Bigblue is a leading European logistics platform that empowers 500+ e-commerce and retail brands with seamless fulfillment and delivery solutions. As their customer base grew, so did the complexity of handling support queries—ranging from shipment tracking, order delays, to returns and delivery failures. Managing these inquiries became increasingly challenging, especially as Bigblue scaled its operations and worked with multiple carriers, suppliers, and various stakeholders in the logistics and delivery funnel.

Bigblue’s Challenge: Scaling Customer Support Without Compromising Experience

In the absence of Bigblue, e-commerce brands would have to manually tie up with local carriers to ship their products and manage returns, delivery status updates, and product concerns. But Bigblue takes care of all of this, coordinating with carriers, e-commerce brands, warehouses, and various stakeholders in the delivery funnel, so that their merchants can succeed.

This makes Bigblue’s mission to scale customer support all the more challenging:

Growing, unpredictable volume of support tickets:

 As Bigblue expanded—acquiring new customers and partnering with more e-commerce vendors and carriers—the number of support requests surged. Depending on the time of the year, around festive or holiday seasons, there are also spikes in incoming customer tickets. But relying on additional staffing to handle this growth quickly becomes cost-prohibitive and operationally taxing, underscoring the need for a more 

scalable, cost-effective, reliable approach.

Bigblue’s customers may face a range of concerns. In case of issues arising from delayed or lost shipments, or damaged or incorrect products, a specific response is needed. Each situation demands a nuanced approach, requiring the verification of the claim and a resolution that meets the customer’s needs. This requires coordinating with multiple disparate stakeholders to gather the right information, carefully analyzing and acting on it.

 Another challenge for Bigblue is managing a Europe-wide customer base that submits inquiries in various languages, including English, Spanish, and French. On top of that, the company adheres to strict linguistic guidelines for its support agents—for instance, using the informal “tu” instead of the formal “vous” in French—to ensure culturally appropriate communication.

 Bigblue partners with more than 50 carriers, hundreds of e-commerce brands, and numerous warehouses. Additionally, much of the customer information would be stored in CRMs and internal databases. For support to scale effectively, it must seamlessly integrate with each of these platforms, ensuring the right information can be retrieved to resolve every ticket efficiently.

The Solution: Ema’s Customer Support AI Employee

Ema’s Agentic AI-powered AI Employees gave Bigblue the flexibility, reliability, and scalability they needed to 10x customer support, ensuring nuanced responses in multiple languages with human-like empathy and quality at scale—so much so that some of Ema’s answers are now being used as templates for helping human agents be more efficient and accurate.

Ema’s Customer Support AI Employees continuously learn from past support cases and all relevant documentation, adapting its responses in real-time to stay aligned with evolving delivery and shipment details. It intelligently categorizes and routes tickets, retrieves relevant SOPs, and seamlessly integrates with CRMs, carrier websites, and internal databases to synthesize accurate resolutions.

Unlike static bots, Ema engages in dynamic, multi-step conversations, adjusting its responses based on evolving context. Beyond answering queries, it takes action—coordinating with stakeholders, updating records, and even closing tickets once resolved—delivering 

truly autonomous, high-impact customer support:

: While Bigblue's customer care team on average responds to customers within 2 hours , Ema enables them to deliver accurate and nuanced responses in less than 90 seconds.

Efficient Handling of Fluctuations in Business Cycles:

 Cyclical businesses often experience surge in customer queries in peak seasons. Ema helped Bigblue maintain its SLAs with high customer satisfaction during the peak holiday seasons, without the operational hassles of hiring and onboarding a temporary workforce.

: Immediate, accurate responses to common queries improved the overall customer experience, reflected in better CSAT (Customer Satisfaction) scores.

: Ema rapidly learned to craft structured responses to customer queries, which then served as the gold standard for training new human agents to deliver top-tier customer support.

: Ema’s analytics layer can surface recurring pain points, whether delays at certain routes or issues with specific courier partners, giving leadership the data they need to refine processes.

Impact: Faster, Smarter, More Scalable Support

By leveraging Ema’s Customer Support AI Employees, Bigblue has reduced response times, operational costs, improved customer satisfaction, and freed up human agents to handle more complex, nuanced cases.

