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 Personas.

 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.

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

Apply now and take the first step towards an exciting career in AI and product management with Ema!

Calling Fresh Graduates to Our APM Program - Help Change the World!

Customer support is no stranger to new tools claiming to enhance customer experience, provide relevant information, and genuinely understand customer needs. The demand for these tools is driven by rising customer expectations for instant, high-quality support, especially considering that 

 of customers abandon their purchases due to poor customer service. Yet, many companies struggle to meet these demands with tools that overpromise and underdeliver, leading to fragmented processes and endless document updates.

Tired of losing customers and dealing with fragmented, legacy support? 

 — your all-in-one AI employee for Customer Support. Ema empowers your agents to be better and make customers happier.

To truly understand how Ema tackles these challenges and the importance of solving them, let’s first explore the underlying issues businesses face.

High Demand for Immediate, Quality Support

Customers today expect rapid and accurate responses. According to 

, 37% of companies prioritize cost control in customer care, but this often conflicts with meeting high customer expectations. Customers, especially younger generations, increasingly demand personal interactions and have little patience for time-consuming, multi-step processes.

No one wants to talk to a bot, largely because everyone has had similar, excruciatingly painful experiences that end with repeating the same word over and over to try to get to a human agent. The majority of companies still rely on basic, rules-based chatbots that provide stiff, unhelpful responses, leading to increased frustration. This issue is compounded when customers are passed to an agent who has no context about the issue and has to start from scratch.

The desire and need for a cohesive customer experience are at an all-time high, yet providing a seamless omnichannel experience remains a significant challenge. Customer support is one of the key areas where brands can differentiate themselves. Customers want the flexibility to switch between channels while maintaining the context of their interactions, allowing them to control when and how they need support.

These challenges are not new, but the demand for a solution is higher than ever. Generative AI can address these challenges by offering adaptive and responsive customer interactions. GenAI offers a solution to these challenges through:

Predictive and Proactive Omnichannel Support

GenAI ensures consistent and cohesive interactions across various channels for the entire customer experience. This integration is crucial for meeting customers where they are. Ema is built with the knowledge that context and previous experiences should guide how a problem is resolved, anticipating — not just reacting to — problems. Wherever your customer is, Ema is also there to proactively help.

GenAI brought a new wave of chatbots, but the ones that make a difference can understand and respond to complex queries in a natural, conversational manner, incorporating information from previous experiences and passing it to an agent only when necessary. The Ema chatbot is easy to set up and, unlike most chatbots that take months to configure, it only takes a couple of hours. That means your team can spend more time on customer relationships and less time setting up chatbot keywords and patterns.

Are all product returns the same? Likely not, and neither should the support. GenAI support should have the context and understanding — just like a human would — of different paths and backgrounds, bringing up information that is truly relevant and valuable. Ema focuses on becoming the best tool employee your company has, consistently and autonomously learning from the best agents.

Considerations for Implementing GenAI in CX

While considering how GenAI tools can help your customer support provide experiences your customers will be happy with, here are key factors to consider:

 Understanding the time and effort required not just to start but also to maintain the support tools will help with realistic value assessment.

 Ensuring compliance with data protection regulations such as SOC2, ISO, HIPAA, and GDPR is crucial for maintaining customer trust.

 AI should augment human agents, not replace them entirely. Human oversight is essential for handling complex issues and maintaining a personal touch.

 AI tools require ongoing training and updates to remain effective. Investing in upskilling teams and maintaining AI infrastructure is essential.

GenAI represents a significant leap forward in customer experience management, and the future is bright with what can be achieved with it. However, focusing on what the tools promise and what they can actually deliver is key. Otherwise, your support team will only get another tool to manage. Ema’s unparalleled adaptability and ease of use make it a perfect addition to any customer support team. She is not just a tool; Ema is an AI Employee poised to become the best employee you’ve ever had.

Why Most Customer Support Tools Don’t Cut It

As engineers, we must always understand how our work is creating customer impact. If you don’t understand the impact of your work, you are likely doing fake work. Don’t get stuck in doing fake work.

Building a product that has a tangible customer impact.

