Polyglots and polymaths have been around since time immemorial - humans with an extraordinary ability to communicate fluently in multiple languages and drawn upon wide knowledge and learning. However, there have always been limitations to their capabilities simply because of the boundaries of human ability to learn and capacity to retain. Agentic AI, however, unlocks agents with an indefatigable hunger and ability to learn new languages, tools and applications.

Thus, this new age of AI brings with it the concept of an omniglot agent. Omni- means "all" and -glot refers to languages, tools, and applications. An "omniglot" is an entity who speaks all languages, possesses multiple skills, and has deep knowledge of various domains, along with the ability to utilize many tools.

At Ema, we are building an Agentic AI that embodies the essence of an omniglot, seamlessly navigating the complexities of enterprise data, applications and complex workflows while facilitating effective communication across diverse systems and stakeholders. The ability to work across systems and tasks make an omniglot agent an invaluable asset to any organization. We refer to our Omniglot agent as a AI Employee, representing its multifaceted capabilities.

An omniglot agent would have the ability to process data from all types of data sources - databases, spreadsheets, or cloud services. The agent can analyze data and identify intricate patterns, providing valuable insights that help organizations make informed decisions. Ema's data and knowledge agents are able to connect with all data sources including enterprise data and web data. We continually expand our range of compatible data sources to enhance Ema's capabilities.

Ability to take actions across all enterprise applications

An omniglot agent can take actions across all applications, regardless of whether they have APIs (Application Programming Interfaces) or not. This flexibility allows it to integrate seamlessly into existing workflows, ensuring that it can work with the tools and systems that businesses already use. Some of these enterprise workflows can be quite complex. Ema's action agents are able to connect with all types of applications and workflow systems. We continually expand our range of supported applications to enhance Ema's capabilities.

An omniglot can speak all languages, whether they are human languages or computer programming languages. This means that it can understand and communicate effectively with people from different functions within an enterprise as well as interact with various coding languages used in software development. Ema's agents fluently process and generate both natural languages and computer code.

Omniglots operate effectively in multiple business verticals, including sales, support, marketing, employee experience, human resources, legal, pharmacy, insurance and financial services. Their adaptability allows them to cater to the specific needs of different sectors, providing a versatile solution for various business challenges. Ema can operate across all the verticals and also adapt to multiple roles within a given vertical.

Omniglot agents can operate in all environments, including SaaS, or in the customer's cloud. This architectural flexibility allows them to adapt seamlessly to various operational contexts, ensuring that they can deliver their capabilities effectively, regardless of the infrastructure in place.

What empowers Ema’s Agentic AI to be an Omniglot?

At Ema, we innovate on our Agentic AI platform to deliver an omniglot AI agent that allows enterprises to streamline their workflows. Our proprietary technology allows us to infuse Ema with omniglot-like capabilities. Primarily, Ema’s technical systems operate at two levels:

 Ema’s Generative Workflow Engine™ is like the prefrontal cortex of Ema. It leverages a small transformer model to translate conversations into workflow graphs. It accepts high-level, natural language goals from users and formulates granular plans orchestrating a mesh of 

 to act seamlessly in tandem to optimally achieve those goals. We refer to the team of AI agents working together for a specific goal as an Ema AI Employee.

Once the plan is set into motion, Ema’s AI agent library springs into motion. Agents use their repository of knowledge, skills and actions to achieve their respective objectives. Here, our proprietary technology provides almost omniglot-like capabilities to our agents:

 Agents are able to build knowledge graphs by synthesizing information from diverse data sources. Through combining data from various domains and formats, agents build a comprehensive knowledge base that informs their decision-making and responses. This rich understanding of enterprise data enables them to provide accurate insights and recommendations tailored to specific business needs.

, our proprietary 2T+ parameter mixture of experts model. By intelligently combining insights from 100+ language models, our agents can reason and generate human-like responses, making their interactions more natural and effective as well as minimizing hallucinations. This empowers Ema’s agents to tackle complex queries and provide highly accurate responses at the lowest possible cost, enhancing their overall performance and effectiveness.

 Our agents seamlessly integrate with various software systems and are adept in tool use. These agents are empowered to call APIs, initiate RPA workflows, automate tasks, and facilitate communication between different applications.

In summary, Ema is powered by a mesh of AI agents that are orchestrated through our proprietary Generative Workflow Engine™ operating on its strategy to achieve a high-level goal. Each of the AI agents are supercharged with an ability to combine diverse data sources into a knowledge graph, leverage the power of the 100+ LLMs (through our EmaFusion™ model) in formulating their response, and communicate with a variety of enterprise applications to perform their objective.