The ability to resolve repetitive queries instantly and accurately at scale, while maintaining brand voice and multilingual accuracy has given Bigblue an edge in logistics-driven customer support. With AI-powered Customer Support, Bigblue now delivers a superior, scalable support experience that keeps pace with its rapid growth.

"Ema integrates seamlessly into our ecosystem, autonomously resolving inquiries in seconds across multiple languages. The result has been greater efficiency and smoother operations for our merchants. We’re excited to deepen our integration and partnership with Ema Unlimited.” — Laetitia, Customer Care Team Lead

Ema’s AI Employees are designed to 10x customer support by delivering instant, accurate, and multilingual responses—without compromising on quality. 

 today and transform your support operations.

How Bigblue Uses Ema for AI-Powered Customer Support 

, the latest addition to our Customer Experience Suite—an integrated suite of powerful AI employees designed to redefine support operations with Agentic AI.

Unlike traditional chatbots that rely on FAQ searches and predefined scripts, Ema goes beyond answering questions—she resolves customer issues autonomously, end-to-end. With the ability to take complex actions, adapt to conversations dynamically, and maintain your brand voice, Ema consistently delivers natural, human-like interactions with your customers.

Ema is already helping leading enterprises automate more than 75% of support interactions in most cases while maintaining CSAT scores above 80%1. By elevating customer experience, Ema isn’t just reducing support costs—she’s turning cost centers into strategic revenue drivers.

Ema has been a game-changer for us! We’re seeing nearly 98% accuracy in responses, and it now handles 80% of our 150,000+ monthly tickets. Plus, our CSAT has jumped to 70-75%—a huge improvement over the 40-50% we typically see in FinTech.

–Venkatraman Narayan, Moneyview’s Head of Customer Experience

The Problem: Why Customer Experience Is Still Broken

Customer expectations have never been higher—they demand instant, personalized, 24/7 support. The quality of customer support is what decides if users will return to and love your brand.

Yet, despite increased spending, companies struggle to meet SLAs and retain customer trust. Here’s what’s holding them back:

 – High-pressure, high-turnover roles lead to burnout, longer wait times, and lower CSAT.

– Manual call reviews cover only 3-4% of interactions, leaving leadership blind to real-time quality.

Incomplete Knowledge Bases, Inconsistent Answers

 – Poorly documented knowledge makes it hard to deliver accurate, consistent responses.

 – Limited to simple queries on chat, they fail when customers deviate from scripts or require multi-step actions, leading to lost trust and higher support costs.

Ema’s Customer Experience Suite: Reimagining the Entire Contact Center

Customer experience isn’t just a support challenge—it spans the entire contact center, from Q&A to knowledge management, insights, and automation. Fragmented solutions lead to inefficiencies, disconnected workflows, and missed opportunities.

At Ema, we’ve built AI Employees that optimize the full CX value chain. Unlike siloed tools, our AI employees share data, learn collectively, and collaborate in real time, creating a continuous improvement loop that drives better results.

The best part? You can start anywhere and then expand seamlessly as your needs grow. With Ema’s Customer Experience Suite, businesses shift from reactive support to proactive customer success.

Ema has been a game-changer for us at Envoy Global. With its unparalleled accuracy and efficiency, it has enhanced our approach to the various elements of customer experience.

–Mahi R. Inampudi, CTO, Envoy Global and President, Envoy India

How Ema’s Customer Support AI employee Transforms CX

Ema delivers end-to-end resolution in natural language, optimizing every customer interaction for brand loyalty and business growth. Here’s how:

 – Understands customer intent, context, and policies to provide personalized, dynamic responses across channels and languages.

Autonomously resolves queries by performing multi-step actions like interacting with third party vendors, placing orders, or understanding an image. The best part? Ema’s agentic AI ensures you don’t have to pre-define every single workflow.

Intelligent agent assist (human in the loop): 

Knows when to escalate and provides real-time recommendations to human agents for faster, more effective support.

 – Grounded in enterprise data and policies, ensuring transparency, compliance, and adherence to brand guidelines. Continuously learns from admin and end user feedback.

 Learns from past tickets, eliminating the need for scripted workflows, replacing them with simple natural language instructions that can easily be maintained by non-technical users.

With Ema, support teams shift from answering every query to supervising AI-driven resolutions, stepping in only for complex cases. Here’s more on how each capability works.

