Kognitos is attending ITC Vegas, October 14-16 in Las Vegas. See us at booth #454 or join us for dinner at Momofuku.
Kognitos is attending ITC Vegas, October 14-16 in Las Vegas. See us at booth #454 or join us for dinner at Momofuku.
APIs are essentially the set of protocols, routines, and tools used to build software applications. They help connect different forms of software and enable automation across applications.It is tempting to give ChatGPT and other LLMs direct access to these APIs. Doing so would be a security disaster waiting to happen. This is because LLMs can be easily tricked by an attacker to follow their instructions instead of the user’s. Attackers can use this to steal private information, takeover systems, or infect other automated LLMs.
They can place hidden poisoned prompts on public webpages, emails, or in any data that the LLM accesses. If the LLM looks at the poisoned data at all, that is often sufficient for the attacker to gain complete control of the LLM’s actions for that session. Within an enterprise this could wreak havoc, especially for enterprises who contain Personal Identifiable Information (PII) or Protected Health Information (PHI). But there is a better way that enterprises can use the power of Generative AI and LLMs to automate business processes and other activities without incurring major security risks.
Instead of giving ChatGPT and LLMs direct access to APIs, any time an LLM wants to call out to another system, its plan must be reviewed by a human first. The best way to do this would be to present the plan as detailed English steps, and then use a non-AI system to run the approved plan. This interpreter ensures that people remain in control, and can make certain that actions taken by AI are both precise, correct and safe for their business. This is what our customers at Kognitos use today to automate business processes using both LLMs and APIs in a safe, scalable way that empowers the business user.
In conclusion, while Language Models like ChatGPT have made significant strides in the field of natural language processing, we must not overlook the security risks associated with their access to APIs. It is imperative that we take necessary precautions and implement strict security measures to ensure that LLMs are not exploited by attackers. We must keep a watchful eye on this field and ensure that we prioritize security while advancing these technologies. Instead of giving direct access to APIs, platforms keeping people in the driver seat to approve the actions of LLMs is the best path forward for enterprises.
Many businesses leverage OCR + RPA to save time and reduce errors associated with manual data entry. For example, a manufacturing company that processes thousands of invoices per month that are highly standardized and do not vary often, can use OCR technology to extract invoice data automatically. Today some forms of OCR are becoming available to business users through no-code templates and pre-trained models, but most require some developer expertise. These technologies are a good first step, but unfortunately only cover a small portion of the total documents used by businesses.
Despite its strengths, OCR and RPA both have limitations that restrict the ability to process complex documents. OCR technology has limited ability to recognize unstructured text like handwriting, non-standard fonts, and poor image quality. Furthermore, OCR technology has limited accuracy rates in understanding the context and extracting information from the wider business process. It is particularly challenging to extract information from complex documents that include tables, graphs, and other visual elements. OCR technology also struggles to process documents with incomplete information, and it is difficult to catch and handle errors using OCR technology alone.
RPA functions much in a “Bad Data In, Bad Data Out” style. When fed bad or incomplete data by OCR, RPA bots frequently break and are described as “Brittle”. Additionally, RPA is best when used for processes that do not have lots of exceptions. Complexity or variations in documents can create exceptions that cause RPA bots to break.
OCR + RPA is a great tool for standardized processes, but struggle with different document types. These limitations of OCR technology and other traditional approaches to document processing are holding back businesses in today’s fast-paced market. Businesses need accurate and efficient document processing to make informed decisions, streamline workflows, and maintain a competitive edge. Incomplete data, inconsistent data, and errors in document processing can cause businesses to lose money and damage their reputation.
Generative AI like ChatGPT, or GPT4 is a game-changer for document processing. It uses advanced deep learning algorithms to analyze large volumes of data and identify patterns, making it highly accurate and efficient in document processing. Generative AI can learn from diverse examples and adapt to new data inputs over time, making it highly effective in processing complex documents with tables, graphs, and other visual elements. It can also recognize and understand the context of the wider business process, making it highly effective in handling incomplete or inconsistent data.
For example, a hospital can use Generative AI to process medical charts, which contain complex data structures such as tables, diagrams, and graphs. By using Generative AI, the hospital can extract critical information from the medical charts, such as patient diagnoses, medications, and treatments, with a high degree of accuracy and speed.
