The shift from monolithic, inflexible systems to agentic, adaptive architectures marks the next evolution in enterprise technology, promising greater autonomy, flexibility, and productivity.

Agentic systems redefine tech stacks by shifting away from the rigidity of traditional SaaS tools towards easy integrations and on-the-fly customizations.

Unlike non-agentic systems that have rigid GUIs and need constant updation, agentic systems adapt in real-time to user needs, interpreting natural language inputs to create workflows and generate contextual interfaces as necessary.

Agentic systems ensure true cross-application and data integration by maintaining ‘neutrality’—allowing users to work across ecosystems holistically.

. As enterprises make this technological leap, where software goes from playing an assistive, incremental role to being autonomous, intelligent, and collaborative, they will also have to 

build an enterprise ecosystem that can succeed with Agentic AI

This means updating everything from the processes and procurement methods of software to the mindset and metrics through which its success is measured. Importantly, tech architectures will evolve from the vertically siloed, static tech stacks that grew with traditional SaaS towards more horizontal, integrated systems, where data flows dynamically yet securely, and decisions can be made real-time by AI agents.

This technological shift from non-agentic to agentic systems will address the many critical limitations of current systems, improving flexibility, transparency, and user experience. Agentic AI will overcome the monolithic nature and weaknesses that characterize today's SaaS platforms.

Breaking free from Monolithic Architectures

Non-agentic systems employ microservices at an architectural level. In today’s SaaS applications, users are bound to a predetermined set of components, such as cloud infrastructure, data storage mechanisms, business logic, querying mechanisms, and graphical user interfaces (GUIs).

While microservices are great for breaking down technical components, they fail to provide the flexibility businesses need to design their own solutions at the product level. For example, if a company using a CRM system wants to implement a custom pricing algorithm or integrate with a specific third-party logistics system, they often need to resort to complex workarounds to achieve this.

In contrast, an agentic AI system will dynamically adapt to new requirements by incorporating functionalities and integrations without disrupting the overall system.

This is possible because the workflow-logic in an agentic system is implemented outside the core business logic. Think of it as software where not just the components but the workflows themselves are interchangeable, customizable, and adaptable.

The difference is best illustrated in the graphics below:

In agentic architectures, the boundaries that separate different software modules are intentionally porous, enabling easy integration and customization on the fly. Want to swap out your CRM’s default pricing algorithm with one that is specific to your niche industry? Such modifications aren’t simply possible with agentic systems, they are also seamless, with the new functionality adapting smoothly into the rest of the architecture.

Workflows in non-agentic systems come with a set of challenges, including predefined, rigid paths, technically complex setups, and a limited ability to adapt to changing requirements. Enterprises are forced to conform to the logic embedded within the software, rather than molding software to their unique needs.

Consider how actions across multiple systems—say, CRM, ERP, and ticketing—often require navigating several different interfaces. Users must follow rigid, predefined processes that rarely match the real-life, dynamic scenarios in which they operate. This lack of flexibility not only limits user experience but also creates barriers that stunt process innovation.

But with agentic systems, workflows are adaptive, evolving on the fly based on natural language commands and personalized preferences. Imagine prompting an agentic system with, "Connect my CRM to ERP and initiate an alert anytime inventory drops below a specific threshold," and watching the system interpret and execute that intent—creating cross-application workflows dynamically, without intervention from IT.

The real advantage for agentic AI systems lies in 

. Instead of being confined by static process flows and technical jargon, agentic systems enable users to describe their needs in plain language, transforming those inputs into actionable workflows. The result is a fluid orchestration that adjusts based on real-time user inputs, external triggers, and evolving business needs.

Traditional non-agentic systems rely heavily on GUI-based interactions between humans and computers, which has proven to create a brittle user experience. Over the past three decades, substantial investments have been made in improving GUIs and UX design across software, but a core problem remains. These interfaces require constant upgrades to maintain a "fresh" look and feel, resulting in a cycle of continuous redevelopment and user retraining.

Consider, for example, the evolution of MS Office, and the introduction of the infamous "Ribbon" interface, which disrupted productivity across its user base and caused them to relearn how to perform basic tasks. SaaS tools tend to undergo major UI overhauls periodically, requiring extensive retraining for employees and the risk of churn from users.