Building a demo that unlocks customer conversations

Building a test framework that dramatically increases how fast one can write tests at Ema

Reducing the number of steps and time required for developers to iterate on code, increasing developer velocity at Ema

Building a platform that increases the number of products/personas that can be built at Ema dramatically

Building an infrastructure that scales across availability, cloud, and reliability that unlocks a new customer base at Ema.

You are expected to own your work process end-to-end. Being blocked on other job functions isn’t an excuse. Roll up your sleeves, and figure out how to get it done. Remember, engineering is more than coding, product design is more than ideation, front-end development is more than styling and UX wiring.

Build an MVP and iterate to continue your incremental impact. Don’t obsess over engineering-purity concerns that won’t make a difference at our scale. Cut scope and make simplifying assumptions to get something working end-to-end; don’t block the whole product on one pet feature.

…that only external-facing work is valuable. Your customer might be another engineer, another Ema employee in another function, or it might be the financial bottom line if you’re working on efficiency work. Regardless of the end goal, shipped impact is what matters.

…that engineering excellence, or thinking through work before doing it, isn’t valuable. Shipping doesn’t count if it’s not shipped well, because eventually, it will reduce our long-term impact. Shipping the same thing twice doesn’t count as twice the impact.

The only job title in Ema engineering is software engineer

We do not have separate roles for ML engineers. We do not have separate roles for QA. We do not have separate roles for front-end or back-end engineers. We do not have separate roles for database administrators, test engineers, or production automation engineers.

Everyone codes. Everyone understands systems at multiple levels of abstraction. We do not have strict silos. We do not have full-time experimenters or prompt engineers. We do not have architects who only design or coders who only code. It is not acceptable to refuse to do a task because you don’t know how or don’t want to — you can learn!

We do not have people-manager-exclusive roles. Engineering managers write code. They’re expected to do a slightly different set of activities, of course, but they are part of the engineering team first. Everyone is expected to contribute to product management and timeline planning, with managers as leaders and facilitators, not taking on that “softer” work all on their own.

…that we don’t value specialized knowledge or past experience. Some of us have advanced degrees or training in ML; some of us have more experience in one part of the stack than others. Play to your strengths when possible, but you’ll be expected to branch out.

10x engineering is a pattern of execution that can be learned and practiced. The most important thing is to be continuously productive and improving in one way or another.

Challenge the status quo: Don’t settle for normal goals. Look for ambitious engineering goals and pursue them with intense focus. Don’t just copy existing design patterns or code without understanding.

Keep yourself busy with multiple projects at once, so that you’re able to stay unblocked. Start and stagger multiple projects so that one is in the design phase while the second is in the execution phase. The variety will keep you from losing track of any part of the company, and different activities will operate on different cadences.

If you see a problem, solve it. Mediocre engineers wait for someone else to find problems, some others find the problems and just complain about it. 10x engineers find the problems, solve them, and move on without much fanfare.

Be frugal in design. Software bloats due to unnecessarily complicated designs that slow down performance or time to impact. Rejecting bloat means striving for simplicity and efficiency in software development. This involves making systems as simple and efficient as possible and reusing functions, modules, and APIs.

Think through your project end-to-end, and spot problems before they happen. The fastest work is work that doesn’t have to happen; the slowest work is work that has to be done twice. You’re an owner of the whole product, so getting your component done isn’t useful unless it contributes to the whole experience.

Use AI to gain leverage. We’re an AI-first company. Use AI when it’s useful, to automate tedious work, or to help form ideas. Learn how to use Copilot (every engineer has a license!), ChatGPT, etc. to their strengths to accelerate yourself when writing code.

Be your own QA. We will never have a separate Quality Assurance function in Engineering. Assuring the quality of our work, ensuring that we build is high quality, ensuring that our customers are successful, and ensuring that our product doesn’t regress is part of software engineering — testing, monitoring, and alerting. If you’re building something, your impact isn’t complete unless what you build works repeatedly and sustainably. You should personally make sure and take pride in the quality of what you build.

Use your team to ideate when you get stuck. No individual is a perfect output machine alone. Talk to your coworkers to help find information. Talk through your ideas to refine them.