In another example, Kognitos worked with an international conglomerate that needed to match payments with invoices from different subsidiaries across the globe. The complexity of the process and the variety of documents and languages necessitated a tool that could understand context, and financial professionals in a shared services center to directly control the automation, not developers. By combining Generative AI with OCR in a Generative AI Automation platform, the conglomerate could greatly reduce the number of people manually processing data with high degrees of accuracy.
Generative AI can also help businesses handle cases when documents have incomplete data or when OCR has extracted incorrect information. It can recognize the context of the wider business process, understand the relationships between different pieces of information, and use this information to extract the correct data. For example, an insurance company can use it to process claims that contain incomplete or inconsistent data. The claims processor rather than a developer, is in control, and can teach Generative AI Automation how to handle situations with incomplete data, and how to find that data in future documents. An example of how a business user can teach Generative AI automation how to handle a document with a simple command can be found here: Generative AI + OCR
In conclusion, traditional approaches to document processing such as manual data entry and OCR technology helped take the first step, but have significant limitations that can hold back businesses. Generative AI is a game-changer for document processing, providing accurate and efficient processing of complex documents. By implementing Generative AI Automation for their document processing needs, businesses can streamline their workflows, decrease errors, and reduce the need for developers, greatly lowering the cost of automation and the ROI of projects. This gives companies a competitive edge in today’s fast-paced market. The time to adopt Generative AI for document processing is now.
Automation services offer a lot more than merely helping you accomplish tasks faster in the retail space. Implementing automation benefits nearly every aspect of your retail business. Utilizing automation in retail will inevitably equip you with tools to make your brand more relevant to consumer needs, help manage expenses, resources, and provide matchless and incredible customer experiences. Automation is transforming the retail industry in many ways and has the potential to completely change the way businesses operate, grow in efficiency, and increase revenue.
Some of the biggest challenges in the retail industry include:
There is a psychological discomfort linked to self-denial. The natural human instinct is to seize the reward at hand. This tendency is evidently shown in our consuming habits. Instant gratification is a quick way to win the satisfaction of your customer, but it becomes extremely challenging for the supply chain side of the business. The goal line continually shifts as we find new technological advancements to deliver near-instant results. Automation becomes key to satisfy your customers’ demand by simplifying and streamlining processes that directly impact the way in which the customer interacts with your brand.
Using Kognitos Generative AI Automation, retailers have the ability to collect and analyze customer data. This gives the business user the tools they need to create a quick and personalized experience that caters to customers desiring their needs to be met in a quick fashion– with a simple command. Kognitos can improve your customers’ overall experience through increased data processing and personalized marketing campaigns; this helps retailers produce quick and personalized customer support, resulting in improved customer satisfaction and loyalty.
Automation provides four main values:
Artificial intelligence can be a highly effective tool for retailers to provide the best possible customer experience. Potential improvements range from reducing shopping time with automated checkouts to having more personalized discounts to offering round-the-clock customer service with the use of chatbots. Retail automation software helps by significantly reducing the sales cycle duration and improving the salesperson’s productivity. In the context of retailers, optimists claim that generative AI will aid the creative process of artists and designers, as existing tasks will be augmented by generative AI systems, speeding up the ideation and, essentially, the creation phase. GPT-3 can be implemented to help businesses accomplish tedious, repetitive tasks. For instance, developers can build a tool that generates various layouts for the design required in different situations.
Kognitos is Generative AI for automation and can similarly help retailers as Kognitos AI solutions have the ability to automate repetitive tasks and streamline operations, giving retailers more time to focus their efforts on strategic initiatives. With our conversational exception handling, correcting errors has never been faster and easier. For example, Kognitos is able to help retailers automate all documentation from purchase orders to inventory management. Business users within a retail supply chain or finance department simply need to type what they wish to have automated in Kognitos’s Koncierge. Koncierge brings the power of GPT3 and ChatGPT into the enterprise. It takes your wish, creates a plan of action and then runs the English as automation as seen here.
Demographics are quickly changing and consumers increasingly want personalization. Executives and market data agree that being ahead of the curve and responding promptly to changing customer needs is a real challenge. Brands everywhere are encountering this in one way or another. Retail chains now use AI to personalize a customer’s experience, and target that customer more closely. But to do so successfully, stores need easy access and management of data to feed models. Generative AI Automation helps in the collection and cultivation of such data, and enables a lower CAC, and higher LTV of a customer base.
