5 solid reasons to outsource your AI software development
Customized AI software development is one of the most powerful approaches to leveraging AI in business. It brings a true competitive advantage by implementing solutions tailored to the specific challenges and business needs of the enterprise. Undoubtedly, the main difficulty of this approach is the ability to successfully develop and implement AI projects. Cooperation with an experienced AI vendor, who is responsible for end-to-end delivery, is one of the most effective solutions, bringing with it a number of benefits.
5 benefits of outsourcing AI & ML development
1. Cost reduction
One of the main drivers influencing the popularization of AI software development outsourcing is the significant cost reduction. The experience and know-how of the AI vendor ensure a transparent approach to project planning, implementation and related costs. Moreover, savings also come from reducing expenditure on hardware, workspace, training and employment. The client receives full support starting from needs identification to commercial deployment, as well as access to top tech talents.
At deepsense.ai we are flexible when it comes to the cooperation model. The first two are focused on a fixed price or time & material approach designed for customers who have clearly defined goals and requirements related to customized AI software development. In this case, the deepsense.ai team is responsible for the implementation of a specific solution or a component thereof. Where less specific projects are concerned – where in the first place the business use case needs to be identified and described in the data science language – the team augmentation approach is a better solution. It allows business owners to control costs at every stage and flexibly decide on the further development of the project. deepsense.ai’s people support the client’s internal team on a daily basis, as regular peers, and provide knowledge transfer. Such cooperation significantly increases the in-house software development capacity.
2. Flexibility
The diversity of AI and ML applications requires a plethora of experience and skills. Outsourcing provides a great deal of flexibility and confidence in testing new approaches. Even companies with in-house data science teams which are willing to execute multiple ML projects would need to spend a lot of time preparing and training for each implementation. Instead, they can work with an experienced vendor providing know-how about the latest technologies and possible solutions. At deepsense.ai, with over 100 world-class full stack developers, data scientists and software engineers on board, we provide effective support in the area of AI software development. Our people are equipped with the full tech stack required to help clients reach their milestones quicker, and ready to work hand-in-hand with internal business and technical teams.
Flexibility can be also provided by the agile approach to developing AI software. At deepsense.ai we work on the basis of CRISP methodology – a process model including six stages of running AI projects that leads to the right solution when repeated over and over again. This concept involves an adaptable attitude, where priorities and pathways change as project development progresses.
3. Access to top tech talents
The majority of enterprises are faced with a huge shortage of skilled AI talent. In particular, companies whose core operation is not related to AI may have a problem attracting world-class experts. Cooperation with an experienced AI vendor gives enterprises access to top tech talents without incurring the costs of recruitment and employment. Moreover, people with highly specific tech competencies can jump on board only when needed, for example just to streamline the process of data analysis or model training. At deepsense.ai we put great emphasis on acquiring and retaining the most talented AI/ML and data science experts. Our team members have backgrounds from top European technical faculties and have won international awards. Moreover, they develop tech competences not only through the implementation of commercial projects, but also through their involvement in the scientific development of AI/ML, academic activity and participation in prestigious data science competitions.
4. Focus on business value delivery
At deepsense.ai, the overriding goal of custom AI software development is not to deliver the software itself, but above all to provide the client with real business value. The continued success of a project depends on how deeply the given business use case is understood. That’s why at deepsense.ai we focus on close relations with business owners. From the very beginning of cooperation with clients, the entire team dedicated to the project participates in the discussion on business needs, possible solutions, and available data. This ensures that all team members have a broader awareness of the purpose, know the limitations, and are able to contribute both technically and conceptually. This approach makes it possible to efficiently determine the scope of cooperation, focus on low-hanging fruit first, and set up success metrics. The external team is independent and can objectively assess the chances of delivering business value in a given area. Such confidence combined with experience and agility in training ML models offers the best chance of success. As a result, clients remain focused on the core elements of their business and in the meantime get a custom AI solution tailored to their needs.
5. Faster results
While cooperating with an AI company, there is no need to take care of the necessary infrastructure or training, freeing up time for analyzing data, testing various approaches and building models. This significantly shortens the project implementation time. The vendor’s proven track record of building and implementing AI guarantees the delivery of first-class solutions. deepsense.ai’s portfolio includes more than 150 commercial projects for clients from the USA and Europe. Our commitment and know-how are valued by global companies including Nielsen, L’Oréal, Intel, Nvidia, United Nations, BNP Paribas, Santander, Hitachi and Brainly.
Artificial Intelligence outsourcing: final thoughts
Close cooperation with an AI vendor allows enterprises to maximize the potential of state-of-the-art technologies and focus on the industry-related aspects of building a competitive advantage. We might go so far as to say that truly remarkable results are not possible without combining in-house and outsourcing models – at least not without investing a great deal of time and significant financial outlays. Cooperation with a reliable AI vendor ensures a number of benefits, and also brings a fresh perspective on the data which is being analyzed.