Ema delivers natural, fluid interactions in 150+ languages, maintaining brand consistency and policy adherence. She integrates with enterprise systems like CRMs to personalize responses and recognize returning customers to deliver a tailored experience.

She keeps conversations on track even when customers switch topics, provide unclear details, or ask complex questions, maintaining context and responding with clarity and empathy.

Beyond just answering questions, Ema detects frustration and urgency, adjusting her tone to reassure customers and provide the right level of support. She also knows when to step back—whether due to business policies, sensitive topics, or the need for human intervention.

True resolution isn’t just answering questions—it’s executing the right actions across multiple systems. A simple refund request may require verifying order history, checking eligibility, updating records, triggering cancellations, notifying finance, and confirming resolution with the customer. Missing a step leads to frustration and repeated contacts.

Traditional systems rely on rigid, predefined workflows that fail when customers switch topics or introduce unexpected scenarios—leading to dead ends, repeat interactions, and escalations.

Ema takes a smarter approach. Powered by 

, she understands customer intent, dynamically plans actions, and orchestrates multi-step processes. She gracefully handles failures, missing data, and edge cases while ensuring compliance with business policies.

With Ema, support teams no longer need to script every scenario manually. They define key objectives in natural language, and Ema intelligently adapts—delivering faster, more personalized, and scalable customer support. Here’s a comment from one of our 3PL customers, who utilizes Ema’s multi-turn conversation and action capabilities:

Logistics is complex as agents need to coordinate with third-party vendors and make real-time decisions based on data on the ground. With Ema, we’ve achieved a 4.8/5 rating across multiple categories and languages on these complex issues. In many cases, its responses surpass human agents and are now part of our Agent SOP.

– Head of Customer Experience, e-Commerce Fulfillment Provider

Intelligent assist with human in the loop

As your AI employee, Ema balances autonomy with human collaboration. She knows when to handle issues herself and when to escalate—routing cases to the right agent based on expertise, past interactions, and workload.

When an agent steps in, Ema shifts into Agent Assist mode, summarizing conversations, surfacing insights, and suggesting the right next steps. She retrieves order details, policy guidelines, and even drafts responses, so agents can focus on resolution, not research.

Ema is your teammate. She learns from top agents, scales their expertise, and helps the entire team work smarter, faster, and more effectively.

Ema gives admins full oversight and control, ensuring transparency while adapting to your business needs.

 Every interaction is fully auditable. Admins can review her responses, understand her reasoning, and refine her behavior to align with company policies.

 Ema continuously improves by learning from both user interactions and admin feedback, adapting in real time.

Support leaders can fine-tune her tone and personality to ensure she reflects the brand’s values. 

 Beyond standard reports, Ema’s metrics dashboard offers deep insights into customer issues and knowledge gaps. The best part? Admins can take actions, right from within the dashboard.

Enterprise support is messy—issues evolve, and not everything is neatly documented. Ema doesn’t need rigid inputs; she learns directly from your helpdesk.

Just connect your help desk, and she learns from past tickets—identifying patterns and understanding how your team resolves issues across chat, email, and voice with minimal setup.

Updating her is as simple as using natural language—no complex training, no manual rule-setting. Ema is built for easy deployment and maintenance, keeping your support process agile and efficient.

Ema is built to meet industry-leading compliance standards, including GDPR, SOC 2 (Type I & II), ISO 27001:2022, HIPAA, and NIST. Our data governance framework ensures PII is redacted before being passed to public LLMs, maintaining strict privacy controls.

With top-tier encryption and customizable private models, our comprehensive security approach reflects our commitment to handling sensitive data with the highest level of care.

Explore Ema’s Customer Experience Suite

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

 — from autonomous deflection to agent assistance, QA, knowledge management and product insights.

Further, with Ema’s Generative Workflow Engine™, you can create custom AI employees that can tackle the most complex workflows in any business function, amplifying work across teams.

Hire Ema to elevate your customer experience

Customer experience is no longer just about answering questions—it’s about taking action, making decisions, and delivering real outcomes. Businesses embracing fully agentic AI are shifting from reactive support to proactive, autonomous customer success.

With Ema, companies are automatically resolving over 75% of issues with 98% accuracy and 80%+ CSAT.