How can Ema’s Omniglot Agentic AI Help your Enterprise?

The flexibility of Ema’s Agentic AI enables value creation in every potential function for a myriad of use-cases. Ema’s platform allows creation of customized, multi-agent AI employees to revolutionize your workforce.

Our enterprise customers are already deriving value from our platform across a wide range of functions including 

 across both multi-cloud and on-prem deployments.

With our focus on robust security and compliance including SOC 2, ISO 27001, HIPAA, NIST & GDPR, Ema offers the most performant and trusted Agentic AI platform to deliver value to your enterprise.

 Ema to transform your enterprise and discover what you can achieve.

Explore Ema's Agentic AI Platform, your versatile AI solution for customer support. Ema excels in engaging interactions, informed decision-making, and seamless workflow automation.

Ema's Agentic AI

Rise of the Omniglot: Agentic AI to Tackle Enterprise Automation


First, we’ve no “manager only” role in the company.

We expect every manager to also play an IC role. We don’t have any ‘manager only’ position in Ema, this includes senior leadership as well. Our managers spend a percentage of their time going deep and building/creating themselves. For example, in engineering, managers will write low level designs, build prototypes, write code, perform code reviews, and delve into any technical topics as needed. Our managers actively reduce mundane busywork to be able to focus on excelling in their IC role.

…we do not care about hiring world class managers or developing management skills for our employees. In fact just the opposite. We'll invest in growing our managers while making sure they are capable of and willing to do IC work.

Ema managers build championship teams, don’t win popularity contests.

Hire people who are better than yourself. 

Continue to raise the bar on talent for the team and for the entire company. This means consistently finding people who are better qualified than the manager and the existing team.

Primary job of managers is to unlock the true potential

 of every individual and create the most productive teams. Imagine you are trying to coach the Math Olympiad team for your country. You won’t fill the team with average math students, you want to find extraordinary talent and you want to stretch them and coach them to be the world's best. That’s the philosophy we follow at Ema.

Your job is not to please everyone in the team. Your only job is to empower our teams to create maximum impact for the company.

Invest your time and resources in high performers.

 These are the individuals who will drive the team's success and impact. Ensure that their skills are matched perfectly to their roles. By concentrating on high performers, you create an environment where excellence thrives. Let low performers go quickly. Tolerating low performance demoralizes high performers. Read the book “

 of ‘adequate performance deserves a generous severance package’. We expect every employee to go above and beyond. As managers your job is to uncover the next level of performance for your team and for the company.

"The only way to discover the limits of the possible is to go beyond them into the impossible." – Arthur C. Clarke

Ema managers encourage their teams to challenge the status quo and explore new frontiers. Impossible goals help us think through the constraints more crisply and allows us to think outside the box. For example, encourage the team to think ‘why does it need 3 months to build this feature? Can it be done in 3 weeks? How about 3 days? How about 3 hours?’, ‘Can this feature scale to 10000 documents? How about a million documents? How about a billion documents?’ ‘Can we get 100 customers in 30 days? How about in 7 days?’ Ask questions and challenge assumptions. That’s how we innovate.

…you should encourage your teams to cut corners to get something done. As an enterprise software company trust is everything for us. We build robust, secure and compliant software. Embracing the impossible is a thought experiment that allows us to constantly challenge the status quo thinking.

Don’t be afraid to “micro-manage”, we call it “micro leadership”.

Brian Chesky’s philosophy of managing products

. Our managers are hands-on leaders who immerse themselves in the details of their team's work. They set clear expectations for their direct reports and actively engage in all aspects of the project. This deep involvement allows managers to accurately assess the quality of work produced and gain valuable insights into team members' performance. By staying closely connected to the day-to-day operations, our leaders can make informed decisions, provide meaningful feedback, and drive continuous improvement across the organization.

…you should continue to micro-manage every individual all the time. Once you gain a deeper level of alignment with your team, you want to empower the team to move faster and not be a bottleneck.

"Success is not final, failure is not fatal: It is the courage to continue that counts." – Winston Churchill

Setbacks are an inevitable part of any journey towards success

. As a leader, it's crucial to foster resilience within your team. Encourage your employees to view failures as valuable learning experiences rather than defeats. Create an environment where calculated risks are encouraged and mistakes are seen as opportunities for growth.

Teach the importance of persistence in the face of adversity.