Today's GUIs are often rigid, limiting structures that need precise data models, extensive coding, and specific training for proper use. By the time users become proficient with a particular interface, it may change. If a CRM system has a specific workflow for entering customer data or creating reports, and this doesn't align with a company's own processes, users need to adapt their work to fit the software—rather than vice versa.

Agentic systems, on the other hand, introduce a paradigm shift by replacing direct human-computer interaction with a 

 In this new model, AI acts as the intermediary between human intent and computer execution. The interaction with AI occurs through natural language, eliminating the need for evolving GUIs and reducing the learning curve for users. And when GUIs are needed by users, agentic systems can generate those too on the fly.

Imagine an employee who wants to apply for vacation. In a traditional HR tool, she would need to navigate multiple screens and systems, starting with checking her remaining vacation days, reviewing all her ongoing projects, verifying if any are in a critical phase during her planned time off, and then formally applying for vacation in the right system. Her manager would then repeat similar steps while reviewing and (hopefully) approving the request.

But with an agentic HR system, the employee could simply type in, "I want to take 5 days off starting 1st Feb. Check all my projects and submit a request if everything looks good." Agentic AI would understand the context and intent, access the necessary systems, check project statuses and vacation balances, and submit the request if appropriate. The manager would receive a notification from the system, indicating the employee's vacation balance, the state of active projects, allowing for an informed decision.

The employee then receives a notification reflecting on the manager’s decision, and both have achieved the entire process without logging into more systems or navigating extra GUI screens. If visual representation or interaction is needed at any point, the AI dynamically generates the appropriate GUI, tailored to the specific task.

By shifting much of the functionality to AI agents, we can create more flexible, intuitive, and adaptable user experiences. Agentic AI can generate new ways to present data on the fly, is customizable to individual user preferences, and addresses the limitations of current rigid GUIs.

As we consider the future of software and business automation, agentic systems are going to redefine how humans interact with technology. The concept of ‘neutrality’ will play a crucial role in this shift, as cross-application functionality without bias becomes essential.

: Just as systems of record struggled to provide seamless cross-application analytics, non-agentic SaaS systems have difficulty providing true integration across different ecosystems. Agentic systems overcome this with a neutral stance, enabling effective integration without bias toward one ecosystem.

: Agentic systems need to interact with diverse data formats and vendor-specific structures—doing this impartially enables richer and more effective integrations.

: One of the biggest concerns users have with traditional SaaS systems is the risk of vendor lock-in. Neutral agentic systems allow users to integrate with any platform, reducing that risk and empowering companies to evolve without constraints.

: APIs will become the primary interface for agentic systems, connecting software components, databases, and services. This API-centric model allows for flexible interactions, adapting in real-time to changing needs.

: With AI as the primary interaction interface, GUIs will become task-specific tools that are rendered based on what the user actually needs, eliminating the cognitive burden of inconsistent designs across different systems.

: Instead of managing multiple logins and switching contexts, agentic systems will offer a single, unified interface where users can interact with all their digital tools seamlessly, primarily with natural language.

While existing SaaS vendors have an inherent conflict of interest in creating truly agentic systems, given they could cannibalize their current product offerings, we at Ema have built a horizontal agentic AI platform with the above technological shifts in mind, with a vision to make the relationship between humans and technology more seamless. 

 is therefore not just an incremental improvement, but a leap akin to the industrial revolution, redefining how humans work with software, which can finally prioritize adaptability, personalization, and intelligence.

Agentic vs Non-Agentic Systems: How Tech Stacks Differ in the New Paradigm

Time is everything when it comes to closing deals with enterprise proposals. Crafting the perfect proposal often takes weeks or months, requiring meticulous attention to detail, cross-referencing vast repositories of documents, and managing input from multiple stakeholders.

For large enterprises, the process is even more challenging: thousands of documents need to be scanned, categorized, and synthesized. Onboarding a new team member into this complex workflow adds another layer of challenge, as they navigate through an overwhelming volume of content and try to align with various teams.

This was the reality for one of the world’s top 10 leaders in IT services, whose Request for Proposal (RFP) team faced immense pressure to deliver high-quality proposals under tight deadlines. That’s when they turned to 

Ema’s Proposal Manager AI Employee—and

 found the transformation nothing short of remarkable.

