Introduction 

Large Language Models (LLMs) are considered to be an AI revolution, altering how users interact with technology and the world around us. Especially with deep learning algorithms in the picture data, professionals can now train huge datasets that will be able to recognize, summarize, translate, predict, and generate text and other types of content.

As LLMs become an increasingly important part of our digital lives, advancements in natural language processing (NLP) applications such as translation, chatbots, and AI assistants are revolutionizing the healthcare, software development, and financial industries.

Therefore, to get a closer view of the challenges, we will discuss the four limitations of LLMs devise a decision to eliminate those limitations, and focus on the benefits of LLMs. 

1. Limitations of LLMs in the Digital World

We know that LLMs are impressive technology, but they are not without flaws. Users often face issues such as contextual understanding, generating misinformation, ethical concerns, and bias. These limitations not only challenge the fundamentals of natural language processing and machine learning but also recall the broader concerns in the field of AI. Therefore, addressing these constraints is critical for the secure and efficient use of LLMs. 

Let’s look at some of the limitations:

1.1. Contextual Understanding

LLMs are conditioned on vast amounts of data and can generate human-like text, but they sometimes struggle to understand the context. While humans can link with previous sentences or read between the lines, these models battle to differentiate between any two similar word meanings to truly understand a context like that. For instance, the word “bark” has two different meanings; one “bark” refers to the sound a dog makes, whereas the other “bark” refers to the outer covering of a tree. If the model isn’t trained properly, it will provide incorrect or absurd responses, creating misinformation.

1.2. Misinformation 

Even though LLM’s primary objective is to create phrases that feel genuine to humans; however, at times these phrases are not necessarily to be truthful. LLMs generate responses based on their training data, which can sometimes create incorrect or misleading information. It was discovered that LLMs such as ChatGPT or Gemini often “hallucinate” and provide convincing text that contains false information, and the problematic part is that these models point their responses with full confidence, making it hard for users to distinguish between fact and fiction.

1.3. Ethical Considerations 

There are also ethical concerns related to the use of LLMs. These models often generate intricate information, but the source of the information remains unknown, hence questioning its transparency in its decision-making processes. To add to it, there is less clarity on the source of these datasets when trained, leading to creating deep fake content or generating misleading news.

1.4. Potential Bias

As LLMs are conditioned to use large volumes of texts from diverse sources, they also carry certain geographical and societal biases within their models. While data professionals have been rigorously working to keep the systems diplomatic, however, it has been observed that LLM-driven chatbots tend to be biased toward specific ethnicities, genders, and beliefs.

2. Addressing the Constraints of LLMs

Now that we have comprehended the limitations that LLMs bring along, let us peek at particular ways that we can manage them:

2.1. Carefully Evaluate  

As LLMs can generate harmful content, it is best to rigorously and carefully evaluate each dataset. We believe human review could be one of the safest options when it comes to evaluation, as it is judged based on a high level of knowledge, experience, and justification. However, data professionals can also opt for automated metrics that can be used to assess the performance of LLM models. Further, these models can also be put through negative testing methods, which break down the model by experimenting with misleading inputs; this method helps to pinpoint the model’s weaknesses.

2.2. Formulating Effective Prompts 

The way users phrase the prompts, the LLMs provide results, but with the help of a well-designed prompt, they can make huge differences and provide accuracy and usefulness while searching for answers. Data professionals can opt for techniques such as prompt engineering, prompt-based learning, and prompt-based fine-tuning to interact with these models.

2.3. Improving Transparency and Removing Bias

It might be a difficult task for data professionals to understand why LLMs make specific predictions, which leads to bias and fake information. However, there are tools and techniques available to enhance the transparency of these models, making their decisions more interpretable and responsible. Looking at the current scenario, IT researchers are also exploring new strategies for differential privacy and fairness-aware machine learning to address the problem of bias.

Final Thoughts

LLMs have been transforming the landscape of NLP by offering exceptional capabilities in interpreting and generating human-like text. Yet, there are a few hurdles, such as model bias, lack of transparency, and difficulty in understanding the output, that need to be addressed immediately. Fortunately, with the help of a few strategies and techniques, such as using adversarial text prompts or implementing Explainable AI, data professionals can overcome these limitations. 

To sum up, LLMs might come with a few limitations but have a promising future. In due course of time, we can expect these models to be more reliable, transparent, and useful, further opening new doors to explore this technological marvel.

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The Katara platform, which leverages a mix of traditional ML/NLP alongside cutting-edge foundational models and Generative AI, significantly streamlines community and developer onboarding, while surfacing actionable analytics. Teams can offload repetitive and fragmented workflows to AI-agents across disparate platforms saving them time. This has already resulted in substantial efficiency gains for several early launch partners, notably saving over 8,700 hours annually for AVAIL Protocol, Hivemapper, and Filecoin.