Generative AI is a tool that retailers can utilize to drastically change the way they approach content creation– visually or audibly. With the implementation of Kognitos in the retail realm, it is easier than ever to be equipped and prepared to cater to the ever-changing demands as well as positioning yourself for success in the future. With Kognitos, business users can teach automation products how to pull valuable information about demographics to help forecast future trends with a simple statement like, “get customer feedback.” With Kognitos, business users merely have to say in English what they want to have happen and then there you have it!
In this day and age, process automation is a must-have for any business looking to stay ahead of the curve. By leveraging technology, organizations can quickly reduce errors, save money and time on mundane tasks – freeing up their valuable human resources in the meantime. Moreover, advancements in tech have made it far more accessible so that even smaller companies not only have access but also get to benefit from automated processes ranging from basic data entry all the way to complex multi-system workflows! With automated processes, organizations can build an edge on the competition by staying ahead in rapidly changing markets and delivering impeccable customer service.
Businesses are increasingly relying on Robotic Process Automation (RPA) to take care of routine, everyday tasks – allowing human employees to focus their efforts elsewhere. While RPA is largely productive in these endeavors, it may sometimes stumble upon anomalies that fall outside its programming parameters; a “human intervention” scenario known as an exception. When this happens, the whole operation can be put at risk: delays mount up and efficiency decreases while costs skyrocket.
There are two major types of exceptions in process automation.
Business Exceptions happen when a bot is unable to process further due to programmed instructions. For example, a bot could only be programmed to process expense invoices upto $1000. In such a case any expense above $1000 would cause the bot to throw a “business exception alert” to a supervising human.
Application Exceptions happen when a bot encounters a technical issue like a server crash, network malfunction or a good old software bug. In such cases the usual strategy adopted by RPA bots is to just retry the process.
To minimize the impact of exceptions, organizations need to have robust exception handling strategies in place. This may involve training RPA bots to handle common exceptions using artificial intelligence (AI) or machine learning algorithms, or incorporating human oversight into the process to ensure exceptions are resolved quickly and efficiently. Additionally, organizations should continuously monitor and fine-tune their RPA bots to ensure they are functioning optimally and handling exceptions effectively.
But implementing robust exception handling strategies is very expensive and time consuming. Handling business exceptions not only requires business users to plan as many exceptional scenarios as possible, but it also involves them explaining such situations to bot developers who have limited business domain knowledge. Application exceptions might not always be resolved by retries, and such a scenario involves intervention of the IT team to analyze and fix the issues, which can be very time consuming. Such time overheads can add up over time, leading to significant losses in productivity and profitability.
Kognitos offers a unique and innovative solution for process automation – a botless system that uses an LLM-based interpreter to understand and execute processes written in plain English. This system acts as an IT layer for businesses, allowing them to simply explain their processes in English, eliminating the need for complex IT configurations and freeing up valuable resources. For example, in the case of invoice processing, Kognitos would communicate any exceptions directly to the business user in plain English and allow for resolution with a simple prompt. This drastically reduces the time and cost associated with planning and handling exceptions with traditional RPA solutions, as well as making it possible to handle exceptions that may have been impossible to plan for. The result is a more streamlined, efficient, and cost-effective process automation solution for businesses of all sizes.
Automation in the CPG industry is not new, but it is evolving rapidly. Performing tasks accurately and at scale, both on the production floor and in the office, not only delivers a competitive edge to the company as a whole, but brings peace of mind to the employees and managers involved in these processes.
On the other hand, there is a steep learning curve to adopting any kind of new technology (think back to when you first implemented your ERP). The time lost in learning, mistakes made in the process, and sheer frustration is all said to be an “investment” towards a future with streamlined processes…
Until the next new software, of course.
Traditional automation in CPG (RPA) fails to deliver the desired ROI for two main reasons:
And coordinating between IT and business units is often challenging.
Multi-step procedures such as vendor onboarding and inventory management still remain stubbornly manual due to the high volume of exceptions which break traditional RPA Automation.
In all fairness, CPG leaders have pushed the hard for operational excellence during and after the pandemic, an unprecedented event which shook global supply chains. As the global economy opens again, CPG leaders face a new challenge: Battling inflationary pricing across transportation, raw materials, and labor.
