Sodima Solutions started as a generic software development company. At first we developed websites, applications, and artificial intelligent (AI) chatbots. We enjoyed getting involved with clients from different industries and every project became a new experience. Every project taught us different approaches for the software development process and we continuously managed to deliver better solutions for our clients. – Agile software development & a strong team makes it happen!
With all the excitement about different projects, new opportunities and cool things that we learned along the way, it was difficult for us to scale our operations. It became a challenge to leverage what we had learned from previous projects and it was difficult to become great at something specific that our customers could recommend us for. In other words, constantly reinventing the wheel slowed down our growth. - It’s time to nail a niche.
Together with one of our first clients Structurely we are developing a SaaS chatbot solution for the real estate industry. Working together with the Structurely team on this project has taught us about the challenges that come with the conversational user interface. Working through lines of code with the team of a promising chatbot start up is no joke. – Oh! and they want to disrupt the real estate industry.
Considering the current chatbot landscape it seems extremely difficult to find a good value proposition for this technology. Most chatbots that we encounter don’t deliver on a good user experience. Some bots fail the conversational part while other bots don’t parse valuable data. In other words, the bot doesn’t have the valuable data that it can turn into useful information for the user of the bot.
Then again, there’s always this question if chatbots end up costing us more, or less time than a well-designed website. It’s obvious that chatbots are very different than websites, but we still have to draw this comparison as websites are currently the default solution of consumer behavior when it comes to contacting a company.
We like to believe that we encounter the same issues that other folks in the chatbot community are struggling with.
We share the pain, it’s difficult, it’s messy, and at the same time we rely on platforms like Facebook Messenger to fuel the growth of the chatbot industry.
It is our goal to work on chatbots that battle these issues and allow to solve real problems for businesses. We are eager to find ways to enhance the experience of chatbots in general and provide an experience that retains users.
Our plan is to specialize in chatbot development for the “bot as a service” and e-commerce space, targeting companies (like Structurely) as well as larger businesses that want to invest in a conversational e-commerce experience to enhance their customer support capabilities.
Regardless if chatbots are over-hyped or not, chatbots are the result of a change in consumer behavior. E-mail was for desktops and mobile chat is for the smart phone eco-system. Website are the way to communicate with brands on the web and chatbots are the way to interact with brands on messaging platforms. Most of our communication currently happens on messaging platforms like iMessage, WhatsApp, FB Messenger, Slack, Telegram, WeChat, KiK, Viber and many others that follow the messaging trend.
Most of these platforms offer an app store that allows an easy download and seamless integration of the bot with the platform. Chatbots that are not native to the messaging platform can easily be integrated with the right API configuration. From there it is possible to contact the chatbot just like you would contact another person. Chatbots can also live on websites where they can additionally enhance the experience of the site, remind customers of special product offerings, allow for quick transactions and much more depending on the functional design of the bot.
Besides their powerful integration capabilities with almost any communication channel, chatbots deliver a great experience for companies that have a large request for customer support. The bot can answer mundane but frequently asked questions (FAQ) to help the customer service workload. Machine learning (ML) enables the bot to remember the intents from previous conversations and increase its natural language processing (NLP) capabilities to deliver the best answers. In other words, it becomes easier for the bot to understand the conversation and answer appropriately.
What ever you do, give it enough sweat, love and patience.