“Katara AI, tuned for developer content and tasks, helped us to get clarity on where gaps are in our developer experience so we can better focus our funding on addressing and enhancing [it.] Katara provides a solution to quantify DevRel problems that were otherwise impossible to compare. I will take it wherever I go.” – Fmr Filecoin Developer Advocate – Jenks Guo, now Head of Developer Relations of Babylon Labs

Matthew Rossi, Cofounder and CEO of Katara, commented on the funding, “With this significant investment we’re poised to build the stack that has been missing for DevX teams across Web2 and Web3. Our human-in-the-loop approach brings maximum efficiency and safety to agentic workflow automation.”

David Feldman, Cofounder and CTO of Katara, also commented on the funding, “This investment will enable us to scale our solution globally, leverage the latest AI techniques, enable incredible content collaboration, and ensure that our customers receive the most meaningful and actionable insights.”

The need for better AI-powered workflows for DevX teams, like Developer Relations, is clear and growing. Teams are under-resourced and burn a ton of time and resources on repetitive Q&A, custom educational content creation, multi-format content delivery, among other manual processes. An inability to deliver accurate and timely content experiences negatively impacts traction with developers and impedes the overall growth of a project’s value. Nearly half of developer relations professionals surveyed by the State of DevRel report in 2023 reported experiencing burnout in the past year, with content creation cited as the top challenge.

Ken Nguyen, Partner at Diagram, added, “Katara’s holistic approach to automating developer workflows with GenAI is groundbreaking. Matthew is the perfect founder to build Katara given his depth of experience at Polygon, Chainalysis, and other web3 and AI companies.”

Thibaut Chessé, Partner at Sparkle Ventures, added, “Katara sits squarely within our thesis for how GenAI tools can solve problems with how web3 communities grow and blockchain networks accrue value. We are thrilled to support their journey as they expand their innovative platform to new markets.”

With the seed funding, Katara will expand its reach beyond web3 to include major web2, AI, and open-source platforms. The tools developed by Katara will turbocharge the efficiency of developer experience teams in all of their workflows.

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Greetings Robert, Could you please share with us your professional journey and how you came to your current role as Chief Innovator of Monjur?

Thank you for having me. My professional journey has been a combination of law and technology. I started my career as an intellectual property attorney, primarily dealing with software licensing and IT transactions and disputes.  During this time, I noticed inefficiencies in the way we managed legal processes, particularly in customer contracting solutions. This sparked my interest in legal tech. I pursued further studies in AI and machine learning, and eventually transitioned into roles that allowed me to blend my legal expertise with technological innovation. We founded Monjur to redefine legal services.  I am responsible for overseeing our innovation strategy, and today, as Chief Innovator, I work on developing and implementing cutting-edge AI solutions that enhance our legal services.

How has Monjur adopted AI for streamlined case research and analysis, and what impact has it had on your operations?

Monjur has implemented AI in various facets of our legal operations. For case research and analysis, we’ve integrated natural language processing (NLP) models that rapidly sift through vast legal databases to identify relevant case law, statutes, and legal precedents. This has significantly reduced the time our legal professionals spend on research while ensuring that they receive comprehensive and accurate information. The impact has been tremendous, allowing us to provide quicker and more informed legal opinions to our clients. Moreover, AI has improved the accuracy of our legal analyses by flagging critical nuances and trends that might otherwise be overlooked.

Integrating technology for secure document management and transactions is crucial in today’s digital landscape. Can you elaborate on Monjur’s approach to this and any challenges you’ve encountered?

At Monjur, we prioritize secure document management and transactions by leveraging encrypted cloud platforms. Our document management system utilizes multi-factor authentication and end-to-end encryption to protect client data. However, implementing these technologies hasn’t been without challenges. Ensuring compliance with varying data privacy regulations across jurisdictions required us to customize our systems extensively. Additionally, onboarding clients to these new systems involved change management and extensive training to address their concerns regarding security and usability.

Leveraging cloud platforms for remote collaboration and accessibility is increasingly common. How has Monjur implemented these platforms, and what benefits have you observed in terms of team collaboration and accessibility to documents and resources?

Monjur has adopted a multi-cloud approach to ensure seamless remote collaboration and accessibility. We’ve integrated platforms like Microsoft, GuideCX and Filevine to provide our teams with secure access to documents, resources, and collaboration tools from anywhere in the world. These platforms facilitate real-time document sharing, and project management, significantly improving team collaboration. We’ve also implemented granular access controls to ensure data security while maintaining accessibility. The benefits include improved productivity, as our teams can now collaborate efficiently across time zones and locations, and a reduced need for physical office space, resulting in cost savings.

In what ways is Monjur preparing for the future and further technological advancements? Can you share any upcoming projects or initiatives in this regard?

At Monjur, we’re constantly exploring emerging technologies to stay ahead. We continue to training our Lawbie document analyzer and are moving toward our goal of being able to provide real-time updates to our clients legal documents.  

As the Chief Innovator, what personal strategies do you employ to stay abreast of the latest technological trends and advancements in your field?

To stay current, I dedicate time each week to reading industry reports, academic papers, and blogs focused on AI, machine learning, and legal tech. I also attend webinars, conferences, and roundtable discussions with fellow innovators and ch leaders. Being part of several professional networks, provides me with valuable insights into emerging trends. Additionally, I engage in continuous learning through online courses and certifications in emerging technologies. Lastly, I maintain an open dialogue with our  team and regularly brainstorm with them to uncover new ideas and innovations.