The Solution: Ema’s Proposal Manager AI Employee

Recognizing the challenges of their complex workflows, this enterprise partnered with Ema to redefine their proposal generation process. We at Ema worked closely with their team to integrate their vast repository of documents, ensuring everything was meticulously categorized to provide precise context to Ema’s Proposal Manager AI Employee. This ingestion and categorization allowed Ema to generate proposals that were not only tailored to the company’s bespoke requirements, but also in one-third the time their human employees took — allowing these teams to focus on more creative, value-added activities.

With Ema, what used to take weeks now takes days, or even hours. By leveraging Ema’s ability to pull insights, generate accurate first drafts, and align content with the company’s style, their team saw immediate improvements in productivity. Senior proposal writers were able to focus on strategic elements, leaving the smaller tasks to Ema’s AI-powered efficiency.

Why Ema’s Proposal Manager Employee Stands Out

: Ema reduced the time spent on proposal generation by 

, turning a multi-week process into one that could easily meet tight RFP deadlines. Ema saved countless hours, giving the team the freedom to focus on creativity and strategy.

: Ema’s team provided round-the-clock support, quickly addressing feedback and tailoring the onboarding process to be smooth and hassle-free for this IT services leader. This collaborative approach was necessary for success.

: Ema’s Proposal Manager AI Employee efficiently scanned and synthesized information across thousands of documents, ensuring no crucial detail was missed. With this precise context and real-time data integration, Ema ensured every response was highly accurate, relevant, and free of errors and hallucinations that might come with public LLMs

: Ema adapted seamlessly to the company’s workflow, handling complex tasks while scaling effortlessly for future needs. Whether it was creating detailed executive summaries or pulling specific data points, Ema delivered responses that were on-point and aligned with the company’s expectations.

For enterprises whose business depends on creating high-stakes proposals and quality long-form documents, Ema is more than just another software tool. It is an intelligent, collaborative AI employee that can be customized to your specific enterprise contexts and collaborate with human employees to elevate every step of document generation. By transforming a traditionally time-consuming process into a seamless,efficient, and delightful workflow, Ema’s Proposal Manager AI Employee empowered this IT leader to stay ahead in a competitive market.

If your organization is looking to unlock faster, smarter, and more accurate proposal generation, 

 is the solution you’ve been waiting for.

 today, and accelerate your business with fast, high-quality document generation.

Revolutionizing Proposal Writing: How Ema Helped a Leading Enterprise Boost Efficiency by 3x

Documents are how teams across organizations make progress in business. But behind every polished document lies a cumbersome process hindered by manual effort, fragmented workflows, and the need for specialized expertise—until now.

We at Ema are delighted to launch the most comprehensive AI solution for long-form document generation, across industries and use cases.

Over the past year, Ema’s Document Generation AI Employees have helped hundreds of daily users achieve over 80% productivity gains. Users consistently describe Ema as “addictive” and “the easiest way to publish the best document you’ve written".

Here’s how Ema creates world-class documents, achieving the complexity, nuance and accuracy of a human.

The Critical Role of Documents in Modern Enterprises

Across industries, enterprises depend on documents to function seamlessly. Just think about the last week—how many hours did you spend building a document, slide deck, or spreadsheet? These instruments are core to how we plan, align, execute, and communicate our work.

Whether in procurement, finance, sales, HR, or a more specialized team, documents serve as the backbone of communication, decision-making, and action. Knowledge workers in industries like legal services or BFSI are especially familiar with long chains of documents, which are put together after complex processes of data collection, research, and fact-checking.

Ema's Document Generation suite was built to streamline this complexity, making it easier for teams to manage and produce the documents that drive enterprise success.

Chain of documents that represents a typical procurement process

Generating long-form documents with LLMs is a hard problem

Text generation may seem like a solved problem for many AI language models, but long-form document generation of enterprise-grade quality remains a hard problem. Large Language Models (LLMs) struggle with creating accurate, grounded, and high-quality documents due to hallucinations and poor alignment with human expectations.

The other large challenge with adopting AI in these flows is a more human one: tenured users who are accustomed to creating their documents a certain way find it difficult to change their behavior and learn to work with AI in a collaborative way.

Ema's Document Generation suite solves these issues by leveraging specialized AI agents, real-time data integration, and a user experience designed to make AI usage delightful.

Introducing Ema's Document Generation Suite

With Ema’s Document Generation AI Employees, you can create long-form documents composed of hundreds of sections in under 5 minutes with near-perfect accuracy–without losing the joy of performing a creative and intellectually challenging task.