 Help your team understand that significant achievements often come after overcoming difficult challenges. Celebrate not just the final success, but also the determination shown along the way.

 means using feedback to constantly improve and maintaining a mindset of tactical enhancement. We reject the notion that something working well cannot be improved. Foster a culture where feedback is actively sought and implemented. Regularly review processes, identify areas for improvement, and celebrate progress. Empower your team to own their development, stay curious, and apply new knowledge. Prioritizing continuous improvement keeps your team agile, competitive, and ready for future challenges.

 Treat each crisis as an opportunity to not only improve the product, but also improve the process and team bonding. Startups will have crises.

Lead by example in demonstrating resilience

. Be open about your own struggles and how you've overcome them. This transparency can inspire your team to adopt a similar mindset. Provide necessary support and resources to help your team bounce back from setbacks, such as mentoring programs or professional development opportunities.

By cultivating resilience, you're equipping your team with a powerful tool for long-term success and personal growth. Remember, true resilience isn't about avoiding failure, but about how effectively you can learn, adapt, and keep moving forward.

Understand what it means to create impact at Ema

. Shift focus from mere busyness to meaningful outcomes that drive the organization forward. Encourage your team to ask themselves: "How does this task contribute to our company goals?"

Define clear metrics for success that align with the Ema’s mission

 and strategic objectives. These should go beyond simple activity measures like tasks completed, instead focusing on tangible results and value created for our customers. Remember, sometimes the customer could be an internal customer.

Teach your team to differentiate between high-impact work and low-value busywork.

 Empower them to challenge processes or tasks that don't contribute significantly to the company's goals. Foster an environment where quality of output is valued over quantity of input.

Regularly review and prioritize projects based on their potential impact

. Be willing to reallocate resources from low-impact activities to high-impact initiatives. Encourage your team to regularly assess their to-do lists and calendars, ensuring they're dedicating most of their time and energy to impactful work.

Celebrate and reward impact-driven achievements rather than just effort or activity.

 This reinforces the importance of focusing on outcomes and encourages a results-oriented mindset across the organization.

…we do not try out new things and experiment constantly. Generative AI is still in its early phase and we’ll learn by trying out new things. Many things we try will fail. Learning what does not work and articulating the insights clearly is  meaningful impact for the company. 

Build trust, excellence and accountability, not entitlement.

Build trust with and within your team. Lack of trust results in conflicts, politics and slow decision making. Building trust requires

: Be reliable and consistent in your actions. Follow through on commitments and promises.

: Communicate openly and honestly. Share information and decisions promptly and clearly.

: Ensure you follow up on tasks and promises 

 needing reminders. This demonstrates reliability and respect for others' time and contributions.

Coach yourselves and your team on Fast Decision Making: 

Most teams slow down and don't demonstrate excellence because decisions are stuck in never ending discussions. Encourage holistic and efficient decision making but refrain from decision paralysis due to hypothetical scenarios. As managers never:

Agree and don’t support a decision – this is sabotage and will not be tolerated.

Don’t agree and don’t support -> you are disengaged. You should not be here.

Excellence requires information, communication and operating cadence:

 Excellence demands clear information, communication, and operating cadence. At scale, well-defined processes and automation are crucial. A regular planning rhythm ensures smooth execution, while concise documents aid decision-making. Managers should focus on impactful execution, ask the right questions, provide timely feedback, and guide the team with clear communication. Effective delegation and encouraging team members to identify and address issues are essential practices.

 is crucial for a productive and positive workplace. Managers should maintain high and positive energy. High energy levels are contagious and can significantly impact team productivity and morale. Prevent negative energy and heavily call out cynicism and negative talk about colleagues or customers.

Managers should eliminate entitlement in the workplace by fostering a culture of accountability and merit-based recognition. Entitlement, the belief that one deserves special treatment without earning it, can lead to resentment and decreased productivity. Set clear expectations and ensure that recognition and feedback is completely based on performance and contribution, not tenure or perceived status. Open and honest communication is crucial; addressing entitled behavior directly and providing constructive feedback can realign expectations.

Give and take feedback as often as possible.

Make feedback a cornerstone of your team's culture.

 Provide honest, constructive, and actionable feedback regularly, not just during formal reviews. High performers thrive on timely input that helps them grow. Offer specific observations and suggestions close to the relevant events or actions.

Actively seek input from your team members and demonstrate openness to criticism. This sets an example and builds trust.

After receiving feedback, communicate which points you'll act on. Follow up on feedback you've given to ensure implementation. By normalizing frequent, honest feedback, you'll cultivate a team that's continuously learning, adapting, and improving.

Share your contact information with team members, including your phone number, to ensure they can reach you when truly needed. Share your plans with them if you would not be available, including who they can reach out to in your absence.