What advice would you give to our readers who are looking to integrate similar technological solutions into their organizations?

My advice would be to start by identifying your organization’s pain points and evaluating how technology can address them. Engage your teams early in the process to ensure their buy-in and gather their insights. When selecting technology solutions, prioritize scalability and interoperability to future-proof your investments. Start small with pilot projects, measure their impact, and scale up based on results. It’s also crucial to foster a culture of continuous learning and innovation within your organization. Finally, don’t overlook the importance of data security and compliance, and ensure that your solutions align with industry standards and regulations.

With your experience in innovation and technology, what are some key factors organizations should consider when embarking on digital transformation journeys?

Embarking on a digital transformation journey requires a clear strategy and strong leadership. Here are some key factors to consider:

1. Vision and Objectives: Clearly define your vision and set measurable objectives that align with your overall business goals.
2. Change Management: Prepare for organizational change by fostering a culture that embraces innovation and training teams to adapt to new technologies.
3. Stakeholder Engagement: Involve all stakeholders, including clients, to ensure their needs and concerns are addressed.
4. Technology Selection: Choose technologies that offer scalability, interoperability, and align with your specific business requirements.
5. Security and Compliance: Implement robust security measures and ensure compliance with relevant data protection laws.
6. Continuous Improvement: Treat digital transformation as an ongoing process rather than a one-time project. Regularly assess the impact of implemented solutions and refine your strategy accordingly.

By considering these factors, organizations can navigate the complexities of digital transformation more effectively and reap the full benefits of their technological investments.

Robert Scott

Chief Innovator at Monjur

Robert Scott is Chief Innovator at Monjur.  He provides a cloud-enabled, AI-powered legal services platform allowing law firms to offer long-term recurring revenue services and unlock the potential of their legal templates and other firm IP. redefines legal services in managed services and cloud law. Recognized as Technology Lawyer of the Year, he has led strategic IT matters for major corporations,  in cloud transactions, data privacy, and cybersecurity. He has an AV Rating from Martindale Hubbell, is licensed in Texas, and actively contributes through the MSP Zone podcast and industry conferences. The Monjur platform was recently voted Best New Solution by ChannelPro SMB Forum. As a trusted advisor, Robert navigates the evolving technology law landscape, delivering insights and expertise.

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Syncro, a leading B2B SaaS company serving the managed service provider (MSP) and IT markets, today announced the commercial availability of its AI-powered Smart Ticket Management solution. Integrated seamlessly into its existing platform, this advanced system is purpose-built to enhance MSPs’ and IT professionals’ efficiency in resolving tickets, significantly reducing the time and effort required.

Built on the feedback from early adopters who experienced marked improvements in operational efficiency, Syncro’s new offering is now available to all partners on the Team Plan within its IT management platform.

Syncro’s AI-powered Smart Ticket Management not only classifies and suggests resolutions for incoming tickets but also introduces an intuitive Smart Search capability, enhancing the way technicians manage and resolve tickets. This feature allows technicians to find solutions more quickly with context-aware searches and a natural language interface.

The Smart Ticket Management feature set is designed from the ground up to integrate seamlessly into the daily workflows of technicians. By automating the initial steps of ticket management — such as classification and suggesting initial resolution steps — Syncro streamlines the process for technicians. It also offers a robust search tool that uses natural language processing (NLP), which ensures that every technician, regardless of experience level, has the tools to excel at their fingertips.

“Our Smart Ticket Management solution is transformational for the MSP and IT markets,” said Michael George, CEO, Syncro. “By leveraging AI to automate and simplify the ticket resolution process, we’re enabling our partners to achieve greater efficiency and productivity. This launch represents our ongoing commitment to providing innovative solutions that meet the evolving needs of our customers.”

Key Features and Benefits

Smart Ticket Search

A powerful ticket search engine leverages NLP to find the most relevant tickets based on a query. This tool understands and finds tickets that match the intention and core issue, even if the keywords don’t match. Smart Ticket Search also works in context, automatically finding tickets closely related to the ticket currently being viewed, ensuring that relevant information is always available.

Guided Ticket Resolution

• Automatic Ticket Classification – When a ticket is created (either by a user or by an alert), the solution automatically classifies the ticket based on its contents. For example, tickets may be labeled for issues related to printers, network, storage, memory, CPU, and other categories (up to 47 different categories at release).
• Guided Resolution Steps – Syncro’s Smart Ticket Management system creates guided resolution steps with a convenient checklist embedded in the ticket. Many tickets include links to scripts and automations, enabling technicians to resolve issues with a simple click.

Positive feedback from early users underscores the impact of Syncro’s solution.

“Smart Ticket Management bridges the skills gap between Level 0 and Level 1 technicians, providing my team with clear, actionable solutions from the first touch,” said Jasper Grewal, president of ROI Technology. “The Smart Search feature shows incredible potential in reducing time spent on cross-ticket investigation, while the guided ticket resolution empowers our junior techs to find the correct solution more quickly.”