Ema learns from your enterprise data such as documents and apps, and combines it with real-time data from trusted public sources. Ema provides hundreds of one-click integrations so you can onboard and train it with the same resources as your human employees.

 Ema can handle complex workflows autonomously. She knows when to pull specific internal and external data sources, how to perform analyses, and can customize the output for each document. This also allows Ema to create sophisticated charts, infographics, and other illustrations.

 Ema ensures that each section of a document is grounded in real, accurate data. Going beyond single-LLM calls and traditional model routing methods, Ema’s proprietary 

 combines over 100 public and private models to provide the highest-accuracy outputs at the lowest costs and latency, ensuring high quality in long-form documents.

Whenever Ema is unsure or lacks data, she knows to abstain and request human guidance. Ema also clearly explains her process pre- and post-document generation for each section of the document, allowing you to easily verify and modify content.

Ema provides the steps taken by the different agents to help you easily modify her behavior

Ema provides granular sources to help you examine and verify the content better

: Ema is capable of working across different document formats—from PowerPoint to Excel to Docx—producing a level of depth typically only available in human subject matter experts.

While Ema can produce really high-quality documents in the first attempt, it’s her 

comprehensive human-AI collaboration features

 Ema allows multiple stakeholders to collaborate seamlessly on a document in real-time, working closely with AI in tight feedback loops. With status tracking, versioning, and commenting at a section level, Ema ensures you don’t have to ever navigate to another tab.

Collaborate with your teammates, easily manage statuses and track progress at a section level

 Generate complex documents by giving Ema instructions as if you were assigning tasks to a human colleague. If a section doesn’t meet your expectations, simply regenerate it—Ema can incorporate new sources, change formats, or adjust based on your feedback. Ema will learn to adapt to you and your company’s feedback over time.

Just tell Ema what changes you need and Ema rewrites content in seconds

 Whether you need sales proposals, prospectuses, legal documents, or onboarding manuals, Ema tailors the document by understanding your specific needs at the section level. For sections you expect specific behavior for, set up categories of sections and specify natural language instructions centrally so Ema can follow recommended steps during generation. Ema can also self-instruct based on past examples and the context of the document.

Setup instructions for common types of sections centrally, for better alignment with your team’s expectations

Lastly, Ema is enterprise ready with robust data protection, compliance, and privacy features to keep your most sensitive data safe. Ema's data governance redacts PII data before passing it to public LLMs. And with RBAC, team level permissions, and different roles, confidential projects always remain confidential. Ema also offers on-prem deployment for customers that prefer it.

Success Stories: High-quality documents, up to 10x faster with Ema

Ema has already transformed document workflows across industries. These success stories are just the beginning. As we bring the product to more teams, we’re excited to see new ways it unlocks to boost productivity and enhance the quality of document generation workflows.

Ema supported hundreds of proposal managers in a top 10 professional services firm, resulting in 3x faster drafting of proposals, enabling the humans on the team to focus on the differentiators while Ema deals with the mundane repetitive questions. This led to a higher proposal output, a 20% increase in deal win rate, and removed the need to hire against attrition.

A Big 4 audit firm used Ema to improve efficiency and speed of creating monthly prospectuses for more than 20,000 funds. Traditionally done by a large team of humans over just a period of 4-5 days, prospectus publishing was a stressful time of the month for their team. Ema is able to create a 90% accurate first draft in minutes, complete with the right charts & formatting for each fund.

Two leading professional services companies use Ema to instantly generate deeply researched Sales Intelligence reports, with all public and third party private information on their potential clients collated into company and individual level documents. In the past, only about 10% of their sales teams were able to receive this support from internal Sales Intelligence teams and the turnaround time to get a report was 3-5 days. With Ema, every interaction or lead is enriched with real-time data in less than 2 minutes, enabling them to uplevel their sales and win more deals.

Ready to Transform Your Document Workflows?

Ema's Document Generation suite is here to help you 10x the process of creating high-quality, accurate long-form documents faster than ever. Elevate the way your enterprise creates and handles documents by using 

, so you can forget the repetitive and mundane and focus on truly moving your business forward.