Ready to Transform Your Customer Experience? See how leading brands are using Ema—check out our 

Introducing Ema’s Customer Support AI Employee: Make Every Customer Interaction Extraordinary

Ema Unlimited is pleased to share this blog from Everest Group authors, Vaibhav Bansal, Vershita Srivastava, and Yusuf Ahsan.

Agentic AI and its reliance on foundation models

The AI and automation space has been evolving at an unprecedented pace. A couple of years ago, Generative AI (gen AI) reshaped our understanding of AI’s potential. Just as the world was beginning to adapt to gen AI, agentic AI then emerged as the new kid on the block and captivated everyone’s attention. While gen AI was about knowledge assistance, agentic AI takes it to the next level through goal-based action, autonomy and adaptability

Though agentic AI is highly disruptive, it is not an isolated new technology. It builds on the capabilities of earlier innovations, especially that of gen AI and foundation models. The foundation models essentially act as the brain for agentic AI, facilitating input analysis, intent recognition, goal setting, advanced reasoning, workflow planning and decision making. This continuous loop of perceive-reason-decide enables agents to perform a range of tasks in varying environments.

DeepSeek has shaken the foundation model ecosystem

Since the launch of ChatGPT in Nov 2022, Open AI has been the industry leader for foundation models. There have also been challenges to that dominance from competitors such as Anthropic, Google, and Meta. All this while certain norms were established by these players around the model ecosystem, such as the need for advanced chipsets, high training costs, significant usage costs and the effectiveness of larger models.

DeepSeek’s entry has challenged these norms like never before. Early tests indicate its superior performance vis-a-vis competitors as depicted in Exhibit 1.

Moreover, this comes at a fraction of the cost of the existing models as outlined in Exhibit 2.

While a few claims may be debatable, DeepSeek has presented a strong proof of concept and disrupted the industry. The question now though is how such limited resources have delivered such a superior offering?

Let’s dive a bit ‘deep’ to ‘seek’ the answers:

DeepSeek’s R1 utilizes multiple expert networks, with only a few activated at a time. For any given query, DeepSeek R1 utilizes only 37 billion parameters out of the total 671 parameters, resulting in a reduced computation cost

It operates on a combination of shared experts with broad capabilities and specific experts with narrow focus areas. A built-in load balancing mechanism ensures even distribution of tasks enhancing efficiency and resilience.

It utilizes a multi-stage training pipeline which combines reinforcement learning and supervised fine-tuning as opposed to relying only on supervised fine-tuning which inflates cost.

Traditional models generate responses one token at a time, with each prediction depending on the previous one. DeepSeek predicts multiple tokens in parallel improving processing speed without major accuracy trade-offs

With these techniques, DeepSeek has challenged the dominance of the incumbent model providers who were winning owing to their compute capacity and scalability. So, how will these advances impact the broader foundation model ecosystem?

: With DeepSeek’s entry, other model providers will be forced to reduce their usage cost. These have been coming down gradually since ChatGPT was launched but expect that to accelerate going forward. Even if a direct cost cut doesn’t happen, more intelligent models will be available at the same price.

: DeepSeek utilized H800 chips by Nvidia instead of the latest H100 GPUs, which the majority of its competitors utilize. Despite the overall demand for chips continuing to climb, the product mix will lean away from the higher end GPUs, eroding manufacturer’s profit margins and reducing hardware costs for end users in the process.

: DeepSeek’s approach could be a differentiator in the short run, but will cease to be a moat in the longer run. As other model providers catch up, the techniques adopted will get standardized with foundational models becoming commoditized.

: Open-source models provide the opportunity to build, modify and integrate the offering as per requirements. While open-source models have found good adoption earlier, DeepSeek is accelerating the creation of an open-source community.

: With Explainable AI, DeepSeek prioritizes transparency and insights on how it arrives at the conclusion. This fosters a trust in DeepSeek and will push others to open their models similarly.

There has been a lot of excitement around Agentic AI’s potential to transform businesses and enable the shift towards a combined digital-human workforce. However, there have also been headwinds. The most common is the concern around cost, contributed by the high usage cost of proprietary models. Also, with usage-based pricing becoming the norm, enterprises are increasingly unprepared for uncertainty around foundation model bills.