“Syncro remains dedicated to continuous innovation, and this release is evidence of our journey towards fully leveraging automation and AI to enhance IT solutions,” said Dee Zepf, chief product officer, Syncro. “Our unique approach of embedding AI deeply into the platform is setting a new standard in the industry.”

The new Smart Ticket Management capability is available now as part of Syncro’s all-in-one platform.

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AtScale, a pioneering leader in data management and analytics, announces a significant breakthrough in Natural Language Processing (NLP). By integrating AtScale’s Semantic Layer and Query Engine with large language models (LLMs), AtScale has set a new standard in Text-to-SQL accuracy, achieving an impressive 92.5% across all combinations of question and schema complexities.

As enterprises generate and store increasing volumes of data, the demand for quick, accurate data analysis has never been higher, outpacing traditional methods reliant on human analysts. AtScale’s integration of Generative AI transforms natural language queries into precise SQL commands, dramatically improving efficiency and decision-making speed. While LLMs excel at generating human-like text, they often struggle with complex database schemas and business logic. AtScale’s Semantic Layer bridges this gap by providing LLMs with comprehensive business-side metadata, eliminating the need to create metrics from scratch or generate complex joins, and significantly enhancing result consistency and accuracy.

“Our integration of AtScale’s Semantic Layer and Query Engine with LLMs marks a significant milestone in NLP and data analytics,” said David Mariani, CTO and Co-Founder of AtScale. “By feeding the LLM with relevant business context, we can achieve a level of accuracy previously unattainable, making Text-to-SQL solutions trusted in everyday business use.”

In rigorous testing, AtScale’s integrated solution outperformed traditional methods by a wide margin. Across a diverse set of 40 business-related questions, the solution achieved a 92.5% accuracy rate, compared to just 20% for systems without the Semantic Layer. These results underscore the system’s capability to handle a wide range of query complexities with superior precision.

Key Benefits of AtScale’s Solution:

1. Enhanced Accuracy: Achieves 92.5% accuracy in translating natural language questions into SQL queries.
2. Simplified Query Generation: Removes the need for LLMs to generate joins or complex business logic, reducing errors and improving efficiency.
3. Business Context Integration: Provides LLMs with essential business metadata, ensuring consistent and accurate results.

AtScale is committed to continuously advancing its AI-driven solutions. The company plans to enhance the integration further by optimizing prompt engineering and expanding training datasets, aiming to tackle even more complex queries with greater precision and efficiency. By doing so, AtScale seeks to empower businesses with increasingly robust and reliable data analysis tools.

You can download the full report here, which includes detailed statistics, charts, and a deeper dive into the analysis.

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OpsVeda, the leading provider of Operations Command Center (OCC) today announced that Juni, the Generative AI powered copilot for operations team-members, is now available on the Microsoft Teams Store. Juni leverages natural language processing and domain expertise to interpret users’ business questions, queries the right information from the OpsVeda real-time intelligence platform and presents responses in line with users’ potential need. Juni(or) serves as a ‘virtual operations analyst‘ to augment the front-line user to make timely decisions without the necessity of waiting for a central pool of experts and their daily spreadsheet routine. Timely decisions by the front-line, powered by predictive intelligence will help enterprises minimize revenue miss and margin leakage.

By continuously optimizing demand-supply-inventory-logistics alignment, the OpsVeda Operations Command Center helps execution focused teams to exploit opportunities and manage risks towards daily, weekly, monthly and quarterly targets. Operational execution is time-sensitive and reliant on specific details. For operations teams, having real-time information and analysis at their fingertips often makes the difference between a successful or a missed month/quarter.

With Juni on Teams, OpsVeda makes detailed operational prescriptions and information more accessible to users at large, without necessarily being dependent on the central analyst pool. The user does not have to navigate to dashboards. Be it orders that are due to ship today or high priority containers arriving today, or perhaps transactions at risk of cancellation this week, the user just needs to ask Juni (through Microsoft Teams) in everyday business language – just as she would ask an informed colleague. And if there is something time-sensitive like a soon to be out-of-stock situation, Juni will proactively push an alert message to her through Teams.

Juni responses incorporate data visualization and snippets of specific transactions (in the form of information cards) to enable users to validate the answers. Direct navigation from the Juni message to the relevant filtered OpsVeda storyboard, or perhaps downloading the specific set of impacted materials or transactions are also enabled, allowing the user to quickly evaluate the responses, and take corrective actions. The user may also schedule the response to be delivered on a regular cadence.

“The depth and breadth of business content has always been OpsVeda’s strength. Real-world operations need to factor in a large number of variables and our algorithms have been automating predictive analysis for the user,” said Sanjiv Gupta, CEO of OpsVeda. He added, “With Juni’s availability on a platform as popular as Microsoft Teams, we are now simplifying operational execution for an even larger pool of enterprise users. OpsVeda is purpose built for helping operations and supply chain teams maximize their revenue and margins. Short to medium term operational execution demands agility and timing is of the essence. Juni on Teams enables faster and democratized access to critical information and opportunities unearthed by the Operations Command Center.”