Introducing Ema’s Document Generation Suite: Create Quality Business Docs 10x Faster

Today, we’re introducing the newest addition to Ema’s Sales & Marketing suite: 

Designed to generate qualified leads on autopilot, Ema’s AI SDR helps businesses book 5x more sales meetings per Account Executive (AE), at just 1/10th the cost.

Some of the world's leading enterprises use Ema to transform their deal lifecycle, from automating sales intelligence and proposal creation to even contracting and deal closure. Today, with the launch of Ema’s AI SDR, we’re excited to extend these benefits to sales prospecting, which directly drives business growth.

Here’s how AI SDR helps sales teams grow their pipeline multi-fold with quality leads while keeping costs low:

AI SDR comes with advanced people- and company-enrichment capabilities, analyzing trillions of data points across multiple sources to identify high-intent leads.

Ema’s AI SDR creates a comprehensive buyer profile that helps it write highly personalized email sequences which resonate with the prospect, ensuring higher conversion rates and more meetings at scale.

AI SDR monitors key metrics, learns from past performance, and runs automated experiments to identify messaging that works. This ensures you achieve maximum ROI with every email sent. 

Ema’s AI SDR uses best practices to ensure your emails have high delivery and low unsubscribe rates, while also keeping your domain rating healthy.

Here’s more on each capability and how it works.

Ema’s advanced data enrichment capabilities go far beyond traditional LinkedIn searches or contact databases used traditionally by SDR teams. Ema uses the following sources to generate better outcomes for your campaigns:

 Ema utilizes a multi-provider data enrichment model to provide you the highest data quality and maximum coverage on contact and company related data, at a fraction of the cost.

 Ema tracks important events like job changes, new hires, promotions, and more across news and social media to help you pinpoint your ideal buyers at the right moments throughout their buying journey.

With a one-click CRM integration, or a simple CSV-upload, Ema allows you to import your leads into our intent classification models. Ema then enriches your data with additional insights tailored to each lead, helping you optimize outreach further.

Ema makes prospecting effortless. With a high-level description of your Ideal Customer Profile (ICP), Ema identifies relevant sample leads in real-time. (You can also tweak your criteria later, if required.) Once you finalize the ICP, Ema analyzes and scores all available leads, generating a refined list of the best-fit prospects based on our in-house intent classification models.

Ema then creates a detailed, well-researched profile for each prospect, with details on the prospect’s background, role, and their pain points. This allows your team to drive meaningful conversations with prospects and engage them more effectively.

Ema deeply understands your buyers, products, and business to craft hyper-personalized email sequences that resonate with prospects.

Ema builds deep knowledge of your company and products in just minutes, using relevant information from the web, product release notes, supporting documentation, blog posts, FAQs, etc. By consuming your sales playbooks, Ema adapts to your team’s best practices for targeting, messaging, and follow-up strategies.

Refine Ema’s output with simple instructions

Ema starts by presenting an example email template, giving you a clear idea of the structure and tone it will follow. Refining this template is effortless—just use natural language instructions.

For instance, if you prefer using bullet points to focus on product benefits, simply tell Ema “use bullet list”, and she’ll adapt the email to fit your preferences seamlessly.

Whether you’re targeting English-speaking prospects or otherwise, Ema’s multilingual capabilities enable you to connect with all your prospects. When it's hard to explain what you want, Ema’s AI SDR can be instructed to look at your past emails, so she can learn to incorporate your style effortlessly.

Smart Follow-Ups, Tailored to Your Strategy

Ema’s AI SDR creates a suitable strategy to follow-up with prospects, generating sample emails for each stage of the sequence. These follow-ups will also link to your case studies and blogs, and include relevant CTAs that are likely to convert.

Once you launch your campaign, Ema generates personalized email sequences for each lead as shown below.

You can view the email sequence generated for each lead, and edit further if required.

Ema tracks its performance against key metrics like emails sent, open rate and response rate and continuously improves by understanding what works and what doesn’t.

For every campaign, Ema generates multiple variations of instructions and runs automated A/B experimentation on them. By comparing performance across these versions, Ema identifies what resonates best with your audience and target market. These insights are then applied to future campaigns, allowing Ema to continuously refine its strategies and improve outcomes through real-time learning and feedback.

AI SDR’s self-learning capabilities are powered by Ema’s fusion model which enables it to generate the highest quality output at the least cost. Fusion is a mixture of expert models 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 internal enterprise data — to maximize relevance and accuracy.

