This is the third part of a guide for companies seeking the optimal way to implement an AI assistant. In Part 1, we compared AI Integrators with off-the-shelf solutions (SaaS – Software as a Service), and in Part 2, with Low-code/No-code integrators. This time, we present a comparison of solutions built from scratch by software houses with implementations offered by AI Integrators.
AI Integrators vs. Custom AI solution providers
We have reached the other end of the spectrum of creating dedicated AI solutions for business in our guide. Previously, we analyzed solutions using ready-made products or integrations of products available on the market. Meanwhile, there are also companies offering bespoke AI assistants, created from scratch.
We present the characteristics of bespoke solutions and compare them with solutions offered by AI Integrators such as KODA.AI, and we look at the advantages and challenges both of them bring.
Bespoke AI assistants, built from the ground up
In the era of increasing popularity of AI, some software houses have expanded their offerings to include the creation of dedicated AI solutions for companies.
Choosing this method of implementation seems to open up unlimited possibilities for the client company: because we tailor solutions, we can implement all the functions we want and perfectly adapt the newly created AI assistant to our current business infrastructure.
Tabula rasa – unlimited flexibility
In the case of bespoke solutions, we start from a tabula rasa – which gives us unlimited flexibility, but at the same time involves a long initial phase requiring the creation of a detailed specification of the tool to be developed. Before the software house proceeds to the implementation phase, it is necessary to develop an extensive list of required solution features.
Since software houses usually do not have their own AI technology – they can use existing components, which involves the limitations described in Part 2 of this guide, or create technology from scratch – in practice, implementing a solution from the ground up takes a considerable amount of time.
A tailor-made solution, however, can be a good option if the company can afford its own dedicated development team, which will not only create and implement the AI assistant internally but also maintain and actively optimize it after the deployment phase.
This type of solution may be especially desirable for businesses that absolutely cannot allow customer data and conversations to be processed by external providers.
Extended implementation phase
The implementation phase is most often associated with a quotation of working time counted in months, because the designed solution requires not only creating it from A to Z, but then also integrating it with the existing infrastructure of the client’s current tools.
A long implementation naturally involves the labor costs of UX designers and programmers, which significantly affects the total cost of implementing bespoke solutions. It will certainly be the highest among all AI assistant implementation methods presented in this guide.
Additionally, the design phase requires a considerable time investment from the client. To create a comprehensive solution, the developers will require information about the details of business processes, business goals, and specific client requirements.
Know-how
A characteristic feature of most software houses is their broad offer and lack of narrow specialization in solutions using AI. We may therefore be inclined to cooperate with a provider who has previously programmed a web application or an online store for us and now offers AI automation implementations.
This approach has its strengths – as the creators of our application, the developers will know it perfectly and, in theory, this knowledge should support them in implementing additional AI-related features. At the same time, the team that specializes in web application implementations or e-commerce solutions will not necessarily be a team of specialists in the field of AI automation.
AI and machine learning may be merely a project requirement, rather than a field of expertise, for a software house with broad specialization. In such cases, the knowledge base regarding AI solutions may be limited, as it is built on limited experience compared to providers specializing in implementation of AI-powered automations.
Therefore, when selecting a provider, it is worth verifying their level of experience in implementing AI assistants and choosing a contractor who offers AI-focused expertise and specializes in these types of solutions.
Contractor, or advisor?
In addition to implementation, an important factor to consider when choosing an AI provider is the scope of advice on AI automation. Since the aforementioned knowledge and experience base may be limited in the case of a software house, the provider of a bespoke solution will certainly focus primarily on the quality of the implementation itself.
They may offer advice based on their experience before implementation. They are unlikely to advise proactively after the implementation, on their own. Instead, they will probably enter the maintenance phase – fixing bugs and ensuring the continuity of the solution’s operation – rather than proactively optimizing the AI assistant’s effectiveness based on business results. Therefore, it’s worth discussing with your AI implementation provider the planned scope of consulting before, and ongoing support after, the implementation phase itself.
AI Integrators
The most important feature that distinguishes AI Integrators from all other types of providers presented in this guide is that they have their own proprietary AI-powered technology. This technology is continuously developed and updated, including the integration of the latest versions and new LLMs (Large Language Models). AI Integrators are eager to invest in the development of their technology because their entire client base benefits from these advancements.
Proprietary technology – faster implementation
Thanks to the existence of such a proprietary AI platform, the implementation of a solution tailored to the client’s business needs usually takes several weeks because the AI automation is not created from scratch.
The implementation is based on using the functionalities of the proprietary AI platform to create a solution tailored to the client’s needs, by developing automation and conversation scenarios, enriching the knowledge base, and integrating the solution with selected tools. The implementation time, from the client’s decision to a working solution, is therefore much shorter than in the case of bespoke solutions designed and implemented from scratch.
A shorter implementation time naturally translates into lower costs compared to the implementation costs of bespoke solutions.
At the same time, the wide range of ready-made automations available in the proprietary AI platform provides great flexibility in adapting AI assistants to the specific client’s needs.
AI experts
Unlike software houses with a broad spectrum of activities, AI Integrators focus on a specific technology – they create AI solutions only, not a wide range of products. This focus on a narrow field is reflected in highly specialized know-how and expertise in the field of AI.
Top-level AI consulting
This expertise, in turn, enables AI Integrators to provide professional consulting before, during, and after implementation – based on their experience from numerous previous deployments. Optimization proposals based on the analysis of solution results come from the AI integrator to the client – the client can, but does not have to, propose improvements themselves.
Proprietary technology also means proprietary analytics and the ability to analyze the implemented automation thanks to access to an analytical module for clients. At KODA.AI, we can take the initiative to improve our clients’ solutions because we continuously analyze the effectiveness of our solution thanks to the analytical module available in the platform. Our clients can also have access to the statistics of their solution.
Analytics is another element that AI Integrators have prepared for all their clients in advance, while bespoke solution providers have to create it from scratch for each client individually, or make integrations with external analytical tools.
Balance between solution flexibility and implementation efficiency
It seems that the balance between the possibilities of adapting the assistant to business needs and goals and the agility in its implementation is key when making the final decision on choosing an AI assistant provider.
The unlimited possibilities in designing an AI solution affect the design and implementation timelines (and still, the final solution may, in practice, end up being underutilized). A long implementation time translates into high costs, which unfortunately, do not necessarily guarantee the creation of a perfect solution.
However, a tailor-made solution may be necessary in cases where there is an internal development team or when there is no consent for data and conversation processing by an external provider.
Access to analytics, which allows assessing the real use of the AI assistant and the level of end-user satisfaction, is an important element of this whole picture.
Conclusions
It seems that efficient solution implementation, testing, and equally efficient optimization based on performance evaluation are the optimal implementation scenario. Before deciding to create an AI assistant from scratch, it is worth considering other implementation options and evaluating their capabilities in terms of: time and cost investment, performance evaluation, data security, and optimization capability.
If you are still wondering about choosing the optimal way to implement AI assistants or AI-powered automation in your company, talk to us.