“Large Language Models and Generative AI has dramatically improved the ability to interpret user requests and create code and other content on the fly. We have married this new technology to our rich repository of business functions developed over the years,” said Ravi Mandayam, VP – Product Engineering at OpsVeda. “Juni is being used by some of our customers and the effect has been magical. The ease of information access has enabled them to respond to changing operational situations much faster. The number and variety of queries that Juni has been answering has grown much faster than most of us imagined. In operations velocity and accuracy of information is everything!”

OpsVeda confirmed that apart from MS Teams, Juni is available on Slack also. Enablement of the Generative AI Assistant on other collaboration platforms is in progress.

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Accenture (NYSE:ACN) today announced the launch of the Accenture AI Refinery framework, built on NVIDIA AI Foundry, to enable clients to build custom LLM models with the Llama 3.1 collection of openly available models, also introduced today.

While enterprises are exploring the power of gen AI, they have to distill and refine the underlying LLM models with their own data and unique processes. The Accenture AI Refinery framework, which sits within its foundation model services, marks a significant step forward in the use of generative AI for enterprises. It will enable clients to build custom LLMs with domain-specific knowledge and deploy powerful AI systems that reflect their unique business needs and help drive reinvention of their business, and their industry.

Julie Sweet, chair and CEO of Accenture, said, “The world’s leading enterprises are looking to reinvent with tech, data and AI. They see how generative AI is transforming every industry and are eager to deploy applications powered by custom models. Accenture has been working with NVIDIA technology to reinvent enterprise functions and now can help clients quickly create and deploy their own custom Llama models to power transformative AI applications for their own business priorities.”

Jensen Huang, founder and CEO of NVIDIA, said, “The introduction of Meta’s openly available Llama models marks a pivotal moment for enterprise generative AI adoption, and many are seeking expert guidance and resources to create their own custom Llama LLMs. Powered by NVIDIA AI Foundry, Accenture’s AI Refinery will help fuel business growth with end-to-end generative AI services for developing and deploying custom models.”

Accenture is also using this AI Refinery framework to reinvent its enterprise functions, initially with marketing and communications and then extending to other functions. The solution is enabling Accenture to quickly create gen AI applications that are trained for its unique business needs.

Accenture’s new AI Refinery framework has four key elements to help enterprises adapt and customize prebuilt foundation models and deploy them to reflect their unique business needs:

• Domain model customization and training: distill and refine prebuilt foundation models with customers’ own data and unique processes to drive reinvention and value powered by NVIDIA AI Foundry.
• Switchboard platform: allows a user to select a combination of models to address the business context or based on factors, such as cost or accuracy.
• Enterprise cognitive brain: scans and vectorizes all corporate data and knowledge into an enterprise-wide index to empower gen-AI machines.
• Agentic architecture: enables AI systems to act autonomously—to reason, plan and propose tasks that can be executed responsibly with minimal human oversight.

These services will be available to all customers using Llama in Accenture AI Refinery, which is built on the NVIDIA AI Foundry service comprised of foundation models, NVIDIA NeMo and other enterprise software, accelerated computing, expert support and a broad partner ecosystem. Models created with AI Refinery can be deployed across all hyper scaler clouds with a variety of commercial options.

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Thoughtful AI, an AI-powered revenue cycle automation company, today unleashed a flurry of news highlighting its forward momentum, including the launch of the world’s first fully human-capable AI Agents specializing in healthcare revenue cycle management (RCM), exceptional growth metrics, and $20 million in new funding through a Series A round led by Nick Solaro of Drive Capital with participation from TriplePoint Capital.

“This investment will enable us to reinvest in cutting-edge research and development, make breakthrough progress in our flagship RCM AI products, hire top-tier tech talent, and expand our go-to-market strategy and operations,” said Alex Zekoff, Thoughtful AI co-founder and CEO. “Our goal is to continue innovating in the healthcare automation space, making AI integration seamless and impactful for our customers. We are grateful for the support of our investors and excited about the advancements this funding will bring to our platform and services.”

The new AI Agents launched today are among the latest innovations from Thoughtful AI. The first suite of these role-based, fully human-capable AI Agents – aptly named CAM, EVA, and PHIL – perform claims processing, patient eligibility verification, and payment posting.

Traditionally, healthcare providers were forced to do this tedious but critical work using costly, manually-intensive and error-prone processes. That left healthcare providers struggling with claim denials, cost to collect, and days sales outstanding (DSO) issues, as well as mounting costs and complications related to staffing, training, and employee retention. But now AI Agents from Thoughtful AI solve these challenges, enabling healthcare providers to stop hiring for these roles, benefit from significantly higher productivity, and focus more energy and resources on delivering optimal patient outcomes rather than trying to figure out how to get paid.

“Back office staffing and reimbursement are core reasons why the U.S. healthcare system is so expensive and inefficient,” explained Zekoff. “In many industries, collections cost less than a penny on the dollar, but collections can cost 10 times that in healthcare. Imagine a healthcare provider making $100 million a year yet having to spend $10 million to collect that revenue. Those dollars should go to the patient experience, not inefficient collections processes.”