Market leader, P&G, was able to grow gross profits 9.7% since 2020 by prioritizing the single most important asset in the company: Time.
The average IT professional is reported to spend 4.5 hours a week searching for documents according to an IDC white paper published in 2012.
That’s over half a workday lost every week of highly skilled and expensive labor to the company. Our technology has advanced to the point where it is capable of saving this lost time, but not all business users can utilize these capabilities without having coding knowledge.
We believe technology should empower the user, not the other way around. Today, less than 1% of the world knows how to code. So, if we can’t speak the language of computers, why don’t we make computers speak our language?
This is the fundamental idea behind Generative AI Automation.
Imagine billions of business users creating and managing their own automations using simple english. The time and cost saved from automating manual tasks such as processing documents, updating CRM, vendor onboarding, and claims management (among others) can be significant when compounded over time. Additionally, Generative AI automations empower business users by allowing them to spend their time being strategic and, of course, happier without tedious and manual work.
The competitive advantage from automation for the CPG industry goes beyond the obvious time and cost benefits, it allows them to spend more time better understanding and serving their customers.
Built In– 5 AI Trends to Watch in 2023
“According to a recent report published by consulting giant McKinsey & Company, which surveyed some 1,492 participants globally across a range of industries, business adoption of AI has more than doubled over the last five years. Areas like computer vision, natural language generation and robotic process automation were particularly popular.” Built In
The digital revolution has taken an exciting new turn with the rise of artificial intelligence technology, allowing businesses to automate processes faster than ever before. This democratization grants organizations unprecedented access to innovative tools that can streamline operations and simplify daily tasks with the human in control. Prepare for a whole new world of automation!
Process automation is gaining momentum in the world of AI, with increased focus on explainability and transparency. By leveraging these cutting-edge technologies to automate tasks that were traditionally labor intensive, businesses can maximize productivity while minimizing risks associated with manual errors.
Developing AI solutions to automate business processes is becoming more and more affordable and efficient.
Explainable AI
With traditional AI systems, humans can find it tough to comprehend the motivations behind decision-making and predictions. This lack of transparency has a cascading effect on business operations as trust in automated processes becomes uncertain. To ensure effective decisions are made with confidence, understanding how these systems reach conclusions is critical for success.
1. In the age of AI-driven automation, a firm’s Accounting Department must grapple with the new challenge of understanding machine decisions. Traditionally this was easy to do when relying on human approvers – one only needed to look back at why something had been approved and modify processes accordingly – but artificial intelligence presents a different set of complexities that require extra insight into how it works in order for adjustments to be made and mistakes effectively prevented from happening again. Organizations are striving to gain greater trustworthiness in the automated decision-making of AI systems. To do this, they’ve turned to Explainable Artificial Intelligence (XAI) solutions which can offer a peek inside an AI’s thinking process and ensure accuracy with clear explanations for each conclusion made.
2. Explainable AI (XAI) refers to Artificial Intelligence (AI) systems that can provide human-understandable explanations for their decisions and predictions. The goal of XAI is to build AI systems that are transparent, trustworthy, and accountable. XAI provides clear and understandable explanations for the AI’s decisions and predictions, making it possible for humans to understand and verify the reasoning behind the automated process. This helps to build trust in the system and ensures that automated processes are aligned with organizational goals and values. Additionally, XAI can help to identify and address any biases or errors in the automated process, leading to more accurate and reliable outcomes. Furthermore, XAI can improve decision-making by providing human-understandable explanations for the AI’s outputs. This can help organizations to identify areas for improvement and optimize the performance of their automated processes.
Kognitos is a cutting-edge Explainable AI (XAI) solution that offers unparalleled transparency and accountability. It allows users to execute simple English sentences in a deterministic manner and provides a detailed explanation of each action performed in plain English. This includes explanations for any actions that were unable to be executed, and the ability to handle such scenarios through a conversational English interface. This empowers businesses to easily audit all actions performed by the system and make strategic adjustments without the need for extensive technical involvement from researchers or programmers. With Kognitos, organizations can ensure that their AI-powered processes are fully transparent and accountable, leading to improved decision-making and better outcomes.
Check out Koncierge for free today! A Generative AI platform designed to automate business processes. Describe what you want to automate and Koncierge will present a plan of action in plain natural language.