Ema provides a proprietary mailbox management and deliverability engine designed to minimize the chances of your emails landing in spam. It automatically monitors your DNS settings, assists with cleaning your email list, and offers email warmup capabilities to improve inbox placement.

The emails crafted by AI SDR adhere to best practices, ensuring they avoid spam filters. Additionally, AI SDR includes built-in features like daily sending limits and opt-out options, keeping your campaigns fully compliant with email sending regulations and maintaining your sender reputation.

Ema adheres to industry-leading compliance standards, including the GDPR, SOC 2 (Type I and II), ISO 27001:2022, HIPAA, and NIST. Our data governance also redacts sensitive information before passing it on to public LLMs. Along with top-tier encryption and customizable private models, this well-rounded approach to security underscores our dedication to handling sensitive data with utmost care.

Automate more than sales prospecting with Ema’s Sales & Marketing suite

Unlike other vertical solutions, Ema is a horizontal Agentic AI platform whose suite of AI employees automate every aspect of 

 — from sales intelligence and sales data analysis to proposal generation and prospecting.

Ema seamlessly integrates with the tools you already use, ensuring a smooth and unified workflow. Further, with Ema’s Generative Workflow Engine™, you can create custom AI employees that can tackle the most complex workflows in any business function, whether lead scoring, meeting scheduling, CRM updates, sales forecasting, or more. Ema’s one-stop solution for 

 provides a holistic tech stack that amplifies the work of all your teams, so they can be more efficient and creative in their work.

Hire Ema today to transform your sales and marketing

Our vision for Ema’s Sales and Marketing suite is to eliminate the repetitive, time-consuming tasks, so that human sales teams can focus on what they do best—building relationships.

With Ema’s AI SDR, you can save 90% of your time, otherwise spent on researching leads and crafting personalized emails—enabling your team to focus on meaningful outreach.

 today. We’re excited to see your calendar filled with high-quality sales meetings.

Introducing Ema’s AI SDR: 5x more sales meetings without adding headcount

Enterprises have a variety of tasks that can be solved via intelligent automation, but no single public LLM can achieve the accuracy needed in specific and dynamic enterprise contexts.

While using multiple models and applying model selection is the right way forward, traditional and ML-based methods of model selection do not reliably deliver high accuracy at low costs and latency.

intelligently combines the best public and private models to achieve the highest accuracy at the lowest costs and latency, while also preserving the security of sensitive enterprise data.

Enterprises face a daunting challenge when it comes to using AI, given how rapidly it is evolving. The key question is: 

How best can you leverage Large Language Models (LLMs) for various enterprise tasks, while achieving high accuracy at the lowest possible costs and latency?

With over 100 LLMs available today, each offering unique strengths in accuracy, latency, and cost and for different tasks, the natural inclination might be to hand-pick the best single model for each job—but this approach is fundamentally flawed and inefficient.

Agentic tasks in an enterprise range from highly simple to highly complex. For example, a straightforward task might involve generating routine email responses, or summarizing meeting notes. In contrast, complex tasks could be providing nuanced customer support, synthesizing vast amounts of enterprise knowledge, or conducting predictive analyses based on intricate data sets. The diversity and complexity of these tasks make it impossible for any single LLM to meet enterprise-grade requirements efficiently and securely.

The idea of selecting a "best" model assumes that one LLM can sufficiently balance the trade-offs between accuracy, latency, and cost across all types of tasks. But this is rarely the case. A model optimized for accuracy may be too slow for real-time applications, while a model that excels in speed may lack the depth needed for more sophisticated queries. Moreover, models that are cost-effective at scale might compromise on performance, leading to inadequate results for critical tasks.

An approach that combines multiple models for each task is thus essential. Enterprises need to move beyond the idea of a one-size-fits-all LLM and embrace using multiple models, each tailored to specific aspects of their agentic workflows.

Consider this: a lightweight model handles initial customer interactions or routine tasks, while a more powerful, resource-intensive model is used for in-depth analysis and complex decision-making processes. Such a strategic combination of models would ensure that each task is handled by the most appropriate tool, optimizing for accuracy, latency, and cost in a way that no single model can achieve.