The AI Agents from Thoughtful AI perform comprehensive and extensive workflows, materially reducing the amount of human intervention needed to run healthcare provider RCM departments. In the process they reduce denial rates, allow for unlimited throughput and deliver economic efficiency. That’s a far cry from what first-generation RPA bots can do.

Why? Because Thoughtful AI empowers its AI Agents with multiple technologies – including large language models (LLMs), natural language processing (NLP), optical character recognition (OCR) and robotic process automation (RPA) – to solve problems like a human would. But AI Agents are even better because they are always available, error-free, fast and infinitely scalable.

AI Agents from Thoughtful AI are winning over CFOs at healthcare providers with their:

• Expertise: AI Agents are experts specializing in RCM processes, including (EVA) patient eligibility verification, (CAM) claims processing, and (PHIL) payment posting.
• Efficiency: Trained in the same amount of time it takes to train one employee, AI Agents can do the work of an entire team and integrate seamlessly into existing systems.
• Scalability: Designed to handle large volumes of tasks and data, AI Agents can process hundreds of times more verifications, claims, and payment postings than humans and with perfect precision. And it’s easy to scale them across core back-office processes.
• Proven Reliability: With extensive experience and deployments across many sizable healthcare providers, backed by Thoughtful AI’s powerful monitoring platform, the company’s AI Agents have demonstrated reliability and efficiency, making them excellent counterparts or replacements for human teams. Thoughtful AI provides enterprise-grade uptime and support SLAs, ensuring the continuity that healthcare providers require.
• Continuous Learning and Enhancements: AI Agents can be trained and enhanced continually, ensuring they remain at peak performance and easily adapt to evolving needs. That enables healthcare providers to benefit from enhancements as models continue to improve. Thoughtful AI also continues to invest in AI Agent reporting and analytics, giving providers more visibility into revenue performance than ever before.

CFO Kathrynne Johns explains how her employer, Allegiance Mobile Health, was able to materially reduce its costs and increase its revenue integrity by using AI Agents from Thoughtful AI: “With Thoughtful AI’s support, we doubled the capacity of our claim scrubbing team by an impressive 100%, seamlessly managing thousands of claims daily with minimal human intervention.”

AI Agents are also making Cara Perry, Vice President of RCM at Signature Dental Partners, smile. She reveals: “Thoughtful AI’s creation of a digital employee revolutionized our claim processing, offering 10x efficiency and limitless scalability compared to traditional methods.”

Thoughtful AI healthcare customers see an average of 5-9x ROI, which has led to greater than 300% net revenue retention for that customer cohort. Existing Thoughtful AI customers are quickly expanding their adoption from one to 30 AI Agents. Thoughtful AI Agents have massively expanded the records processed for healthcare providers by more than 2,400%.

“Our AI Agents save healthcare providers millions of dollars through such KPIs as opex improvement, cost to collect, payment efficiency, and DSO reduction,” said Zekoff. “Watching people’s eyes light up during demos, hearing words of praise from customers’ CFOs, C-suite, and boards, and seeing customers get promoted because of our work together is truly exciting.”

Thoughtful AI’s incredible value to customers has fueled its extraordinary momentum. Since it began generating revenue, Thoughtful AI has exceeded 220% year-over-year growth and has already achieved revenue milestones typically associated with Series B companies.

“Thoughtful AI is re-inventing healthcare automation with AI,” said Solaro, partner at Drive Capital, which also led the company’s seed round and February 2022 seed extension. “We are thrilled to support the company’s mission and growth as it dramatically improves its customers’ ability to deliver excellent patient care while growing their practices’ efficiency and profitability. Thoughtful AI is defining the market in this new era of deep technology automation, and I am excited to see the incredibly positive impact that it is having on customers and their patients.”

Seth Feder, founder of OnTarget Advisors and veteran Gartner healthcare analyst, also noted: “Thoughtful AI emerges as a game-changing solution for healthcare providers grappling with the complexity of the U.S. healthcare system and the need to reduce waste in administrative tasks. By deploying AI Agents capable of handling 80-100% of manual tasks across various healthcare admin roles, Thoughtful AI addresses the critical staffing challenges facing the healthcare industry, which are only exacerbated by the 20% rise in labor costs since the COVID-19 pandemic. Further, Thoughtful AI accelerates the revenue cycle by reducing claims processing time from 35 to 25 days and boosts speed of collection by 40% which eases the financial strain felt by most healthcare providers today. When I was a healthcare analyst at Gartner, we nominated ‘cool vendors’ each year who turn disruptive digital technologies into innovative products. Thoughtful AI is a cool vendor in my book because they deliver cutting edge AI technology capable of expanding as a platform to additional roles in the future, all while delivering fast time to value for their customers. Anyone who can do that is very cool.”

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Suppose you’ve been working on landing a high-value B2B client for months, writing a proposal that you believe is tailored to their needs. It explains your solution based on the technological features, comes with compelling references, and responds to their challenges. Yet, when the client responds with a simple “thanks, we’ll be in touch,” you’re left wondering: Was I heard? Was the intended message or the value provided by the product clear?