While the potential of ChatGPT and other NLP models to automate certain tasks that involve processing and generating human language is exciting, it is important to understand the limitations of this technology. One limitation is that ChatGPT and other NLP models are not adept at mathematical or logical reasoning. Additionally, these models can sometimes generate responses that are inappropriate or offensive, particularly if they are trained on a dataset that includes such language. Furthermore, NLP models like ChatGPT are not able to fully replace human workers, as they do not possess the ability to think and reason in the same way that humans do. This is because it is trained to generate human-like text based on a given prompt or conversation without a deep understanding of what is right and what is wrong. Tasks that involve critical thinking, such as math or business processes, are also hard for LLMs to do because they require precision and repeatability which isn’t a strong suite of LLMs.
Even if Generative AI is able to overcome the above mentioned issues, one major problem that remains with ML driven automation systems is the problem of “opaqueness”. The ML systems would just execute actions based on some logic deeply embedded in one of the model parameters, but we would never know the “why” part for any action. For example, an ML automation system could erroneously send out wrong invoices to your customers, and you would be left wondering exactly what caused it to do so. This means that resolving any bug or issue in the automation would be a nightmare for the IT team. Not just that, but business process automations are inherently logical and procedural. Using Generative AI (like ChatGPT) in this use case would just introduce non-determinism in such tasks that could cause unintended problems.
We need an AI system that is able to execute actions in a deterministic and auditable manner. Traditional programming languages already do this. But less than 1% of the human population knows how to even read code. Hence, there is a need for computers to natively understand statements in native language and know how to execute them (in the same way they know how to execute programming languages like python or java). However, this approach presents several challenges.
A language like English is very contextual. The same word could mean two very different things based upon the context it is spoken in. For example “Capital” can refer to financial assets or the city where a company is headquartered. Programming languages cannot handle such context based actions, and would require separate algorithms for each case.
Native languages are also very ambiguous. These languages were developed in a way that resolved such ambiguities via conversations. For example, if you say “we should call that employee”, if your listener has some doubt about which employee you are talking about, she would just ask you to clarify. Now programming languages are not built to be run in a conversational way. They just run a specific action, and any ambiguity that arises in the course of that would cause an exception.
One other difference between native and programming languages is the difference in their grammar rules. People do not think in terms of “functions” or “classes”. They think in terms of “actions”, “concepts” and “knowledge”. This is because programming languages are inherently mathematical, whereas native languages developed long before humans even had rudimentary knowledge of basic math.
One solution to this problem is Kognitos, which directly understands native language and is able to process it. Kognitos is able to overcome the challenges associated with native language by understanding the context, resolving ambiguity, and understanding the rules of grammar in a way that is similar to how humans understand them. Additionally, it is able to understand and process domain-specific language, making it more effective at automating tasks that involve human language. And, just like programming languages, it also provides a detailed auditable view into its runs, which the business users can use to gain insights into why an action happened or what might have gone wrong in case of an exceptional situation. Kognitos hence provides a way for businesses to reliably automate their tasks while leveraging the power of the latest LLM technologies.
In conclusion, while ChatGPT and other NLP models have the potential to revolutionize the way we interact with machines, it is important to understand their limitations. By enabling humans to directly communicate with computers in their native language and using technologies like Kognitos, we can overcome these limitations and make businesses more productive. As this technology continues to evolve, we can expect to see more advancements in the automation of tasks that involve human language.
Now, we are in the initial phases of the AI revolution. Machines are becoming more powerful intellectually than humans. And just like one horsepower became a hundred, and a hundred became a million in the industrial revolution, the AI revolution is poised to follow the same path, albeit this time with explosive speed. How do we envision leveraging machines that can think faster and better than us? The main question is: Who will be at the steering wheel?
I have an optimistic view of the future. While there is no dearth of doomsday scenarios or dystopian predictions of what AI will bring unto humanity, I believe humans will always remain in control of the world around us. The control will stem from our fundamental distrust of machines that are intelligent – like self driving cars.
But how about the explosive popularity of generative AI? ChatGPT, DallE.2, Stable Diffusion and MidJourney are creating art with superhuman speed and creativity. How did we solve for trust? These platforms provide examples and let the human review, choose and tweak what they want. No matter how powerful the machine is, as long as we get to review and decide what to use, we are in control. That review step is the new steering wheel of the AI revolution.
