Apogee Suite: AI-Powered Legal Document Research Platform
By VICTOR ANJOS
Executives planning on deploying AI systems must look at how well an NLP platform works and how to implement the system.
You have many options to explore when getting a new AI system running. The work will involve several steps, so your business must have a suitable plan for making it all work.
What Will the Implementation Process Involve?
The process of producing a new AI setup will require multiple steps. It involves correcting the proper data, establishing new algorithms for the AI to follow, and training it to where it understands what you want it to analyze.
The work will involve a few steps:
• You have to sort the structured and unstructured data. The data should be completely structured so the AI can identify patterns or other terms.
• New algorithms are necessary for allowing AI to predict or classify information. A programmer will create these algorithms.
• Sentences and terms require segmentation for the AI to understand what it will review. A programmer must establish a layout where the AI can create tokens to help identify certain words while stemming these words to understand their concepts.
• Additional algorithms can help the AI define the input it receives and memorize it for future use. The ability to learn new concepts based on any new content it gathers will also be essential for its success.
The work will require various programmers and analysts to help see how well a system works and what to expect from the process.
Can In-House Development Work?
The process of developing your AI system in-house is the easiest to manage, as you can request people who already work for you to run your AI development and deployment process.
There are many points for running an in-house system for work that you can enjoy:
• Customization is easy because your workers can program a setup based on your needs.
• Since the development process is in your control, you will own the intellectual property of the AI system you develop.
• The in-house developers will also adhere to your company culture. They will work with unique concepts or values for running an AI program.
This option is appealing, but it is also not without its faults:
• There’s no guarantee your in-house developers will understand how to produce an AI system.
• The cost of in-house development will be higher due to added overhead costs, training charges, and employee benefits you would owe your workers.
• You may not have easy access to all the necessary development tools without spending more money to start.
What About An Off-the-Shelf Platform?
An off-the-shelf platform is a readymade platform you can order and install for your business. You can find various off-the-shelf AI programs, including many AI as Service solutions you can use on demand.
One of these readymade solutions can work for many reasons:
• It’s less expensive because everything in your system is already open for use.
• You won’t require extensive technical knowledge to use an AI platform. Many prepared solutions will include guides to help you see what you can do with a setup.
• Some systems can be customized to fit your needs. You can select what AI systems or functions you want a platform to use as necessary.
But as helpful as an off-the-shelf platform can be, it might not always be accurate. You might have to work extra to make the system functional and ready for your needs. There’s also a chance the system might not include the features you want, requiring you to hire someone else to help create something new.
The cost to acquire a program will also vary by provider, with some options being wildly different from others. There’s also the concern that not all off-the-shelf programs are open-source choices that your employees can adjust as necessary.
Professional Contractors Can Help
The third option for deploying an AI system involves hiring a professional contractor to assist you with the work. A contractor team can help you determine what systems you can use for your AI setup and implement something that fits your needs.
The positives of hiring professional services for your development plans include these:
• It may cost less for you to hire a contractor to work, as many professionals operate in different countries where less expensive labor is available.
• You can provide details on whatever needs you have and then allow your contractor to create something new. The contractor’s work will be as personalized as necessary.
• Many professionals will have a greater understanding of how AI works than your employees. You can trust someone’s expertise in creating a new setup that fits.
But as great as a professional contractor can be, there are also a few problems to note:
• A contractor won’t have the same company culture as you, so it might be hard for you to get what you want.
• It is harder to get an outside developer to change your system as necessary.
• The timing for preparing a system can be a while, as your developer might have other tasks to manage as well. There’s no guarantee your developer will be completely focused on your tasks.
Can You Mix All the Above?
You can always use a mix of all three options if you prefer. You can develop a part of the AI platform yourself and then hire a contractor to create a user interface that can manage your system. There’s also the option of acquiring a readymade system and having an in-house team review the programming code and adjust it as necessary, especially if you’re using an open-source solution.
A Final Note
Be sure when looking for an AI system that you know what you’ll do when deploying a setup. The work you put into managing an AI setup can be extensive and require plenty of time, effort, and money. But the right call in determining how you will develop your setup will help.
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.