…that we have exactly the same expectations from every individual. Engineers with different levels of experience will scale differently. What we do expect is that every engineer will maximize their own efforts, and continually improve.

10x individuals aren’t good enough; we are a 10x team

Even if you’re a 10x engineer on your own, you can do better by scaling beyond yourself. Team performance matters more than your individual performance.

Share knowledge and expertise generously. Write documentation so that others can understand just as well as you do. Write design docs and comments that explain why you’ve done something, not just what you’ve done. Make sure everyone is on the same page. With precise documentation, you should be able to share your knowledge, while you sleep.

Enable your teammates. Be available to participate in design discussions. Respond promptly when your input is needed (but also, find ways to make yourself less personally necessary by sharing knowledge more widely!). Share your designs before you start executing so that others don’t work at cross-purposes.

Take the time to give and receive feedback. Write comments on design docs, and point out things you don’t understand. Read pull requests and give feedback from a place of understanding, don’t rubber-stamp.

Grow the team. Participate in interviewing and hiring, and keep the bar high. Help onboard, new engineers. Mentor and guide more junior engineers or anyone new to a problem or technical domain where you have a lot of experience.

…that you aren’t still responsible for your own work. No engineer is purely an individual contributor or purely an enabler of others; all engineers should do a mix of both.

Tools serve humans, humans don’t serve tools

Machines exist to serve humans, not the other way around. If your developer tooling is bad, speak up. If the tool isn’t right or isn’t quite serving your purpose, fix it or extend it. If a tool doesn’t exist, write a new one. Software engineering is recursive, like being a blacksmith: the same skill that lets you use the tool is the skill that lets you improve it.

Processes exist to serve humans, not the other way around. If your team process isn’t making you more productive, speak up. If a meeting isn’t useful, find a better structure. If you find yourself doing busy work, talk to your manager and find a way to not do that.

Use automation wherever you can. Write automated tests. Write automated deployment. Write monitoring that will automatically tell you when things are broken. When manual human effort is unavoidable, write step-by-step playbooks or checklists so that we don’t have to rediscover how to do things.

…that we won’t do things that don’t scale, especially in new product areas. Doing the scrappy manual thing once is an important part of staying productive and iterating quickly. Doing the scrappy manual thing twice is a problem.

…that everyone needs to, or should, build or select their engineering stack from scratch. While we expect everyone to contribute to improving our engineering stack, we also benefit from consistency.

We have an extreme bias toward action. Write code or sketch out an API to make the problem you’re grappling with concrete instead of the abstract. Solve real examples of problems, not hypotheticals.

Discover your assumptions, and test them. Build prototypes that will exercise the hard parts of the problem and prove that it’s possible to solve. There are no rules and boundaries at Ema. If you want to do something to create an impact, do it. Don’t wait.

Ideas are a dime a dozen, execution is all that matters. Don’t paint yourselves into a corner by starting a project with endless ideation and discussion. Don’t sit on writing a scoping, or design document for a week. Without appropriate validation, that’s a waste of your time. Build a prototype, quickly validate your hypothesis, and then write a design document to bring the knowledge together.

At our scale, most technical decisions are two-way streets. Make a decision, write the assumptions, and move on. Don’t wait on decision-making. Optionality is useful but don’t try to write a beautiful document with 10 options. If you really think that the decision is a one-way street (e.g., choosing a vendor), use the 

…that thoughtful design isn’t important — quite the opposite. But getting to a useful, actionable design usually requires getting into the details first.

Follow through and own your projects, including quality control. We do not have a separate quality control function; we have engineers, and we expect their output to be high quality. Communicate progress, and own the outcome.

Don’t leave things half-done or let work just trail off. Complete the project, or be honest enough with yourself and your teammates to cancel it. If something isn’t working, try to understand why, and document that too — negative results are still valuable.

Remember that your project is unfinished if you only have design, code, and unit tests. The rest of the software engineering process of automated end-to-end testing, monitoring, alerting, on-call runbooks, and good communication is equally as important.

