AI touches every aspect of our lives with 100+ use cases. Since most of us have smartphones and laptops, we have all interacted with some form of AI powered software from Windows’ Cortana to Apple’s Siri. We may not have found them useful but B2B AI applications are more compelling with numerous benefits.
However, modern AI solutions need a degree of specialization since they are based on data. Since it takes significant effort to obtain the data and build a high performing model, there are still numerous areas where mature AI solutions do not exist. This makes companies turn to custom AI development as a solution.
What is custom AI development?
Custom AI development is the process of developing a company-specific AI solution targeting a particular problem. Since custom AI software is developed for a single business it needs to satisfy the business’ specifications and expectations. On the other hand, off-the-shelf or out-of-the-box (OOTB) AI software is a packaged solution sold by vendors to satisfy the needs of numerous organizations.
What are different types of custom AI solutions?
Custom software comes in 2 flavors and this is the same for AI:
- Configuring an existing open or closed source solution to better serve the needs of an organization: For example, most companies use ERP software from well-known vendors. However, given the diversity in the requirements of different companies, ERP systems need to be heavily configured. This configuration can take months but it is crucial for effective use of the software
- Creating a solution from scratch: Companies that have unique needs due to their focus area or their scale can choose to build custom solutions. These solutions may leverage existing software libraries.
When does a business need a custom AI solution?
Though businesses mostly achieve their AI transformation through off-the-shelf solutions, the AI market may not offer a specific solution to address their problems. Businesses can benefit from a custom AI/ML development when
- off-the-shelf solutions have limited performance: Different applications have a drastically different impact on financial performance. For example, improving sales effectiveness by 10% thanks to a better account prioritization solution would yield a lot more financial benefit than an automation solution that automates an infrequently used process. When an application’s financial impact is significant, it may be worthwhile to build a custom solution to achieve higher performance compared to an off-the-shelf solution.
- off-the-shelf solutions need significant configuration: This could be due to a lack of integrations or due to low initial performance of the off-the-shelf solution. Machine learning relies on data and if the company’s data is not similar to the training data of the model, model performance may be low. Companies could make up for this by providing more training data to the solution provider or by working with a consultant or data annotation team to further train the model.
- off-the-shelf solutions do not exist: AI is an emerging field and mature solutions do not exist in every business function or industry.
Which AI solutions should you build in-house?
Data powers most modern AI models. Companies with a significant scale could use their own data to build capable solutions in strategic areas. And they may want to make sure that their competitors never have access to these solutions. In-housing would be appropriate in such cases if the company can not secure exclusivity from vendors.
For more, feel free to read our article on in-house AI solutions.
What are the most common areas for custom AI development?
- Computer vision solutions: With the help of image recognition, organizations can customize solutions for various use cases such as automated visual inspections for quality assurance in manufacturing plants. We believe this area to be more relevant for custom solutions as each company has different quality assurance processes
- Conversational AI platforms: Chatbots that use Natural Language Processing (NLP), Natural Language Understanding (NLU) and Natural Language Generation technologies are important for businesses to improve customer service and several other business functions.
- Other machine learning solutions: Businesses can leverage developers to build custom machine learning solutions such as customer analytics, predictive maintenance and anomaly/ fraud detection. There are out-of-the-box solutions in most of these areas but companies resort to building custom solutions when out-of-the-box solutions do not exhibit high performance while working on the company’s specific challenges.
Who are the partners for outsourcing custom AI development tasks?
AI consulting services
AI consulting services help companies use AI technologies to improve their businesses. Thanks to their experience with numerous client projects, these companies can productize custom AI solutions for their clients. They can also help clients formulate an AI strategy, identify AI use cases and implement AI/ML solutions and provide training to client’s employees.
Starting a data science competition
Businesses can launch competitions to solve their challenges using crowdsourced AI labor force. Businesses define the problem, present data that crowd will use and offer a prize for the winner by using competition platforms. Data scientists develop customized AI/ML algorithms and solutions that can help tackle the specific challenge for businesses.
Launching AI competitions is challenging since it requires expertise in data encryption and access to external data science talent. Therefore, companies can get support from vendors like bitgrit that provide AI consulting and data science competition services to businesses for their custom AI needs. They identify AI applications, use the crowd to build high performing solutions and also help companies build in-house AI/ML teams.
If you want to take advantage of data science competitions to build low-cost, effective AI solutions, contact us:
ML development companies
There are software development firms that focus on AI and ML solutions. They combine data science expertise with practical domain knowledge to deliver integrated custom solutions to address real business challenges. These companies focus on specific areas like machine vision or conversational AI since the machine learning approaches that solve these problems are a bit different. For instance, Master of Code focuses on building conversational AI solutions for their clients.
As Andreessen Horowitz explains, most AI companies also offer services along with their products. Therefore AI product companies also provide ML development services based on their products.
As with any software development, companies can work with freelancers to build machine learning solutions. This approach is more difficult to manage than working with a separate company. However, it could result in savings. For more, feel free to check out our talent on demand research.
What type of custom AI development would be suitable for your project?
There are various ways to handle outsourcing custom AI development tasks, yet, you need to assess which approach suits most to your business needs. This will depend on the data to be used, management attention your company can provide the project and your company’s budget. When the company
- needs support in identifying the business aspects of its AI application, AI consulting companies can be good to work with. However, getting both strategy and implementation support will definitely increase the project’s budget.
- can find ways to encrypt its data with outside support and has already identified AI use cases, data science competitions can help build effective solutions economically while also improving the company’s brand as a recruiter. Data science competition organizers can also help companies identify ways to encrypt their data which will still allow data scientists to work on them
- is clear on how the AI model will support their business and can not encrypt their data in a way that supports model development, it needs to work with ML development companies or freelancers. If the company has experience working with freelancers and can afford the management attention to support them, freelancers could be a budget-friendly alternative to ML development companies
How to assess custom AI development partners?
Following outsourcing procurement best practices helps in choosing an AI development partner, too:
- References in the specific area: A computer vision company shouldn’t build your conversational AI solution. You should ask for example case studies in the specific area from outsourcing partners. This way, you can better assess partners’ capability to solve your problem.
- Geographic coverage: Building a conversational AI solution in Chinese may not be easy for a conversational AI company that never worked with the language. This needs to be taken into account especially in choosing conversational AI partners
- Other outsourcing procurement best practices which include evaluating companies based on Total Cost of Ownership, ensuring robust support and compliance with your company’s information security guidelines, etc. are also important
If you still have questions about custom AI software development, we would like to answer them:
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