But choosing the right combination of models in this manner, for a wide range of enterprise tasks, is no simple feat either. We explain why common approaches to model selection are inadequate for the enterprise, and how that shaped

 our vision to build EmaFusion™—our proprietary model that achieves the highest accuracy at the lowest costs and latency, in a manner that is secure, flexible, and efficient across all enterprise tasks

Why Model Selection or Model Routing is Inadequate

In their quest to leverage large language models (LLMs) for agentic tasks, enterprises have turned towards various model selection strategies hoping to identify the "best" model for each task. But both traditional and machine learning-based approaches to model selection have proven inadequate for the complex and dynamic nature of enterprise environments.

ML Expert Manual Configuration-Based Model Selection

One common approach involves having a machine learning expert painstakingly analyze each agentic task to identify the most suitable LLM. Through extensive trial and error, they configure a routing framework that hardcodes which task should be directed to which model.

While this method may seem thorough, it has significant drawbacks:

: Once a configuration is established, incorporating new models into the framework becomes challenging. The entire system may need to be re-evaluated and reconfigured, which is not only time-consuming but also limits the ability to quickly take advantage of advancements in LLM technology.

 This approach requires the expertise of seasoned machine learning professionals who must continuously monitor and adjust the system. The trial-and-error nature of the process is labor-intensive, costly, and prone to human error.

Static Solutions in a Dynamic Environment:

 Enterprises are dynamic, with constantly evolving tasks and requirements. A rigid configuration may not adapt well to changing needs, leading to sub-optimal performance as tasks evolve or new tasks emerge.

 As the number of tasks or models increases, the complexity of managing the routing framework grows exponentially, making it difficult to maintain efficiency across the system.

In these solutions, a machine learning model analyzes the prompt body of the agent's task and attempts to route it to a single LLM. While this method appears to solve for the limitations of the above approach, it also suffers from a few issues in enterprise environments.

 These models are typically trained on benchmark datasets available on the internet, which do not reflect the specific and nuanced tasks of an enterprise setting. The model selection process is thus not well-tuned to unique enterprise environments, leading to poor performance.

 In enterprises, agentic tasks are often complex, with prompts that may encapsulate multiple atomic tasks. For example, a single prompt might require generating a detailed customer report while simultaneously updating internal records and providing recommendations for the next steps. Existing machine learning-based selection models struggle with such complexity, often routing these prompts to high-capacity models that, while powerful, may be unnecessarily expensive and slow.

 Due to their inability to decompose complex prompts into manageable sub-tasks, these systems tend to over-rely on the most capable—and typically most expensive—models, leading to inflated costs and increased latency.

 The training data for these selection models often lacks the diversity and specificity required to accurately route enterprise-level tasks. This results in models that are not only biased towards general-purpose tasks but also fail to optimize for the diverse needs of an enterprise, where specialized knowledge and context are critical.

Both these model selection strategies, while valuable in certain contexts, fall short when applied to enterprise agentic tasks. They either lack the flexibility to adapt to new models and tasks or are constrained by training data and complexity, leading to inefficiencies and higher costs. There is a need for a more sophisticated approach, one that can effectively combine multiple models to meet the diverse and evolving demands of modern enterprises.

Model merging, the process of combining parameters or knowledge from multiple pre-trained models into a single unified model, has been explored in academic research as a method to leverage diverse model capabilities. Techniques such as 

parameter averaging, knowledge distillation, and weight interpolation 

have been proposed to merge models trained on different datasets or tasks.

These approaches remain largely theoretical, however, impractical for real-world use cases, particularly when attempting to merge closed-source and open-source models. Closed-source models often have proprietary architectures, undisclosed training methodologies, and inaccessible weights, making direct parameter alignment or knowledge integration infeasible. In addition, 

 further complicate the practical application of such merging efforts.

The fundamental incompatibilities between open and closed-source models—ranging from architecture differences to training data biases—highlight that model merging is primarily an academic exercise with limited utility in real-world scenarios. Practical approaches such as ensembling, Retrieval-Augmented Generation (RAG), or fine-tuning individual models for specific tasks are far more effective and implementable in production environments.

EmaFusion™: Inspired by how humans solve complex problems

 lies in the analogy of assembling a team of human experts to tackle a complex problem, where each expert brings unique strengths, perspectives, and domain-specific knowledge. Instead of relying on a single generalist model, 

EmaFusion™ integrates outputs from a diverse set of foundation models, smaller models, and domain-specific models, enabling a collective intelligence that surpasses the capabilities of any individual component.

