now, you must identify which feature might be helpful to feed into the AI model.
Training the model: Based on the requirements, choose the suitable model for your business and train it using the data prepared above.
Assess the performance: Deploy the AI system using the evaluation techniques and check for its performance in different scenarios. Update the model and data and see if the AI adapts to it.
Deployment: Deploy the AI into a working environment where it can be fed data, make decisions, and give the required output.
Monitor the model: Assess the deployed model's performance and update it periodically to make it more suitable for your business environment.
Ethical audit and governance: Make sure your AI model is aligned with government policies. It should be fair, transparent, and unbiased.
Documentation: Document the aspects of the AI model, including all the research, development, and data collection processes.
Gathering feedback: Gather feedback from the users and stakeholders to check for shortcomings in the deployed AI model. This step identifies the updates required to the models.
How can it benefit your business?
We have been pretty vocal about the benefits of custom AI up until now. But have you ever wondered how it specifically benefits your business?
Let us look at some of the benefits:
Solves your problem, not someone else’s: As you tailor custom AI for the benefit of your company, it will provide you with specific solutions that will solve your problems specifically.
Boosts efficiency: Custom AI can help boost the efficiency of your organization by automating the task and freeing up your team. What's more, it will also get more tasks done.
Knows your customers better: AI's feature of learning from data helps you get better consumer insights. This will help you personalize experiences for them and win their loyalty.
Gives you the edge: By better implementing AI in your business, you are sure to stand out from the crowd.
Grows with you: One of the great features of Custom AI is adaptability. It will not fall behind when the business scales; instead, it will evolve with the business.
Your data, your rules: You have complete control over your data, which will, in turn, provide greater security.
Building a custom AI solution
Building a Custom AI involves all the necessary steps, from problem identification to deployment of the final product. As a business, this is one aspect that requires a structured plan for successful implementation.
Identifying AI opportunities
AI has repeatedly proven its worth by helping businesses solve problems and create better market opportunities. As a CEO, you must first identify which sector of your business is inefficient and faces significant challenges.
You need to study the current data and ponder over whether AI can solve your problems and improve overall decision-making in your business. On top of that, you also need to look if the AI project you are looking forward to aligns with your business goals and objectives.
Data and statistics are readily available to analyze in the current business scenario. As a CEO, you need to assess the data and analyze your business needs. If you extract quality reports from your data, the AI model you aim to create will learn accurately and provide better decisions and solutions.
You should also focus on prioritizing AI initiatives based on their importance and availability to your company. For this, let us look at a structured framework to prioritize AI initiatives so that your project has a bigger positive impact:
Business Impact: Look at the benefits the AI initiatives would provide. It should bring positive impacts like cost control and savings, revenue generation and growth, and overall consumer satisfaction.
Data quality and availability: Properly assess whether the data you are working with is readily available. The success of various projects depends upon data availability.
Technical Feasibility: To better prioritize, you need to see if the project you are trying to work on is feasible and can be developed within your organization. In this structure, you also need to analyze the cost of AI development and check whether it falls within your budget.
Alignment with goals: Before anything else, you need to examine your company goals and check whether the AI initiative aligns with them.
Risk analysis: You need to evaluate the risks associated with AI initiatives. If the risk has potential rewards, take it.
Timeline allocation: For every AI project, you must assess the required resources and timeline. Then, choose a project that will use time and resources efficiently.
“We’re training machine learning models, foundation models, to make sense of data to find connections that no human ever could.” – Chris Gibson, CEO of Recursion.
The AI development process
After deciding to start developing the AI model, you need to follow the steps for successful development. Let us look at the steps:
Data collection and preparation: You need to focus on collecting and analyzing the data to feed the AI model. In this process, you need to filter out the data that is irrelevant to the model. Also, the raw data can be converted into usable data, and the most relevant features can be selected.
Model training: In this step, you need to choose the AI model depending on the nature of your problem. Feed the model with the data you have prepared. Also, it must be validated so that it can work even with new data.
Testing and refinement: You need to use a dataset that the AI model has not seen yet so you can evaluate how it reacts to new data. Check the model for any errors it makes and tweak the algorithms and parameters to fix them. Reiterate the model after the necessary adjustments and see if the errors persist.
Deployment and integration: In this step, you must prepare the environment to host the models. After this, you can deploy them into the system by making the necessary adjustments. Finetune the model to perform efficiently in the given system. Integrate the model with the system and work on its security. Provide final documentation to the concerned team.
Partnering with AI Experts
Now that you have a clear picture of what Custom AIs can do, you might want to look in the right direction to build one.
