Apogee Suite: AI-Powered Legal Document Research Platform
By VICTOR ANJOS
Today’s chatbots are highly advanced, as you can use conversational AI programs to analyze a client’s linguistics to determine the response and tone necessary for a conversation.
The potential to save money by using a chatbot instead of a traditional customer service department is significant, as the chatbot can communicate with people at any time. But as appealing as a traditional chatbot can be, it may be harder to plan than you realize. A chatbot will require more configuration than conversational AI, plus it may cost more to prepare than AI.
According to Tidio, developing an in-house chatbot can cost at least $10,000, with the cost being even higher when you require more functions out of your setup. The need to constantly refine and adjust the chatbot can be frustrating, as a chatbot might not fully understand the data a user might provide.
Conversational AI bots can learn and understand customer requests and messages. These can configure themselves after a while, boosting their ability to respond to customer needs.
tl;dr Give me the Goods
Conversational AI Can Learn Data Better
First-generation chatbots aren’t going to cut it these days. Today’s chatbots need to be capable of interpreting messages well while collecting customer data. The ability of a chatbot to personalize a message and understand more of what the customer wants can also be a challenge. Additional programming is necessary to allow a chatbot to stay functional and helpful, plus it has to understand trends in what people require.
According to TechTarget, conversational AI uses more advanced technology to analyze customer requests and data. First, it uses Natural Language Processing (NLP) to find data. NLP helps review speech and text to see what meanings a person has. It uses the context of the customer’s speech to determine what actions are necessary.
Meanwhile, Machine Learning (ML) can help the AI produce accurate responses to automate various tasks. ML can help the AI learn new details to improve future interactions while also predicting outcomes based on responses a client provides. The historical data available makes it easier for the AI to interact well with the client.
The problem with traditional chatbots is that they require extra adjusting on one’s own to improve how well they can respond to customer queries. Chatbots may not understand natural language, as they only look at certain keywords.
Whereas older chatbots are transactional ones that facilitate interactions with a fixed set of options, a modern conversational AI program will be more human-like. It can access multiple knowledge bases to understand the context of whatever someone says.
Capturing Info Helps
Older chatbots are capable of processing information and sending requests. But they may not be capable of moving their data to databases that can collect customer info.
The Chatbot Business Framework writes that you can link general chatbot data into an SQL database. You can host a database on your server, although a cloud platform might be more convenient. The database can sort your info based on context, data type, and purpose.
But the data that the chatbot collects may not be based on dialogue datasets or question-and-answer sets. iMerit states that conversational AI programs can provide more detailed reports for databases. The use of various datasets for AI training can help the AI learn how to respond to different questions or identify when people have specific needs for work.
Simple Deployment
The deployment of a chatbot or conversational AI program can influence how well it works. You can establish goals for your platform, including looking at how your program will interact with people and serve various business objectives.
Whereas a chatbot will require a rigid series of rules for how it will interact with people, a conversational AI program can work with most functions that are easier to deploy. These include:
- Recording communications
- Searching old communication logs for trends and ideas
- The inclusion of filters or triggers to train the AI
- The ability to highlight keywords you feel are essential based on what people are saying
Conversational AI is easier to deploy because it requires fewer control measures on your end. While you can adjust the AI as needed, it will not require as many adjustments as what a regular chatbot may demand.
Integration is still critical when getting conversational AI to work. You can integrate your platform to various outside programs to allow the AI to check business inventory, prepare email lists, link to social media feeds, and recommend different items or services to customers based on what they request.
The Convenience Is a Necessity
LeadDesk writes that nearly two-thirds of people prefer to start communicating with a business through a self-service platform. The belief is that communicating with a chatbot helps narrow things down to where they can talk with a very specific person in the event they need real human help.
But it may cost less to use a conversational AI program than it would to use a chatbot alongside a customer service department. The AI program will learn from the customer’s needs and provide help without requiring the customer to talk to an actual person.
Chatbots are very convenient and useful, but expect to spend more money and time than you might anticipate to adjust and configure your chatbot. Conversational AI provides a simpler approach to work that helps you keep the communication virtual, saving money on customer service efforts.
Apogee Suite of NLP and AI tools made by 1000ml has helped Small and Medium Businesses in several industries, large Enterprises and Government Ministries gain an understanding of the Intelligence that exists within their documents, contracts, and generally, any content.
Our toolset – Apogee, Zenith and Mensa work together to allow for:
- Any document, contract and/or content ingested and understood
- Document (Type) Classification
- Content Summarization
- Metadata (or text) Extraction
- Table (and embedded text) Extraction
- Conversational AI (chatbot)
Search, Javascript SDK and API
- Document Intelligence
- Intelligent Document Processing
- ERP NLP Data Augmentation
- Judicial Case Prediction Engine
- Digital Navigation AI
- No-configuration FAQ Bots
- and many more
Check out our next webinar dates below to find out how 1000ml’s tool works with your organization’s systems to create opportunities for Robotic Process Automation (RPA) and automatic, self-learning data pipelines.