Shipping the first version of the product does not mean you are done with the product. Remember, the customer’s product journey begins at that point. We still need to continuously monitor usage, quickly fix issues, and continuously improve the product to create enormous value for our customers.

Clean up after yourself. Remove dead code, close out experiments, and document the results. Detritus of past work makes every future project slower and harder to understand.

… that we make perfect the enemy of good for MVP launches. At our stage, time to impact matters and MVP can be adequate for customer needs. Make sure to set expectations correctly with the customer and the GTM team working with the customer. Remember that our customers still expect high-quality products, but they may be constrained by the number of available features.

When 10X Isn’t Good Enough

Every week, I engage with numerous customers and encounter many CTOs, CIOs, and Chief AI Officers struggling to determine which GenAI systems would be the most beneficial for their enterprises. The focus is often on automation — how can we achieve more with less? What role will co-pilots play? Are RPAs still necessary? What about assistants? Navigating this landscape can be complex, with countless startups and established companies introducing new products. In this edition of our blog series, we delve deeper into agentic applications and their potential to transform enterprises.

 (RAG) systems, which query large documents with precision, have already gained popularity across enterprises. Today, every application claims to have a co-pilot, essentially a conversational interface for existing functionalities. Agentic systems, however, take this a few steps further. These systems encompass planning, loops, and other structures, enabling them to accomplish tasks end-to-end. Agents can use tools, making life much easier. Think of these tools as the ultimate sidekicks, helping our model interact with external data, whether by pulling in information or sending it out.

Imagine a claim adjuster in the insurance sector as an agent persona. This adjuster has a defined role and set of tasks, utilizing tools to review claim details, assess damage reports, or send approval emails for claim settlements. Agents benefit from LLMs’ reasoning abilities. As foundational models become more powerful, creating agents will become increasingly straightforward.

While basic LLMs and co-pilots are excellent for specific tasks, agents excel at automating entire complex workflows. But how do agents differ from RPAs? The difference is clear to anyone familiar with Gen AI. RPAs are essentially ‘if-then-else’ statements meticulously woven together to perform complex tasks. They are extremely fragile; any change in context requires rewriting RPA scripts. RPAs lack reasoning abilities, making them incapable of handling tasks that require understanding complex data (text, video, audio) and dynamically figuring out how to act.

We started Ema with a single mission: to create AI employees capable of assuming any role within an enterprise and automating monotonous, repetitive tasks. AI employees are a type of agentic system where multiple agents collaborate to emulate a human role. Each agent may access different models and tools. Ema’s AI employees are called a Personas. A Persona defines the role the AI employee assumes. Ema’s Persona Builder allows customers to specify the goals, resources, and constraints for Ema in the role they want to hire her for. A single conversation is enough for Ema to create and deploy a new Persona for a new role. Ema has already been trained on hundreds of enterprise applications, for both read and write actions. Ema can also call into RPAs and has API interfaces, enabling seamless integration with internal and external applications. A Persona typically spawns multiple agents, orchestrating them to complete complex tasks. Each agent can dynamically figure out what actions to take based on a high-level description of the task it receives from another agent (or humans).

Ema’s multi-agent architecture makes it the most flexible agentic system in any enterprise. In her role as a customer support agent, Ema not only acts as a chatbot to answer many customer questions but also resolves complex customer issues. For instance, in customer refund cases, Ema’s support assistant persona examines policy documents, accesses databases, decides whether a refund is warranted, processes the refund, and emails the requesting customer maintaining the tone and brand voice your company uses. Unlike the previous generation of chatbots, customers do not need to manually configure and maintain a refund workflow. Ema automatically creates this workflow based on a simple conversation with users and stays up to date with any changes in the refund policy documents, database schemas etc.

Today, Ema’s Personas are working on a variety of use cases, from aiding in the prior authorization of medical procedures and managing various healthcare workflows to adjusting insurance claims and writing business proposals.