, encouraging team members to approach you with questions or concerns. Be responsive to urgent matters, even outside work hours, but set clear boundaries to maintain personal work-life balance and prevent misuse of your availability.

, especially those working on critical projects. This proactive approach demonstrates your support and engagement.

Remember, being available isn't just about physical presence – it's about creating an environment where your team feels supported and empowered to seek your guidance when necessary.

Tips and tricks to drive a high-performance culture and manage teams better. Stay informed with most cutting-edge management practices.

Management Style

Stay updated with tips and tricks on building a strong organizational culture focused on delivering impact to customers.

Organizational Culture

Learning to lead effectively by lessons drawn from a wealth of management knowledge and experience

Leadership

Embrace the Impossible: Managing at Ema

Monoglot systems, or 'one-trick ponies', are extinct; they were only capable of doing a few things and performed poorly at handling complex tasks. Most of the systems in use today are polyglots performing multiple tasks:

Enterprise Search Platforms can search for knowledge within diverse enterprise data sources.

RPAs and Integration Platforms (e.g UiPath, Workato, Zapier) work with multiple applications and websites.

LLMs can mimic multiple human skills including language skills, reasoning etc.

While polyglot systems have evolved across one of the dimensions (among search, taking actions or reasoning), they are unable to bring these abilities together. They fall short compared to humans who are able to think, reason, and act across many different dimensions. Humans can easily switch between various types of tasks and adapt to new situations, something that even the most advanced polyglot systems today struggle with. But does that make humans the ideal solution for enterprises?

Enterprise search systems are great at collating information from different sources within a company. However, that’s the limit of their capabilities – akin to a really smart library catalog. Unlike human workers, these systems can't draw inferences from the information they receive and take actions. For example, a search system finding a historical sales proposal, can't understand it and then update it based on the company’s latest capabilities. A human employee would excel at this task. 

Also, search systems employ rudimentary mechanisms to understand human queries and struggle to understand what people really want. They might give results that match the exact words used, but miss the real meaning behind the question. Humans however are much better at understanding a question, even if not perfectly worded.

In summary, while enterprise search systems are useful for finding information quickly, they're still far behind human employees when it comes to understanding context and taking appropriate actions.

RPAs (Robotic Process Automation) and integration platforms are digital robots capable of undertaking tasks across different computer systems (e.g automatically enter data from an email into a customer database). However, they too have some big limitations. They can't search for information like enterprise search systems. If asked to find all customers meeting a certain criteria in their workflow, they would struggle – unless the logic is "hard-coded" in the workflow.

They're also very fragile. If the systems they work with undergo even minor changes, they often stop working. While humans have the creativity and intelligence to adapt to such changes, these platforms fail and are unable to tolerate even a slight deviation from their “hard-coded” tracks.

As these digital robots lack the intelligence to make decisions based on new information, they are hence unable to dynamically cater to customer questions or requests that aren’t explicitly pre-programmed. Also, they only understand computer language, not natural human language resulting in a necessity to provide them with exact, step-by-step instructions in computer code or through a clunky graphical interface.

For example, imagine an RPA set up to process refund requests. It may work fine till the refund form changes slightly and then stop working entirely. Additionally, if a customer asks for a special type of refund the RPA wasn't programmed for, it wouldn't know what to do. These failings severely reduce the usability of these platforms for enterprises.In contrast, a human employee could easily adapt to changes, intelligently cater to unusual requests, and communicate directly with customers in normal language.

Large Language Models (LLMs) can understand and speak human languages, which is quite unique. However, just as humans have differing aptitudes for different skills, LLMs too vary widely. Some LLMs are really good at understanding natural human language tasks, whilst others are great at taking lots of information and summarizing it. Some others are experts at presenting information in a nice, easy-to-understand way. But here's the catch - performant LLMs can be very expensive to use, and may even be slower than others.

The other challenge with LLMs is the lack of an enterprise’s business context - they don’t know what your company does, who your customers are and how your business processes function. While they have a huge general knowledge base, they lack the context to be useful to an enterprise. They also don't know how to work with your company's applications or data-sources. So whilst they may be good at general reasoning, they can't intelligently read a customer support ticket and update a customer's address or check on an order status.

There are ways to connect LLMs to company data and applications using tools like Langchain. However, this is extremely effort-intensive and requires specialized expertise to build and maintain. It's like trying to build a car in your garage - a worthwhile passion project, but unlikely to ever be even half as good as one made by a professional manufacturer. This do-it-yourself approach is known as 'hand-coding' in the realm of data and application integration. In both cases, enterprises have learned through the years that building by hand isn't worth the effort. Instead, they found it was better to use specialized, easy-to-use integration platforms that are faster, easier, and more efficient.