Another challenge is the Agentic AI infrastructure. Most organizations are unable to invest in their own infrastructure to host models. Hence, they consume these models through cloud services, which subjects them to data security risks and becomes detrimental in regulated industries.

Concerns around the lack of trust in the outputs generated by an AI agent, pushes back the whole narrative around autonomy.

The disruption caused by DeepSeek is expected to solve these challenges to a good extent. Exhibit 3 demonstrates the expected short-term impact of foundational model commoditization on players involved in an agentic value chain.

While there might be variation in the extent of impact on individual players in the short term, the overall agentic AI market is expected to get a boost in the longer term. Let’s understand how:

Cost reduction, availability of open-source models and greater explainability will lead to a surge in adoption. As Jevons paradox kicks in, enterprises will be able to infuse more processes with agentic AI, without compromising their bottom line leading to increased overall spending. Agentic AI will get more democratized, expanding its reach to a range of enterprises, beyond those with significant financial resources.

While foundation models have received all the attention, the real value for enterprises is at the application layer. With commoditization, the development of domain-specific AI agents will accelerate and with the supply-side becoming more competitive, ultimately this will offer more choice and better quality to enterprises.

DeepSeek has shown us the ‘art of possible’ and might mean reduced short to mid-term profitability for foundation model players. We can expect this to be a trigger for further model engineering, optimization and innovation with advances on domain-specific and multi-modal models. Further breakthroughs in this field are going to benefit the entire agentic ecosystem boosting adoption

Enterprises that continue to rely on just one foundation model for all their needs are going to be at a continuous disadvantage going forward. The enterprise demands today are so complex and diverse that a single model can’t fulfil all of them, while being accurate as well as cost-effective. With new innovations like DeepSeek taking place every few weeks and the goalpost for the best or the most superior model constantly changing, enterprises can’t keep on switching their entire agentic ecosystem from one model to another. Therefore, model fusion technologies which dynamically combine or leverage different foundation models, are expected to become more prevalent. These technologies will not only optimize model cost and accuracy for enterprises, but will also make their agentic ecosystem future-proof by providing the flexibility to incorporate new innovative models seamlessly.

DeepSeek has provided the answer to the most prominent concerns plaguing enterprises in their agentic journey. However, these are by no means completely addressed. Enterprises, especially in regulated or data sensitive industries, are grappling with data privacy and security concerns, which DeepSeek may not have done much to alleviate. In fact, the exposure of DeepSeek’s data including chat history and sensitive information has aggravated this concern. DeepSeek’s evasive nature when encountering questions related to controversial national issues has put into question its reliability in the face of censorship.

Nevertheless, DeepSeek has expanded the horizon through innovation. With a slew of advances expected to follow, models getting more and more commoditized and model fusion becoming a norm, this is the perfect opportunity for enterprises to embark on their agentic journey. The barriers to entry have been brought down significantly allowing enterprises to sail ahead on their agentic journey without a heavy financial luggage.

The DeepSeek Effect: Impact of Foundation Model Commoditization on Agentic AI Adoption

How to Build a 100-person company without HR

, inspired us to share more on our philosophy of 

to streamline our administrative overhead and create a culture of autonomy. We’re committed to practicing what we preach at Ema, and so we create and deploy AI Employees that can automate mundane tasks, boost productivity, and free our teams up for high-value, creative work.

This is the story behind two of Ema’s internal AI Employees—

—what they do, how they were built, the results they’ve achieved, and what we’ve learned along the way.

While this blog may spark some ideas for existing Ema customers, it can also provide inspiration for other leaders wanting to know how to implement Agentic AI for tangible results in their organization.

How did we discover Agentic AI use-cases within Ema?

In order to identify the most high-impact use-cases, we analysed our team’s workflows and processes and interviewed key management stakeholders. We found a few patterns across functions that senior leaders reading this would probably resonate with:

Several key operational and technical learnings and best-practices hidden away in people’s minds

Significant overlap in information discussed across the company, leading to key team-members repeating the same explanations over and over, serving as bottle-neck in efficient operations

Significant time spent in mundane web-searches, information finding and summarisation tasks

Overburdened teams: Certain functions, like security and compliance, are especially faced with heavy and unpredictable customer demand

Loss of time spent in awaiting async responses from other teams

 were some of the key functions that dealt with the above patterns. We decided to tackle them first: Employee Experience is important because of its widespread impact on the productivity and well-being of everyone at Ema. Sales, of course, is as a strategic revenue function, which could be most affected by the above dysfunctional organizational patterns. Information silos and bottle-necks, especially in an industry as fast-paced as ours, can actively compromise a company’s ability to move fast, drive sales, and succeed.

