These Alli App Market Apps can help you to review and improve your privacy agreement, or to verify whether or not you have complied with company policies.
After Allganize launched the Alli LLM App Market, a lot of curious clients had questions about exactly how domain-specific LLM apps could help them to make their chatbots and digital assistants more efficient and accurate.
Domain-specific LLM apps have a ton of benefits over their more generic cousins, of course. Benefits range from understanding industry jargon, to reducing a phenomenon called AI hallucinations, to increasing the factual accuracy of queries.
So let’s talk about some of the applications created by Allganize that are available in the Alli LLM App Market.
Let’s have a look at five of the most popular apps in the Alli LLM App Market, so that they might serve as an example of what can be achieved with domain-specific AI learning.
Whether using it for your own company, or as a service to a third party, this app can help you to create a robust privacy agreement using AI research specific to your vertical.
Simply provide the app with a copy of the current privacy agreement. The tool meticulously reviews the contents, identifies potential gaps, suggests improvements, and ensures compliance with national and regional privacy laws.
The Human Resources Assistant app can streamline your HR processes by providing an employee-facing chatbot that answers their basic questions and provides management tools for their diverse needs.
Features include automated onboarding for new hires, leave management that ensures compliance with company policies, benefits administration for health insurance and retirement plans, employee performance metrics tracking, and HR feedback collection.
By providing access to income tax documents, the tax chatbot can do an analysis and answer questions about individual filings or company wide tax trends. It can also provide clickable citations that give the users a high-level summary of its findings, rather than having to delve through a section-by-section breakdown of the analysis.
By feeding in your expense claim, this app will verify whether or not you complied with the company’s expense policies. It will note if you went over any financial thresholds, and if you made any claims that are not covered by standard company policy.
Rather than rely on human memory or a ticketing system that might not have all of the details, this app sours a customer’s entire customer service email history.
It then generates a report with all of the key details and summaries of queries, both past and current. This allows user agents to craft informed responses to questions, and maintain a consistent message with the customer.
By narrowing the training and data ingestion scopes of domain-specific apps, there are some concrete benefits that can be realized.
For example, when using a general model, LLM generative AIs often get industry-specific facts wrong. They’re far more prone to hallucinations, simply making up things based on rumors and hearsay rather than using real world knowledge and factual sources that the industry in question trusts.
Instead, choosing industry-specific data ingestion results in a far more realistic and relatable chatbot or personal assistant. Because the scope of data ingestion is primarily limited to your specific industry, there are far less hallucinations.
The key to achieve delusion-free results is using diverse, well structured data from reliable sources. Without careful curation, there is no way to know what kind of strange behaviors an AI can pick up. You’ve probably seen news stories about AIs with unpredictable behavior ranging from paranoia to outright racism. This can be avoided with a tight training ruleset, and by using only carefully selected source data.
Many of the chatbots that are out in the wild come off as, for lack of a better term, robotic. They draw their answers directly from the company’s FAQ, they only delve into the most surface level cases, and their ‘bedside manner’ is appalling.
Contrast that to the conversational chatbots that are trained on Allganize’s domain-specific data. They move fluidly from subject to subject. They delve far beyond the company’s help pages. They’re able to pick out the more difficult and frustrating situations and ask if the customer would like to escalate the issue to a human agent.
The reason for this difference is that domain-specific AI models can draw upon millions of trusted external resources, specifically curated for your field. Going back to our legal example, imagine if the answers that a chatbot gives are rooted in knowledge gleaned from Supreme Court rulings, the Cornell legal archives, Findlaw, the Federal Reporter, and a dozen other trusted industry sources?
This is the power of using a well tuned but resource-diverse industry savvy chatbot, such as the ones available in the Alli LLM App Market.
First of all, Alli offers a lot of diverse LLM choices. Of course the popular OpenAI models such as GPT-3.5 and GPT-4 are on offer. Antrhopic’s Claude is quite popular as well. But they also offer their own internally developed models such as Alli sLLM. All of these LLMs are available in an on-premises format.
Second, each industry is defined with billions of internal parameters that define core aspects of training, data ingestion, and customer interactivity. This is how you avoid the severe hallucinations and misinformation spread by other, more generic, AIs.
The modular nature of the Alli LLM App Market means that once your first AI application is built, you can take what you learn from the first app and apply that knowledge to other AI functions across your enterprise. For example, you might pair your company’s employee-facing chatbot with an AI Human Resources assistant, trained with similar data pools. Then the two can seamlessly pass matters off to one another, as employees shift from procedural and technical questions to matters that are better handled by HR.
Finally, you choose your own deployment method to suit your particular IT model and security needs. Deployment preferences that you can pick from include SaaS, on-premises hosting, and hybrid models that combine the flexibility and cost savings of the Cloud with secure and ultra fast local hosting for internal deployments.
If you have any questions about the apps available in the Alli LLM App Market, contact Allganize for a free consultation. An advisor will be able to pinpoint your needs and advise you on the best course of action for your company.