In summary, while LLMs have powerful general reasoning capabilities, they have single-dimensional expertise, lack enterprise business context and are unable to work with enterprise systems and data-sources. Adding in these missing capabilities to LLMs is an effort-intensive process requiring significant expertise.

Are human employees the ideal solutions for enterprises? Is there a better option?

The human brain is the original intelligent, do-it-all system. As humans, we can understand different contexts and perform diverse tasks in response, which is why we call human thinking 'omniglot' - meaning 'all-language' or 'all-skills'. We can learn new tasks, solve problems, and switch between different types of work easily. However, humans have limitations to their cognitive abilities and availability too. We need sleep and need breaks to recharge our brains. We are able to learn, but acquiring a new skill or unlearning an old skill can take months, sometimes years.

In the dynamic and competitive world of today, enterprises need employees with a rapid ability to learn combined with high availability to deliver impact across business functions. Hence humans too, due to the limitations in our capabilities fall short of being the ideal employees.

In summary, polyglot systems perform well on individual dimensions of accessing enterprise knowledge or taking actions through enterprise tools but lack an ability to reason. Individual LLMs are able to reason and communicate conversationally but lack the piping to connect to knowledge sources or take actions. While humans are able to perform most functions, they learn slower and are not available around-the-clock.

 serving as a digital brain that mimics how humans think and act but without their limitations. Ema operates similarly to humans, surpassing all polyglot systems - understanding natural language and business context, drawing inferences from myriad data sources using best-of-breed skills and taking actions working with enterprise applications. However, unlike humans, Ema has a supercharged ability to learn and is available 24x7 thus making her the ideal employee for an enterprise.

How does Ema’s Agentic AI platform work?

Knowledge workers need skills, domain knowledge, and the ability to interact with enterprise systems. Ema's omniglot agentic platform combines these multidimensional capabilities, providing a foundation on which individual agents with human-like capabilities are built. Ema's proprietary Generative Workflow Engine™ then creates an agent mesh that is configured and trained. Once primed for action, the agent mesh resembles a team of highly coordinated experts working together, which we call a AI Employee. Ema’s AI Employees display the same multidimensional capabilities as humans and surpass them in their ability to learn, unlearn, and relearn quickly, as well as operate continuously without breaks. Thus, they provide a powerful tool that combines the best aspects of human cognition with the tireless efficiency of AI.

Want to know more about Ema’s omniglot-like capabilities and the underlying proprietary technology powering our platform? Check out our 

 where we dive deep into Ema’s capabilities and technology.

Ema's versatile Agentic AI platform creates value across business functions and use cases. Our technology allows you to build customized, multi-agent AI Employees that function revolutionize your workforce. Ema supports both multi-cloud and on-premises deployments, ensuring flexibility for your infrastructure needs.

Leading enterprises are already leveraging Ema's platform to supercharge their operations across 

Our focus on security and compliance including SOC 2, ISO 27001, HIPAA, NIST and GDPR makes Ema the most performance and trusted Agentic AI platform to deliver value to your enterprise.

Evolution of the Polyglots: Where Enterprise Search, Automation Systems and LLMs Fall Short 

It has been a very exciting time since we came out of stealth in March this year. We increased our Series A funding to $50M, with an additional $36M raise, bringing our total capital raised so far to over $61M. This round was led by Accel Partners and Section 32 along with participation from Prosus Ventures, Hitachi Ventures, Sozo Ventures, Wipro Ventures, SCB10X, Frontier Capital and Colle Capital. Additionally, we are honored and humbled to be included in the prestigious Pegasus Partnership Program by Microsoft.

Our customer base has more than tripled since our launch, underscoring the market’s trust and interest in our innovations. Today, we proudly unveil our AI Employee Builder Platform, on-premise deployment capabilities, and our accreditation to the latest compliance standards in AI. The journey has been incredible, and we are excited to be at the forefront of transforming the future of work with Agentic AI, all the while recognizing that this is just the beginning.

Eighteen months ago, we embarked on a mission to revolutionize enterprise productivity with Universal AI Employees. We pioneered the development of Agentic AI, introducing Ema’s AI employees, designed for specific roles. With over 200 pre-built connectors, Ema’s AI Employees seamlessly integrate with internal data sources, create knowledge graphs, learn from human feedback, and operate tools to perform effectively in various enterprise roles. This innovation eliminates the need for extensive model training or manual fine-tuning once a AI Employee is built.