Here is when the AI professionals come to the rescue. These individuals bring expertise and skills to the table. Having worked in this field, they have the strategic approach to efficiently handle AI technologies and build a perfect model for your business that can handle your problems.
The developers will ensure that the application is technically sound, reliable, secure, and meets all coding standards. Given the resources and time allocated, they are bound to check its efficiency and scalability.
Whatever the needs of your business are, the data scientists will work around that to ensure the model is on par with your goals and objectives.
In-house vs. outsourced development
For the development of Custom AI, you have to choose either of the two: In-house development or outsourced development.
In in-house development, the organization utilized its own resources and personnel to develop, deploy, and maintain the model.
Whereas in outsourced development, you hire external agencies or get help from external organizations to develop a model.
To be able to choose, you need to understand the difference in detail. Here are a few of them:
In-house Development
Expertise
The internal team must be adequately trained.
Cost
The initial costs, including training and infrastructure, will be higher and might increase with time.
Deployment Time
You will have to start from the very scratch, from hiring to development, so the deploy might take time.
Customization
As this is done inside the business, customization becomes relatively easier.
Ownership and Control
While developing in-house, you will have complete control of the project.
Scalability
This method's model will be more scalable according to the business's needs, but more resources are required.
Communication
The communication is seamless in this development method.
Support and Maintenance
As the team is already available, timely support and maintenance will occur
Quality
The quality might be top-notch if it goes according to the initial plan.
Outsourced Development
Expertise
Top-notch people with experience will already be available.
Cost
The cost is usually fixed beforehand. So, there are no hidden cost.
Deployment Time
Deployment might be faster given the resources are usually available with pre-established process.
Customization
The outsource team will make necessary amendments according to the requirements via structured communication channels.
Ownership and Control
For outsourced development, you will have a shared ownership that strengthens collaboration all while sharing responsibilities for project success in limited control.
Scalability
The scalability depends upon the contract period. In long term contracts, the scaling will be systematic.
Communication
Periodic communication is one of the main problems of outsourced development. But, timely meetings and feedback loops will solve that.
Support and Maintenance
In long-term commitments, they usually provide support and maintenance.
Quality
Usually the product is top-notch as the agencies are experienced, but it all comes down to the agency you’re taking help from.
How much does AI development cost?
The cost of AI development depends on several factors, such as the project's complexity, the team, data preparation, tools, and support.
To state the amount, the development might cost $10,000 - $50,000 for small projects. This increases to $50,000 - $200,000 for medium projects, whereas the larger projects take about $200,000 - $500,000+.
However, as I said earlier, the cost is associated with different factors. Let's look at them in detail below.
Complexity of Project: The cost might be minimal for a simple project like developing a chatbot or prediction model. However, it increases with the project's complexity. Mid-range complexity includes Natural language processing (NLP) and image processing. The most complex projects require deep and intensive learning and data processing.
The team: This includes the salaries and training of the in-house developers or the Custom AI development cost of the outsourced development.
Data preparation and collection: This included the cost of collecting data, noise-proofing, and refining it to make it suitable for the model.
Tools and infrastructures: This includes all the costs incurred in the Custom AI development process, from cloud hosting to tools, frameworks, and licenses.
Recurring maintenance and support: This includes the cost of regular maintenance, such as bug fixes, updates, and continuous monitoring.
Let us look at the cost of a project of medium complexity:
Planning & Development: $5,000 - $20,000
Data collection & Preparation: $15,000 - $50,000
AI Model Development & Training: $15,000 - $100,000
Integration & Deployment: $15,000 - $35,000
Testing & Validation: $10,000 - $25,000
Ongoing Maintenance & Support (yearly): $10,000 - $50,000
What are the metrics for measuring AI success?
It is very useful to set up intelligible indicators for measuring success when embarking on AI projects because they show how effective the project has been so far.
This leads to smooth progress without any interruptions, enabling stakeholders to understand why certain funds are needed.
Such criteria connect an enterprise's or organization's needs and goals with its artificial intelligence progress, guaranteeing practical gains from this kind of automation system.
These measurements ensure that different areas, such as efficiency and accuracy, can be monitored, enabling informed decisions and resource distribution, hence optimization where necessary.
Besides, clear key performance indicators are important in surmounting obstacles caused by human errors.
Choosing relevant metrics is very important to evaluate the success of AI applications. Let us take a look at three main metrics:
Cost savings: If your business sees a significant reduction in operations costs due to implementing AI, it can be considered successful.
For example, in a Supply Chain Optimization AI, you will have to check the percentage reduction in logistic costs after the AI optimizes them through inventory management, route planning, and forecasting demand.