Today we are merely scratching the surface of the power of generative AI. So far it is writing words and drawing pictures. Some have started making music and videos. These are the creative arts which are imminently reviewable by any human because the result of the generative AI is meant for human consumption. Now, what about everything we built in the industrial revolution? All the diligent machines that drive our GDP? Can generative AI drive those machines and automate the world around us? The answer is yes, BUT. Who is at the steering wheel?
As the level of intelligence of a system increases, the gap between what is said and what is done increases as well. Hence with other humans, we’ve known to Trust but verify. We don’t take the same stance with a tractor or a mule, but we might for a chimpanzee or another human and definitely for AI systems going forward.
Our world runs on machines which are today controlled by humans. These machines are a lot more powerful than humans, but they are not intelligent and thus we trust them. Now, how do we leverage Generative AI for automation in a trusted way but use them to drive these industrial machines?
Here is an analogy: I go to my doctor who is at least a 100 times smarter than I am when it comes to medicine. She takes a brief look at me, performs a few tests and generates a diagnosis. Next, she presents me with a plan of action (in conversational English) in a way that I (with no medical training) can easily understand and trust. Note, she doesn’t jab me with an injection or cut me open to fix me. I get to verify the plan and determine if it is acceptable based on my own priorities, values and beliefs. I then take the plan to the pharmacist, nurse or specialist. They are there to execute the plan. Yes, there might be some tweaks to the plan, but overall, the plan is what I agreed to. In the whole process, I feel I am in control. That review of the plan is the intellectual steering wheel.
Generative AI is crossing over into controlling machines. These industrial machines only understand APIs and computer languages which 99.5% of humanity cannot review. We need to place all humans in the reviewers seat. We need a platform that can take a prescription from Generative AI, have the human review it in a language natural to us, and then execute the agreed upon plan with the diligence of my trusted pharmacist.
Kognitos built that platform that brings the power of Generative AI to all businesses allowing the business user to be at the steering wheel. Unlike traditional automation where any review or management of exceptions to the process requires knowledge of APIs, coding tools and IT jargon, Kognitos navigates the entire automation lifecycle in English, empowering and building trust with the business user. With this a billion business users are empowered to automate business logic intuitively using conversations.
Today businesses need to rapidly innovate while following complex business rules and processes. Kognitos provides a first of a kind platform where both the rigor and precision of business logic and creativity of generative AI can be harnessed in a trusted and scalable manner. While Kognitos Koncierge brings the innovative power of public data sources and large language models to a business, Kognitos Brain discovers and learns a business’s private apis, data and processes. This allows businesses to leverage Generative AI, in English, to accelerate innovation with unprecedented explainability, auditability, scalability and speed.
The professors at IIT focused a lot on the fundamental aspects of computer science. These building blocks make it easier for me to reason why a system is failing or an application behaves the way it does. It helps with architecture, scale, and all sorts of problems. It gave me the tools to approach any problem.
I did my college internship at Nutanix, and then joined the platform team. Nutanix was a great starting point for me and helped me get my legs underneath me. I learned how coding in companies is very different from coding on your own or in college. It taught me how to write code properly and how to manage a project. It taught me what being an Enterprise engineer is all about. But after time I found that my learning had slowed and I wanted to be at a smaller company where I could move faster.
There’s a trade-off isn’t there? You want to build things quickly and experiment to produce innovative products, but at the same time need to build things that will scale and deliver consistent experience. How do you balance this?
Yes there is always a tradeoff and my experience at AI Dash gave me insight into the other side of the coin. I joined as the 24th employee. The culture at AI Dash was very different. They had a “Get Sh*t Done” culture. This works for a while but then can put the platform under a lot of stress over time. Relative to Nutanix I found this culture the complete opposite. So there is a tradeoff between speed and quality at times. Finding top engineers/talent and being a part of a team that helps improve each other is the only way to move quickly while still building something properly.
Everyone knew Binny as a superstar at Nutanix even though I didn’t know him personally. We are also both alumni of IIT Kanpur. One day I saw he posted a video of processing documents completely in English and I quickly realized the potential for LLMs so I cold emailed him and the rest is history!
Wow! This is interesting. As you know we are hiring for a lot of roles right now, what advice would you give to engineers interested in these roles?