Our customers can now deploy any of our pre-built AI Employees within minutes, covering areas such as 

. Working with our customers we have developed specialized AI Employees for tasks like 

, business proposal writing and many more. This rapid deployment capability is a key reason CXOs choose Ema—delivering the fastest time to value without compromising on security and compliance.

At its core, Ema’s AI Employees are collections of proprietary AI agents that collaborate to perform complex workflows. Our proprietary Generative Workflow Engine™ generates workflows and orchestration code, selecting the appropriate agents and design patterns. Ema leverages well-known agentic design patterns, such as reflection, planning, tool use, multi-agent collaboration, language agent tree search (LATS), structured output and multi-agent collaboration, and introduces many innovative patterns of its own. Our comprehensive Agent Library is powered by the patent-pending EmaFusion™ model, which combines the capabilities of over 100 public and private models to deliver the highest accuracy at the lowest cost and latency for each task.

Today marks a significant milestone in our journey as we unveil the Ema AI Employee Builder platform. This revolutionary tool represents a paradigm shift in how businesses can leverage AI to meet their unique needs.

The AI Employee Builder empowers our customers to create tailor-made AI Employees by specifying goals, resources, and constraints. This level of customization was previously the domain of AI experts and data scientists. Now, with just a few guided conversations and straightforward configuration, our partners and customers can create and deploy new AI Employees customized for specific roles within their organization, faster than ever before.

This capability doesn't just expand the reach of Agentic AI—it democratizes it. Whether you need an AI assistant for a niche department or a specialized agent for a unique business process, the AI Employee Builder puts the power of custom AI development in your hands. We are collaborating with select partners to bring this platform to market and will share more details in the coming weeks.

In the rush to innovate, some may overlook the critical importance of trust and security. At Ema, these principles have been our north star from day one. We're proud to announce that we've achieved 

 with NIST RMF, NIST CSF, and NIST 800-171 standards. These new certifications complement our existing SOC2, HIPAA, GDPR, and ISO27001 certifications, forming a comprehensive framework of security and compliance.

Our security measures go beyond mere checkbox compliance. We've implemented robust systems for safe redaction and de-identification of sensitive data, rigorous copyright violation checks, end-to-end encryption of data both in transit and at rest, comprehensive audit logging, real-time monitoring, output explainability, and frequent penetration testing.

These measures ensure that enterprises can leverage our platform with complete confidence, knowing their data remains private, is secure, and in a platform that is fully compliant with even the most stringent regulatory requirements.

Listening closely to our customers, particularly our largest enterprise clients, we recognized a growing demand for greater flexibility in deployment options. In response, we're thrilled to introduce on-premises deployment capabilities for both Azure and Google Cloud Platform.

This new offering represents a significant leap forward in how businesses can implement and manage their AI initiatives. By allowing companies to keep their data within their own trusted environments, we're addressing critical concerns around data sovereignty, regulatory compliance, and performance optimization.

Our on-premises solution isn't just about data location—it's designed to seamlessly integrate with existing IT infrastructure, enabling truly scalable and secure AI solutions that can be tailored to each organization's unique business needs.

The Future: A Conversational Operating System for the Enterprise

As we look to the horizon, we see a future where enterprise software undergoes a fundamental transformation. We envision moving away from rigid user interfaces and siloed applications towards a more fluid, intuitive interaction model.

Our goal is ambitious: Develop a conversational operating system that will serve as the backbone of all enterprise operations. In this future, Ema's AI Employees will dynamically generate new workflows and render user interfaces in real-time, adapting to the specific needs of each task and user.

This shift has profound implications. It will enable small, agile teams to build and manage operations at a scale previously requiring vast resources. The conversational OS will integrate seamlessly with enterprise systems, providing a unified platform for managing workflows, data, and communication.

As we embark on this multi-decade journey to revolutionize the future of work, we're filled with gratitude for the trust and support of our customers, partners, investors, advisors, and team members. Each plays a crucial role in bringing this vision to life.

We invite you to join us on this exciting journey. We're not just adapting to the future of work—we're actively creating it. The enterprise of tomorrow is conversational, adaptive, and intelligent. And it speaks Ema!

We are always looking to hire amazing people who want to revolutionize the future of work. Please apply 

 or send us an email to recruiting@ema.co. For any business enquiries/partnership proposals please reach out at partner@ema.co. If you would like to hire Ema for a role in your company, fill up the demo request at 

Explore Ema Agentic AI, your versatile AI solution for customer support. Ema excels in engaging interactions, informed decision-making, and seamless workflow automation.