Work Better, Faster: How Ema’s AI Employees help

Three months down the line, we at Ema have completely transformed our Employee Experience and Sales processes using Agentic AI. Both these AI Employees are available 24x7 on Slack, and perform a whole host of functions that streamline our team’s workflows.

Provides personalised (geo-specific and role-specific) answers to all questions linked to Ema’s many policies, from insurance and benefits to ESOPs, vacation and sick leave policies

Educates and onboards on Ema’s product offering and the AI landscape

Performs HR actions, including leave application, fetching payslips, updating tax information, and more

Streamlines onboarding for new joiners by creating a personalized onboarding guide for every new employee, also serving as a digital buddy to answer any questions about Ema’s culture, organisational principles, internal tools, etc.

In summary, we no longer have a traditional HR team for our team of 100+ employees, spread out across India and the US. Our Agentic AI-first approach has enabled us to be among the first few companies globally to achieve this. HR in Ema is therefore a more strategic function than usual, involved in building culture and hiring the best talent.

Creates sales briefs with company background and buyer background alongside tailored pitch points for every customer meeting

Autonomously answers all vendor questionnaires (including those on security & compliance) and writes responses to RFPs

Provides intelligence across all customer call transcripts to identify sentiment of every customer, key selling propositions, key product questions & concerns etc.

Provides intelligence on all of Ema’s Hubspot, allowing deep pipeline analysis using simple, natural language questions

Connects with calendar and email to auto follow-up with prospects at regular intervals with personalised emails

Creates pitch-decks ahead of every customer meeting tailored to the customer’s industry and scale utilising compendium of Ema’s existing decks

Provides instant access to all sales SOPs, product documentation, customer onboarding guides etc.

Ema’s Sales Assistant has transformed my entire sales process - I save about an hour before critical client calls typically spent on research and creating a starter deck. For each deal that progress to later stages, our pre-sales and sales team collectively saves around 4 hours on filling vendor questionnaires for clients. I have so much more time to focus on actual sales now.

Using Ema’s Generative Workflow Engine™ to easily build AI Employees

 to tackle our use-cases with Langchain. But after deploying some engineering bandwidth for a trial, we realised we were progressing at a painfully slow pace. We had to manually:

Build, optimize, tune, and maintain AI Agents for every use-case

Maintain data connectors with the SaaS platforms we were integrating with

Create and configure AI Agents with tools needed to perform actions for each SaaS platform (e.g. RazorpayX for applying for leave, Slack for communication, etc.)

This is when we understood the need for a platform that would speed up our pace of development. And soon, Ema’s 

™ was born, to solve the challenges we encountered in our quest to create simple, effective, enterprise-grade 

With Ema’s GWE, we brought over a dozen use-cases to life in just a couple of weeks—without writing a single line of code

As the builder of Ema’s internal AI Employees, our Employee Assistant and our Sales Assistant, here’s what I loved about using Generative Workflow Engine™:

No agonizing over communication and data-flow between agents

Not having to struggle with reading documentation of various SaaS API providers to figure out authentication, data syncing or taking actions

Not worrying about the impact of the latest LLM launches on our use-cases. 

EmaFusion™ intelligently combines the output of over 100+ LLMs, optimizing for accuracy, costs, and latency

—while keeping AI investments future-proof

The ability to focus purely on the business logic and end-user experience whilst abstracting away all of the technical tedium is a magical experience. 

As a business-user, being able to build out these Agentic AI use-cases I’d conceived, without any technical dependencies, was liberating and enabled delivering of impact in record time.

Learnings from our journey, and how to start with Agentic AI

 to streamline our internal productivity is a strategic priority, as we 

 to customers with similar goals—and at a much larger scale.