Productivity: Check for improvement in task efficiency and completion rate. Also, the speed of output processing should be monitored to see if downtime is reduced.
For example, in an HR and Recruitment AI, you will have to monitor the time taken to fill the vacancies that AI does through automatic candidate screening.
Customer Satisfaction: Evaluate if your customers are satisfied with your service. This can be done by monitoring retention rates, Net Promoter Score, ratings, and retention.
For example, in a Retail Recommendation AI, you can check the number of return purchases made by consumers to measure the success of the recommendations made by AI.
Monitoring and evaluation
For the success of any AI initiative, timely evaluation and continuous monitoring of the project is necessary. By doing that, you can check whether the AI is performing according to the plan and make the required adjustments to ensure it meets the business standards.
Key Performance Indicators (KPIs) are important for businesses to track performances against clear metrics. For this, you need to define KPIs that are aligned with your business goals and objectives. You can also employ different analytical tools to collect and measure data to see if they are aligned with the defined metrics.
Define intervals for reviewing the gathered data and generating reports that will eventually help you perform the necessary tweaking in the project. This process also keeps you, and the stakeholders informed about the AI.
To adapt and refine the solution based on data insights, you can follow the steps below:
Data analysis and interpretation:
To do this, you must collect the data from your model using system logs, metrics, and user interactions. Then, you must apply advanced machine learning algorithms and predictive modeling to gain impactful insights. After that, you must analyze the data and look for patterns, anomalies, trends, and correlations to gain insights into user performance.
Iterative Improvement Process:
Iterative improvement refers to making continuous changes in the AI system based on the collected insights and data. Here, you must identify the performance gap and optimize the key functions of the system that align closely with the business objectives.
Continuous Monitoring and Feedback Loop:
Monitoring the system continuously for its performance will let you identify the changes that need to be made. You also need to take feedback from the stakeholders, team, and end users to know whether the AI is performing ideally or not. Remember the KPIs you made earlier? You need to go back and check whether they are executed or not.
Collaborative Approach:
You must foster collaboration between all parties involved, such as developers, stakeholders, domain experts, and data scientists. Also, the knowledge must be shared between the concerned parties. This ensures the AI is refined on both technical and strategic grounds.
How to choose a perfect AI development partner?
Choosing the perfect AI development company is crucial to building a perfect AI that aligns with your business goals.
Let us look at the factors you must consider when choosing an AI development partner:
You need to look at the feedback and reports from previous clients to check the partner's track record.
Ensure the partner is proficient and has a strong background in AI, which may include machine learning, NLP, deep learning, etc.
Ensure the partner is proficient with the required AI tools and make sure they are up-to-date with the latest trends.
Check the project methodology they use, their time management skills, and their communication skills.
Considering the security and development ethics, the partner should ensure they can develop a tailored AI for you.
You need to ensure the partner provides clear costs and expenses and avoids any hidden costs. They also need to provide value for money with the product without breaching the contract.
Real-world examples
Well, we are not just talk and no work; we have the perfect example of our work for you to see.
We developed a custom LLM model for a leading name in the content creation business: SurferSEO.
Let us look at the challenges we faced, how we solved them, and the outcomes below:
Content creators were spending too much time in research, writing, and SEO optimization
Solution
We helped reduce the content creation process by 85%
Outcome
Users saw a significant increase in productivity by generating high-quality content in a few clicks
AI tools in the market did not produce SEO-optimized content
Solution
We helped reduce the content creation process by 85%
Outcome
Users saw a significant increase in productivity by generating high-quality content in a few clicks
Making sure that the generated content was factually correct and informative
Solution
We developed a 30-step knowledge-gathering and fact-checking pipeline to guarantee high-quality content
Outcome
Surfer AI produces high-quality articles that even surpass the quality of human writers
Constantly changing State-of-the-art technology
Solution
We continuously integrated the latest model, including the GPT-4-32k, to avoid being left behind
Outcome
Surfer was the first company in the market to integrate advanced technology
Updating the AI solution based on the user and internal feedback
Solution
We added an architecture that ensured real-time Machine Learning improvements
Outcome
Surfer’s user satisfaction took a record leap as it doubled since the launch
To find out more about our LLM in SurferSEO, click here.
Taking the first step
As you already know what custom AI brings to the table, deep down, you may want to use its benefits.
Well, why not? It is tailored for your needs, efficient, safe, flexible, and may understand your business better than you. It is a win-win, right?
So why wait? To learn more about Custom AI and how it can help automate your business and generate more revenue, join us for a call now.