Ema Agentic AI News

Ema Revolutionizes Enterprise AI: New Funding, AI Employee Builder, and On-Prem Deployment

Innovation is the heartbeat of progress, and in the dynamic realm of technology, it's the leaders who chart the course forward. Today, we are thrilled to extend a warm welcome to Phaneesh Gururaj, who joins us as the Head of Engineering for Ema in India. With a wealth of experience and proven track record in building and scaling successful technology products at redBus, Koo, and Intouch Solutions, Phaneesh is set to redefine the boundaries of what's possible as we continue our journey to revolutionize the future of work.

Grow World GDP by Transforming Every Business with Universal AI Employees

. With Phaneesh leading our engineering endeavors in India, we are poised to reach new heights of customer impact. His leadership will also reinforce our commitment to nurturing talent within India and enriching the ecosystem of Generative AI expertise in the region.

Phaneesh is a true Bangalorean at heart and has been a key player in India’s thriving start-up ecosystem for over a decade. He has built and scaled two world-class products from India – redBus and Koo. As a technologist, his interests span distributed systems, databases, and more recently, Large Language Models (LLMs). Outside of work, he is passionate about sports, including cricket, football, and Formula 1. He holds a master’s degree in Computer Science from the University of Missouri-Kansas City and an undergraduate degree from R. V. College of Engineering.

Phaneesh’s leadership style, characterized by collaboration, empowerment, an owner mindset, and relentless drive, resonates deeply with Ema’s values. He has built and led highly talented teams across India and the US, and we are fortunate to have him at Ema.

“Ema is uniquely positioned to leverage the technology shifts that are happening around us in Generative AI. The innovation in Agentic AI is poised to transform how we work, and Ema is at the pinnacle of this revolution. The impact is seen with our Universal AI employees – a horizontal platform that is dramatically increasing productivity across all roles in enterprises worldwide. I am excited to work with a truly 10X team that is pushing the boundaries of what is possible.”

Welcome aboard Phaneesh! Together, let's redefine the future of work.

Welcome Phaneesh Gururaj to Ema

Generative AI has recently entered a new phase, introducing the concept of AI agents. This development excites many because it moves beyond the traditional one-shot inference, where LLMs generate answers based on prompts without training. As LLMs improve, so does the quality of these inferences. For complex and mission-critical enterprise workflows, non-agentic approaches hit an accuracy ceiling very quickly. Even where they excel, there usually is a better approach via Agents that can provide similar results with a faster and cheaper solution. 

Agentic AI introduces advanced interaction with specialized skills for specific tasks, autonomously decides the best action sequence, adapts plans based on context, and adjusts in real time with human or other agent inputs. This workflow mimics human task performance—planning, developing, changing, iterating, and finding optimal solutions—resulting in remarkably better outcomes.

The development of generative AI has progressed from solo language models, which respond to inputs, to AI agents and now to multi-agent systems. Multi-agent systems function like expert teams, with each agent bringing specialized skills to the table. These agents collaborate, leveraging their strengths to complete complex projects or tasks.

Workplace multi-agent systems work with software programs instead of people:

: Each AI agent is specialized in a specific task. The content creation agent generates text and visuals, the market analysis agent processes data, and so on.

: These agents need to share information. For instance, the market analysis agent informs the content creation agent about trending topics, and the performance monitoring agent provides feedback to adjust strategies.

: The agents work together towards the overall campaign goal. If the market analysis agent identifies a new trend, the content creation agent can quickly generate relevant content, which the ad placement agent then distributes.

Multi-agent systems can be very impactful with real-world applications across industries. For example, in healthcare, they can revolutionize patient care. Imagine a hospital utilizing individual AI agents specializing in diagnostics, treatment planning, patient monitoring, and administrative tasks. These agents work together seamlessly, despite being developed, deployed, and trained separately with expertise from different medical experts. For instance, one agent could analyze medical images, another could generate personalized treatment plans, while others manage patient records and coordinate care. Together, they enhance the efficiency and effectiveness of healthcare delivery, ensuring comprehensive and personalized patient care.

 Agents operate independently, making decisions based on their perceptions, knowledge, and objectives.

 Agents have a limited view of the entire system, making decisions based on their local environment and partial knowledge of other agents.

 Agents share information, negotiate, and coordinate actions to achieve their goals. Communication can be direct (agent-to-agent) or indirect (via the environment).

 Agents work together to achieve common goals or optimize shared outcomes, even if their individual objectives differ.

 Agents adapt their strategies based on experiences and environmental changes.

 Multi-agent system environments are designed to scale easily, accommodating additional agents to handle increasing complexity or workload.