Building Adaptive Software: How Ema Aligns with Human Feedback

Working with customers since day one has taught us that building products in Gen AI is fundamentally different. Traditional software changes behavior only when a manufacturer pushes a new update. Ema’s behavior and performance can be modified through direct customer feedback, much like a human employee. Proper onboarding and continuous performance feedback enhance Ema’s performance over time. Ema’s Personas are designed to easily receive and act on human feedback, which can be given in plain natural language. With Ema, we’re ushering in a new type of software — adaptive software that morphs based on human feedback.

How Can Enterprises Build Their Own Ema Personas?

We created a platform that enables not just engineers but also business users to build AI employees. With Ema, you can start building your next multi-agent Persona through a simple conversation. You can always switch to a visual builder or configuration window for further customization. In a follow-up blog post, we’ll go into the details of Ema’s persona builder feature.

A recurring concern for hundreds of CIOs, CTOs, CAIOs (Chief AI Officers) is a potential for AI agents to go rogue or leak private data. Ema is built with trust at its core, compliant with leading international standards such as SOC 2, ISO 27001, HIPAA, and GDPR. Robust security measures, including automatic redaction and safe de-identification of sensitive data, audit logs, real-time monitoring, robust encryption of all data at rest and in transit, and explainability across all output results, ensure your enterprise data remains private, secure, and compliant. For document generation use cases, Ema also checks for any copyright violations to ensure customers don’t incur IP liabilities. Additionally, Ema never trains models on one customer’s data to benefit other customers.

Agentic systems offer unprecedented opportunities to accelerate your enterprise. Agents learn from your best employees, providing consistent and high-quality automation services that evolve with your enterprise context. We built Ema as an operating system for the enterprise (EmaOS), enabling you to conversationally build any agentic application (Ema’s Personas). Working with customers has highlighted the complexity and enormity of this task, driving us to create real innovation and multiple patent-pending technologies.

 to transform your enterprise and discover what you can achieve.

The Guide to AI Employees: How Ema is Revolutionizing Enterprise Automation with Agentic Systems

Is your team overwhelmed with the sheer number of AI tools launching daily, and still can’t find one that meets your accuracy and reliability requirements? Single-model AI can be unreliable and inconsistent, costly, leading to wasted time and missed opportunities. EmaFusion™ changes the game.

EmaFusion™ is a mixture of experts model that utilizes most of the current foundational models — GPT 4, GPT 3.5, Claude, Gemini, Mistral, Llama2 (plus many more), and private models based on your internal enterprise data — to provide an informed result that maximizes relevance and accuracy.

EmaFusion™ allows Ema to be a universal AI employee who can perform various tasks, from engaging in conversations, reasoning, and making informed decisions to excelling in areas that require substantial knowledge and dynamically creating workflows across multiple applications.

When a task is created, the EmaFusion™ model recognizes and sends it to a subset of the models responsible for the framework that the task is based on, thus providing a more specific, accurate, and in-depth response.

Unlike AI tools focused on just one model, EmaFusion™ intelligently fuses the output from multiple models suitable for performing a particular sub-task type. To personalize the output further, you can use BYOM (Bring Your Own Model) to integrate your custom-trained models for specialized tasks.

EmaFusion™ doesn’t rely on just one source of intelligence but rather on the strengths of different foundational models to deliver the best accuracy while optimizing for cost, latency, and compliance. This approach allows EmaFusionTM to select the most suitable model for each subtask within a larger workflow, ensuring optimal performance.

AI models continuously improve, just like an internal employee would be. Ema automatically evaluates and updates the competencies of each model so the results you get are always the most accurate and optimal.

EmaFusion™ allows you to avoid vendor lock-in by adapting to the ever-evolving landscape of large language models. As new and improved models emerge EmaFusion™ can seamlessly integrate them, ensuring your AI capabilities remain cutting-edge.

Current GenAI solutions often suffer from “hallucinations,” where they produce inaccurate or nonsensical outputs. This leads to costly mistakes, wasted resources, and loss of trust in AI technology. By utilizing 40+ models, EmaFusion™ reduces the risk of hallucinations while using the best each model offers.

Privacy is one of the top-of-mind challenges when it comes to AI technology. To ensure your data is safe, EmaFusion™ obfuscates any personal information before it gets to the AI model.