Here the shortcomings of conventional approaches to Natural Language Processing (NLP) in B2B communication manifest themselves…Despite these strengths, NLP tools are not very effective in understanding the nuances of B2B business and language and are rather limited in understanding the essence and intention behind the text. Common technical words in the document, rhetoric differences, and constant dynamics of the field that specialized terms reflect are beyond the capabilities of traditional NLP tools.

This is where Quantum Natural Language Processing (QNLP) takes the spotlight. It combines quantum mechanics with its ability to process language, making it more refined than previous AI systems. 

It’s like having the ability to comprehend not only the direct meaning of the text but also the tone, the humor references, and the business-related slang. 

QNLP is particularly rich for B2B professionals. This simply means that Through QNLP, companies and businesses can gain a deeper understanding of what the customer needs and what competitors are thinking, which in turn can re-invent the analysis of contracts to create specific marketing strategies.

 1. Demystifying QNLP for B2B professionals

B2B communication is all the more complex. Specificities in the contracts’ text, specific terminals, and constant changes in the industry lexicon represent the primary difficulty for traditional NLP. Many of these tools are based on simple keyword matches and statistical comparisons, which are capable of failing to account for the context and intention behind B2B communication.

This is where the progress made in artificial intelligence can be seen as a ray of hope. Emerging techniques like Quantum Natural Language Processing (QNLP) may bring significant shifts in the analysis of B2B communication. Now let’s get deeper into the features of QNLP and see how it can possibly revolutionize the B2B market.

1.1 Unveiling the Quantum Advantage

QNLP uses quantum concepts, which makes it more enhanced than other traditional means of language processing. Here’s a simplified explanation:

• Superposition: Think of a coin that is being rotated in the air with one side facing up; it has heads and tails at the same time until it falls. In the same way, QNLP can represent a word in different states at once, meaning that it is capable of capturing all the possible meanings of a certain word in a certain context.
  
• Entanglement: Imagine two coins linked in such a way that when one flips heads, the other is guaranteed to be tails. By applying entanglement, QNLP can grasp interactions as well as dependencies between words, taking into account not only isolated terms but also their interconnection and impact on the content of B2B communication.

By applying these nine concepts, QNLP is capable of progressing from keyword-based matching to understanding the B2B language landscape.

1.2 DisCoCat: The Framework for QNLP

The DisCoCat model is a mathematical framework for Distributed Correspondence Categorical Quantum Mechanics (DisCoCat) in language. It effectively enables QNLP to overlay the subtleties of B2B communication—be it contractual wording throughout specification documentation—in a format that is comprehensible and processable for quantum computing systems.

This creates opportunities for various innovative concepts in B2B communication. 

Imagine an AI that is not only capable of reading through the legal jargon of a contract but is also able to differentiate the connections between different clauses. It will also know whether there are gray areas in the document, and understand the overarching goal of the contract. Incorporated under DisCoCat, there is an enormous possibility that QNLP will transform how different businesses communicate, leading to a new paradigm shift of efficiency, accuracy, and understanding within the B2B environment. 

2. Potential Applications of QNLP in B2B

Most of the NLP tools lack the ability to unravel the nuanced flow of B2B communication. QNLP stands out as a revolutionizing tool for B2B professionals, transforming the strategies at their disposal. Let’s explore how QNLP unlocks valuable applications:

2.1 Enhanced Customer Insights: 

QNLP not only sees words but also sentiment, intent, and even purchasing behavior. This enables a B2B firm to know their customers inside and out, enabling them to predict the needs of the buyers and design better strategies for effective customer relations. 

2.2 Advanced Document Processing: 

The strength of QNLP lies in the fact that it can perform the extraction of relevant information with a higher degree of sensitivity due to the application of quantum mechanics. This eliminates manual processing bottlenecks, reduces mistakes, and improves important organizational activities. 

2.3 Personalized B2B Marketing: 

Through QNLP, B2B marketers can create content and campaigns that are tailored to niches and clients. By being able to better understand the customers and the market that the business operates in, QNLP allows companies to deliver messages that are not only relevant but can strike a chord with the audience, paving the way for better lead generation. 

2.4 Improved Chatbot Interactions: 

Chatbots are evolving the way B2B customer interactions occur. However, the usefulness of these tools is limited by their capability to deal with intricate questions. QNLP enhances chatbots to deal with customers’ interactions with more context awareness. Essentially, by analyzing these hard-to-detect prompts underlying the customers’ questions, QNLP-based chatbots are capable of delivering more adequate and beneficial answers that can enhance customer service. 

QNLP is a game-changer for the B2B channel of communication. By obtaining deeper insights into customer data, documents, and interactions, QNLP creates added benefits to B2B businesses in their strategic decision-making and organizational improvements with enhanced performance. 