This approach is inherently collaborative—each model contributes its specialized understanding to produce a well-rounded, nuanced solution. 

Unlike model routing, which dynamically selects a single model based on the input, EmaFusion™ leverages the collective output of all models, allowing it to synthesize diverse insights and subtleties that routing to any single model would miss.

mitigates biases inherent to any one model

, making it especially valuable for complex and multidisciplinary challenges.

Unlike traditional model selection strategies that fall short in the enterprise context, EmaFusion™ introduces a dynamic model fusion architecture that optimizes the use of all available large language models (LLMs), as well as many domain specific and private models, to deliver superior results.

Dynamic Model Fusion for Optimal Performance

EmaFusion™ is the first architecture designed to combine multiple LLMs dynamically, ensuring that each agentic task is executed with minimal trade-offs. By leveraging the strengths of various models,

 EmaFusion™ intelligently balances accuracy, cost, and latency, providing a tailored solution for every task

. This approach is especially critical in enterprise settings, where tasks can range from simple query processing to complex, multi-faceted operations.

One of the core innovations of EmaFusion™ is its ability to automatically synthesize training data. Starting with a small number of seed prompt templates—representative examples of high-level enterprise tasks—EmaFusion™ generates a comprehensive dataset that captures the nuances and diversity of real-world scenarios. This data serves as the foundation for training its novel two-tier Fusion network architecture, enabling EmaFusion™ to understand and predict the best model combinations for any given task.

The first tier of the Fusion network focuses on model prediction. It learns to anticipate which models are best suited for a specific agentic task without needing to run the task through the models themselves. This predictive capability allows EmaFusion™ to quickly and efficiently identify the optimal models, reducing the need for resource-intensive trial and error.

The second tier is where EmaFusion™ truly shines. EmaFusion™ learns how to combine the selected models in the most effective way. For example, consider a complex task like generating a detailed customer report that requires extracting information from multiple sources, summarizing key points, and providing actionable insights. EmaFusion™ might combine a model known for its data extraction accuracy with another model that excels in summarization, and yet another that specializes in generating actionable insights. 

The result is a cohesive output that is not only accurate but also delivered with optimal efficiency.

EmaFusion™  is designed to be highly flexible, with an architecture that can incorporate any model, whether it’s an open-source LLM, an API-only LLM, or an LLM fine-tuned with custom training data, ensuring that enterprises are not locked into any specific set of models. Instead, with EmaFusion™, they can continuously integrate the latest advancements in LLM technology into their workflows without disrupting operations. EmaFusion™ adapts to these changes seamlessly, so that enterprises always have access to the most effective tools for their needs.

The range and dynamism of enterprise environments demand technology and automation that goes beyond what any single LLM or model can provide. Further, traditional model routing methods between LLMs also fall short of enterprise needs, where tasks can be both simple or complicated, and are best addressed by a strategic combination of public and private models that optimizes for accuracy, costs, as well as latency.

EmaFusion™ transcends the limitations of traditional model selection by offering a 

 that can meet the evolving demands of enterprise agentic tasks. Its innovative architecture provides a powerful, flexible, efficient and secure solution for enterprises to harness the full potential of LLMs without being locked into any single one of them, or constrained by the typical trade-offs in accuracy, cost, and latency.

Why Does Model Routing Not Work? How EmaFusion is Revolutionizing the Art and Science of Combining Public and Private Models

To succeed with Agentic Business Automation (ABA), enterprises must transition from siloed, static tech stacks to horizontal, adaptive systems that enable data sharing and intelligent, real-time, secure decision-making by AI.

Enterprises will go from paying for access to software to the usage and outcomes generated by it, working with vendors that are accountable and aligned with strategic enterprise objectives.

As humans go from using software as an assistive tool to working with intelligent and autonomous systems, organizations will need to equip their new superteams with robust change management practices and continuous learning and upskilling.

When electricity was first introduced into American factories in the early 1900s, 

 for more than two decades. It was only after managers reorganized their production lines to use more distributed machinery, which was possible now because of electricity, that they could start reaping the benefits of this groundbreaking new technology.

" refers to this common historical pattern, where breakthrough technologies are often followed by initially slow productivity growth. The sharp takeoff in results only happens years or decades later, when surrounding processes, skills, and business operations have also aligned to generate benefits systematically. Technology alone is rarely enough.

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