Addressing Common Challenges with Agentic AI

The generative AI space is incredibly hot and lucrative due to the possible impact, leading many to invest in building in-house solutions. However, agentic AI tools are challenging to develop and deploy. Common challenges include fragmented tools, LLM hallucinations, accuracy issues, security concerns, and resource demands.

Ema has been addressing these challenges across many industries:

 Ema’s Generative Workflow Engine simplifies the creation of GenAI applications. Ema abstracts away the complexity of dealing with foundation models, computing, model tuning, infrastructure, observability and seamless UX, to provide you with a Gen AI Conversational OS. Adopting Gen AI becomes as easy as onboarding a new employee. Even non-technical users can easily create, train, and maintain Ema's AI Employees.

 EmaFusion’s mixture of experts combines 100+ models to ensure maximum accuracy and minimize hallucinations.

 Ema provides robust security, access control, compliance, traceability, and PII obfuscation. Ema is also fully compliant with leading international standards such as SOC2, ISO27001, HIPAA, and GDPR.

Ema’s multi-agent architecture offers the most flexible and easy-to-use agentic system for enterprises. It automatically creates workflows based on simple conversations and stays updated with changes, iterations, and additional information.

Agentic AI and multi-agent systems represent a transformative leap in AI. By integrating advanced algorithms and large language models, these systems bring a new level of sophistication and efficiency to complex tasks. Ema effectively implements and harnesses the power of agentic AI, setting the stage for significant advancements in automation and operational efficiency.

Discover Ema, your AI employee for customer support. Ema enhances team efficiency with intelligent interactions, informed decision-making, and seamless workflow automation.

AI Employee news

Agentic AI and the OODA Loop: A New Era of Intelligent Collaboration

We have a big mission as a small company: Grow World GDP by Transforming Every Business with Universal AI Employees. For such an ambitious mission to come to fruition, we need extraordinary talent.

 — a unique opportunity for aspiring product managers to kickstart their careers in the dynamic field of B2B Gen AI.

We deeply believe in the transformative impact APM programs can have on both organizations and individuals. I was fortunate to begin my career with the APM program at Flipkart and have witnessed firsthand how it accelerated careers for my peers and me. Our CEO, Surojit Chatterjee, led the product organizations at Flipkart and Coinbase and helped launch their respective APM programs, where he saw them deliver outsized benefits for the teams working with high-energy, enterprising APMs.

Launching an APM program is an unorthodox move for startups — the consensus seems to be that APM programs are run by teams with spare bandwidth for mentorship and low-risk projects new PMs can train on. We hold a contrarian view. Startups offer an exceptional environment for aspiring builders and entrepreneurs to learn the ropes of building products that deliver impact immediately while working at a fast pace and dealing with constraints. Most startups, by design, encourage PMs to be customer-obsessed, wear multiple hats, expand scope quickly, and fail fast and gracefully.

Our APM program is structured to offer autonomy alongside robust support. As an APM at Ema, you will independently lead mission-critical workstreams and own the end-to-end success of your products. However, we recognize that this is your first stint in managing products, and we’ll ensure you are supported through mentorship, coaching, and access to a wealth of resources to help you develop the necessary skills and mindset to succeed. Apart from being assigned a designated Senior PM mentor, the APM cohort will also receive coaching from our founders and senior leaders.

You will also gain hands-on experience across various adjacent roles, from marketing and sales to design, customer success, and data science. This multifaceted exposure will make you a well-rounded PM and is an invaluable opportunity for aspiring entrepreneurs. As a generative AI company, all our product managers are technical and AI-first, collaborating with accomplished AI and engineering partners. This program will help you stay up-to-date with the latest AI research and influence the trajectory of the industry.

Business School tuition sponsorship for top APMs

: For APMs who are given an offer to return to Ema post-Business School / Higher Education, Ema will cover half of the tuition fees upon return. APMs can choose to not pursue higher education and grow at Ema internally.

Coaching and mentorship from highly accomplished leaders

Connections to a powerful and sought-after network of world leaders for future endeavors

We envision this program as a launchpad for future leaders of our company. We’re looking for individuals with excellent problem-solving and communication skills. A technical background and past experience in leading teams or launching solutions can help you stand apart.

To join the Ema APM Program, you need to complete a deck based on the prompt provided 

. This exercise will help both you and us determine if you would thrive at Ema. The current cohort of the APM program will work out of our Bangalore office. Submit your CV, deck, and answer a few basic questions through 

Associate Product Manager at a hyper growth GenAI startup

All posts in category Engineering in AI

Welcome Phaneesh Gururaj to Ema

When 10X Isn’t Good Enough