EmaFusion™: The Key to Unlocking Accuracy and Efficiency

“Why do we hire the smartest people and give them jobs that are so mundane?” This was a constant nagging question during my tenure leading large product teams at Google, Flipkart and Coinbase. At Flipkart, India’s largest eCommerce marketplace, numerous data scientists were dedicated to preparing weekly reports analyzing metric movements, while thousands of customer service personnel handled refund and payment inquiries. At Coinbase, hundreds of compliance staff evaluated new user applications for account approval.

After conversations with over a hundred of enterprise leaders, it became evident that this trend was pervasive across industries. For instance, at a leading networking equipment reseller, hundreds of employees are tasked with enabling sales teams to address detailed inquiries about product specifications and cross-product compatibility. Similarly, at one of the top pharmaceutical benefits management companies, thousands of hours of pharmacists’ time is spent daily on manually reviewing patient records to determine prescription eligibility.

Amidst all the hype surrounding Generation AI (Gen AI) lately, you’d think we’d have this sorted out by now. Every CXO from every mid-sized to large enterprise is pumped about the prospect of whipping up Gen AI applications to supercharge their operations. But let’s be real here — building enterprise-grade Gen AI apps isn’t exactly a walk in the park. It resembles piecing together a complex jigsaw puzzle blindfolded, with components scattered among various vendors, and finding the right expertise feels like searching for a needle in a haystack.

So, it’s no surprise that companies are hitting roadblocks left, right, and center with their AI projects. They’re just so complicated and take ages to get off the ground. Plus, there’s the whole nightmare of data leaks. Some of these Gen AI tools out there don’t have a clue about how to keep sensitive information from slipping into those massive public language models. And trust me, you don’t want to deal with the fallout from that mess.

Oh, and here’s another fun fact: those public language models — they often tend to go off on wild tangents because they’ve never laid eyes on your top-secret enterprise data. So, if you’re aiming to build a Gen AI app that actually gets the job done, be prepared to sink thousands of hours and sky-high computational costs into it.

As one savvy CTO we spoke to put it, “Sure, you can whip up a Gen AI demo in a few hours, but turning it into a real-deal application that actually delivers ROI? That’s a whole other ball game.”

We started asking ourselves what if we could make Gen AI simple, accurate and trusted? What if we could build a true AI Employee who could collaborate with and learn from its human colleagues, take on any role in the organization and automate most of the mundane tasks?

Today, after a year of focused development, I’m excited to share that our company, Ema (short for Enterprise Machine Assistant) is emerging from stealth mode. We share with you a transformative vision, a passionate team, and a proven track record of creating impact for our customers. We believe Ema will bring in a generational shift to how enterprises work in the future. We’re delighted to see the impact Ema is already having with our current customers.

At its core, Ema functions as a Conversational Operating System, enabling enterprises to focus on business logic rather than grappling with the complexities of Gen AI implementation. With Ema, users can activate specialized Personas tailored to assist employees in specific roles, seamlessly collaborating with human counterparts to automate workflows efficiently.

Ema’s Personas operate on our patent-pending Generative Workflow Engine™ (GWE), which goes beyond simple language prediction to dynamically map out workflows with a simple conversation. Our platform offers Standard Personas for common enterprise roles such as Customer Service Specialists (CX), Employee Assistant (EX), Data Analyst, Sales Assistant etc. and allows for the rapid creation of Specialized Personas tailored to rapidly automate unique workflows. No more waiting for months to build Gen AI apps that work!

To address accuracy issues and computational costs inherent in current Gen AI applications, Ema leverages our proprietary “fusion of experts” model, EmaFusion™, that exceeds 2 Trillion parameters. EmaFusion™ intelligently combines many large language models (over 30 today and that number keeps growing), such as Claude, Gemini, Mistral, Llama2, GPT4, GPT3.5, and Ema’s own custom models. Furthermore, EmaFusion™ supports the integration of customer-developed private models, maximizing accuracy at the most optimal cost for every task.

As we unveil Ema to the world, we envision a new era of collaboration between human and AI employees, where innovation flourishes, and every employee feels empowered to unleash their creative potential. Join us as we embark on this transformative journey with Ema.