If Ema’s AI Employees can save a few hours each week for 100-people—while unlocking new efficiencies and making space for high-value, creative work—imagine what is possible for tens or hundreds of thousands of employees, with 

Some of Ema’s customers have been leading the way. 

Here are common lessons from our journeys, for other leaders looking to achieve transformation in their organizations:

Approach the problem customer-first, not tech-first:

 Involve the user in the solution process from Day 1; Leverage their insights and think impact-first when planning your solution instead of obsessing over the best LLM to use; Let solutions like 

™ handle the LLM-level optimisation for you

Take a conscious decision on building from scratch vs utilising an agentic business automation platform: 

Think through the benefits and drawbacks of each approach in your context before finalising an approach to automate your workflows; Ensure that you account for developer hours, ongoing maintenance and development delays when thinking through the RoI on building from scratch

Build incrementally starting with solving a single problem and build momentum with quick wins: 

Instead of launching the perfect AI Employee to automate all woes of a function, start by tackling a small yet thorny user problem and launch incremental features; Ramping up usage within the organisation takes time and multiple quick wins will help accelerate it

. Our entire journey so far has proven that in the new AI-first future, all data intelligence and manipulation can be done from the comfort of a native chat interface, without needing to navigate and click through mazes of SaaS platforms while paying for more seats than you need. Instead, opt for end-to-end agentic automation, delivered at a fraction of the cost.

We hope that our journey has given you the motivation to start experimenting within your enterprise today. If you wish to join the list of enterprises 10x-ing their productivity with Agentic AI, 

book a demo of Ema’s AI Employees today

No More Boring Work: How Ema’s Agentic AI Platform Supercharges our HR and Sales

—a breakthrough assistant powered by Agentic AI that puts enterprise knowledge, automation, and seamless execution at employees' fingertips. Unlike traditional chatbots that rely on keyword searches, Ema integrates with 200+ enterprise apps and systems, building a deep organizational graph and applying agentic reasoning to automate complex enterprise workflows.

Some of the world’s largest enterprises already use Ema as their employee assistant. By automating everyday tasks, Ema helps organizations save millions in support costs, increases employee satisfaction to over 80%, and leads the way for enterprise-wide Agentic AI adoption.

Ema has completely changed our expectations. Our previous vendor couldn’t even support multiple countries, but Ema not only understands complex policies—she reasons through them, takes actions and resolves complex employee issues end-to-end. 

– Chief Information Officer, Global Conglomerate (with 250,000+ employees)

The Problem: What’s Slowing Employees Down?

The workplace today is filled with inefficiencies. Employees spend more time fighting friction than doing their best work. Here’s what’s getting in their way:

 Key knowledge is buried in emails, wikis, and chat threads, forcing employees to waste time digging for answers.

 Simple tasks—like requesting IT support or approving expenses—require navigating cumbersome forms and workflows that slow everything down.

Chatbots and Gen AI co-pilots fall short:

 Most workplace AI tools rely on rigid keyword searches and predefined workflows, failing to adapt to real-world complexity.

Disconnected assistants causing overload:

 Every enterprise platform now comes with its own AI assistant, yet none of them seamlessly connect with all enterprise systems. Instead of reducing complexity, they have added to it.

How Ema Transforms the Employee Experience

Ema’s AI Employee Assistant helps organizations drive productivity and improve employee experience while significantly reducing costs. Here’s how:

 Ema builds a secure, private organizational knowledge graph, ingesting every document she has access to while seamlessly adapting to your company’s unique terminology, permission flows and org structure.

Unlike traditional search, Ema understands context, relationships, and policy nuances—not just keywords. She delivers accurate, context-aware answers, always backed by citations, so employees can trust the information they receive.

 Ema doesn’t just assist—she takes action. From filing expenses to troubleshooting IT issues, she executes workflows dynamically by integrating with enterprise apps, eliminating the need for rigid, hard-coded processes.

Beyond answering employee requests, Ema works alongside other AI employees to automate routine tasks that are done manually today. From onboarding new hires to competitor research and meeting summaries, Ema automates it all—becoming a productivity accelerator for your enterprise.

All posts that contain tag LLMs

The DeepSeek Effect: Impact of Foundation Model Commoditization on Agentic AI Adoption

Introducing EmaFusion™: Your one-stop solution for getting the most out of LLMs

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

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