3. The Road Ahead: QNLP and the Future of B2B Communication

It is worth stating that Quantum Natural Language Processing (QNLP) may exert a transformative influence on B2B communication. QNLP is yet in its infancy, and its capacity to understand the subtleties of complicated B2B jargon does not cease to amuse. Think about early warning systems that are able to log and process not only the quantity of information but also the qualitative psycho-emotional impact and purpose of B2B communication. 

Nonetheless, the use of QNLP to its full potential in a B2B environment depends on a collaborative attitude. It will be the work of quantum computing experts, NLP researchers, and business-to-business industry gurus who will do extensive research and development on this revolutionary technology for its continuous evolution.

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Table of content
Introduction
1. Yellowfin
2. Sisense
3. QlikView
4. Kyligence
5. Tableau
Winding Up

Introduction

In this digital age, data is the new oil, especially with the emergence of augmented analytics as a game-changing tool that has the potential to transform how businesses harness this vast technological resource for strategic advantages. Earlier, the whole data analysis process was tedious and manual, as each project would have taken weeks or months to get executed. At the same time, other teams had to eagerly wait to get the correct information and further make decisions and actions that would benefit the business’s future. 

Therefore, to pace up the business process, the data science team required a better solution to make faster decisions with deeper insights. That’s where an organization needs to depend on tools such as augmented analytics. Augmented analytics combines artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to enhance the data analytics processes, making them more accessible, faster, and less prone to human error. Furthermore, augmented analytics automates data preparation, insight generation, and visualization, enabling users to gain valuable insights from data without extensive technical expertise. 

In today’s exclusive AITech Park article, we will take a quick look at the top five augmented analytics tools that data scientist teams can depend on to democratize advanced-level analytics with augmented data ingestion, data preparation, analytics content, and DSML model development. 

1. Yellowfin

Yellowfin specializes in dashboards and data visualization that have inbuilt ML algorithms that provide automated answers in the form of an easy guide for all the best practices in visualizations and narratives. It has a broad spectrum of data sources, including cloud and on-premises databases such as spreadsheets, which enables easy data integration for analysis. The platform comes pre-built with a variety of dashboards for data scientists that can embed interactive content into third-party platforms, such as a web page or company website, allowing users of all expertise levels to streamline their business processes and report creation and sharing. However, when compared to other augmented analytics tools, Yellowfin had issues updating the data in their dashboard on every single update, which poses a challenge for SMEs and SMBs while managing costs and eventually impacts overall business performance. 

2. Sisense

Sisense is one of the most user-friendly augmented analytics tools available for businesses that are dealing with complex data in any size or format. The software allows data scientists to integrate data and discover insights through a single interface without scripting or coding, allowing them to prepare and model data. Eventually allows chief data officers (CDOs) to make an AI-driven analytics decision-making process. However, the software is extremely difficult to use, with complicated data models and an average support response time. In terms of pricing, Sisense functions on a subscription pricing model and offers a one-month trial period for interested buyers; however, the exact pricing details are not disclosed. 

3. QlikView

QlikView is well-known for its data visualization, analytics, and BI solution that aids IT organizations in making data-based strategic decisions with the help of sophisticated analytics and insights drawn from multiple data sources. The platform allows data scientists to develop, extend, and embed visual analytics in existing applications and portals while adhering to governance and security frameworks. However, some users have reported that the software may slow down when assembling large datasets. Additionally, the software sometimes lacks the desired feature and depends mostly on plugins from the older QlikView, which lacks compatibility with the updated Qlik Sense. The QlikView comes in three pricing plans: Standard Plan: $20/mo for 10 full users only, with up to 50GB/year data for analysis, Premium Plan: starts at $2,700/mo and 50GB/yr data for analysis and more advanced features and  Enterprise Plan: Custom pricing, starting at 500GB/yr data for analysis.

4. Kyligence

The fourth augmented analytics tool that data scientist teams use is Kyligence, as it stands out for its automated insights and NLP technology for businesses to generate deep insights within seconds. The technology also promises a centralized, low-code platform that emphasizes a metrics-driven approach to business decision-making and further identifies the ups and downs of the given metrics, along with discovering root causes and generating reports within seconds. However, the tools are considered to be quite complex and expensive when compared to other augmented analytics tools on the market. Kyligence comes in three pricing plans. Standard plan: $59/user/month, Premium plan: $49/user/month (minimum 5 users), and Enterprise+ plan: Flexible pricing and deployment options.

5. Tableau

Last but not least, we have the famous Tableau, an integrated BI and analytics solution that will help in acquiring, producing, and analyzing the company’s data and provide insightful information. Data scientists can use Tableau to collect information from a variety of sources, such as spreadsheets, SQL databases, Salesforce, and cloud applications. Talking about the interface, it is quite easy regardless of your technical skills, allowing you to explore and visualize data effortlessly, but professionals at an executive level might have issues adapting to this technology. However, the most concerning part of this software is its high pricing and lack of customization in terms of visualization options. In terms of pricing, Tableau comes with two exclusive plans: for an individual user, it is $75/month, and for two users, it is $150/month.

Winding Up

With the importance of data, data analytics, and augmented analytics tools, data scientists are paving the way for effortless and informed decision-making. The five tools listed above are designed to automate the complex data analysis process.

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