First you have to establish a connection. Binny and I went to the same college and had the same company background. Next you have to establish experience, and clearly say why your experience helps. Lastly, you must connect with the vision and why you can help with that vision. It has to be short, crisp and clear.
You’ve been at Kognitos for a year now. What about the team or culture that you really like and what are the skill sets needed to be a good fit?
I wanted to join a team with more experienced engineers from whom I could learn. People here know how to build products that scale into multi-Billion dollar companies. Kognitos is the type of environment and where everyone challenges each other and themselves so we all improve. We set extremely high standards for our code. It has to be A grade. We have a very robust test framework and so your code has to live up to the high bar.
Only ambitious people can thrive here. We have conflict to make us all better. You have to give more than 100% each and every day and that might take a toll on some people, but that is the intensity we want to set and if you enjoy building software you will love it. You can be a part of something that will truly impact people at scale. I wanted to build something where even my parents will be impacted in how they use computers and Kognitos is that type of product.
The possibility of LLMs, Generative AI and Kognitos is massive. We are democratizing things to where even people who have very little experience with computers can interact with computers directly using plain language. Today it is just in plain English but in the future in other languages, Tamil, Hindi, Spanish etc. I’m helping build something that will be a part of an extraordinary revolution in computer science, and it’s exciting to know that I can leave behind my name as an integral part of that legacy!.
In my experience, people don’t really know what’s going on in their business. They often have a very incomplete picture, especially if there is variation in a process.. With Kognitos we can fill in that gap, provide that information and help companies capture not only how their business operates today, but give their employees the power to automate away parts of this work that are manual. Businesses also need the capability to audit LLM outputs and know why something went wrong (and things usually will go wrong in any enterprise system). Our architecture and design make this possible through Conversational Exception handling and the ability to audit in English. This is the right approach for the enterprise.
As businesses continue to enter the digital age, Gartner’s recent blog posts have indicated an impending shift from Enterprise Resource Planning (ERP) Platforms to Enterprise Run-Time Platforms as a necessary piece of agility and scalability. This major change will require companies looking towards ERP strategies they can tailor towards faster & more adaptive models that keep up with their ever changing needs – such as Digital Core/Lean Core/Agile Core options. It is clear this new era in business operations has just gotten underway!
The era of big, expensive on-premise ERP systems has been rapidly replaced by cloud and SaaS solutions. These applications offer businesses access to new types of enterprise software with faster implementation times at minimal cost due to subscription models. In the HCM space, this shift is almost complete with cloud being an industry norm for many companies such as Salesforce and Workday who pioneered the technology; even legacy vendors like Oracle and SAP have adopted a “cloud first” development strategy in order to stay competitive within their field. This architectural shift makes it easier to open their core systems via APIs. AI can then be able to interact with these systems in a much more natural, reliable and robust way.
ERP software has historically had a steep learning curve. Not only that, but the maintenance cost of this software has been a pain for almost all businesses relying on them. This means that new age businesses are increasingly looking to move away from the traditional offerings and adopt a leaner and more agile solution. This is where Generative AI comes in. With its ability to understand natural language based business domains, and its ability to natively talk to machines, it can help bridge the gap between domain experts and software. Businesses would only need to train their employees in how the business works – not how software works.
Thus by utilizing ERP systems and Generative AI automation, businesses can achieve efficiencies that lead to increased profitability. By leveraging the power of technology in this way, companies create an optimized workflow for data management with greater accuracy and scalability – ultimately enabling them to make more informed decisions backed by reliable intelligence! This combination is enabling organizations to become more agile and responsive, allowing them to rapidly adapt as necessary in this ever-changing business climate.
Business users now have access to smarter, more user-friendly enterprise software through the integration of generative AI tools. No longer do they need to spend hours learning a new application – instead, domain knowledge and AI will collaborate in order for businesses to get the most out of their ERP solutions. This opens up an exciting opportunity where Enterprise Generative AI enablers & ERP vendors can join forces and provide cutting edge products that promote success within enterprises all over the world!
At Kognitos, we are already walking the walk for Norco – one of our esteemed clients. We believe in showing results not just talking about them; so let’s explore how this vision could become a reality with concrete examples.
Gartner Article: https://blogs.gartner.com/tonnie-van-der-horst/erp-is-dead-part-1/
