Ema is a platform that enables enterprises to deploy production-ready AI agents that automate complete business processes rather than just serving as assistants. Founded in 2023, the company serves Fortune 2000 clients across HR, IT, and finance domains with thousands of AI employees in production. The platform addresses the gap between reactive AI assistants and proactive autonomous agents by providing multi-step workflow orchestration, deep system integration, persistent state management, and built-in governance. Key deployments include Vipro with 240,000 employees globally using the platform for employee experience, and Article (an executive recruiting firm) which reduced time-to-hire by 67%. The platform achieves over 90% of invocations through automated triggers rather than manual user prompts, with 100% of AI employees directly modifying state in production systems.
Ema represents a comprehensive approach to deploying LLMs in production environments, focusing on what they term “AI employees” rather than assistants. The platform was founded in 2023 and has rapidly scaled to serve Fortune 2000 enterprises with thousands of AI agents in production. The core thesis is that production AI systems need to be proactive, capable of directly acting on real systems, maintaining persistent state across complex workflows, and operating under strict governance frameworks. This positions the platform as an end-to-end LLMOps solution rather than just a chatbot or co-pilot interface.
The platform’s customer base includes major enterprises like Vipro (240,000 employees using it for employee experience queries), Prime Therapeutics (healthcare prior authorization), Hospital of Special Surgery in NYC (appointment scheduling), and Article executive recruiting firm (67% reduction in time-to-hire). A critical statistic is that while builders create a 50/50 split between conversational and API-triggered agents, more than 90% of actual invocations are automated rather than user-initiated, demonstrating true production autonomy.
Ema’s platform operates on a multi-layered architecture designed for enterprise deployment flexibility. At the infrastructure layer, the platform is multi-cloud and can deploy on-premises across Azure, GCP, and other clouds. This design choice stems from customer requirements in regulated industries like finance and healthcare that demand air-gapped deployments where the system cannot communicate with external systems including the web. Data residency requirements are also built into this layer.
The LLM layer introduces what Ema calls “Emma Fusion,” which abstracts model selection away from end users. Rather than requiring enterprises to track model leaderboards and manually switch between models, Emma Fusion automatically routes tasks to appropriate models based on accuracy, cost, and latency requirements. This removes a significant operational burden from customers who would otherwise need to continuously evaluate and update model choices as the landscape evolves.
The builder platform layer contains several key components. Pre-trained domain-specific agents handle common tasks like data extraction from 500-page documents with deduplication and contradiction resolution. A generative workflow engine allows mixing of agentic and deterministic steps, recognizing that not all production workflows should use AI for every step. For example, provisioning access to a new system might follow strict deterministic logic that shouldn’t be subject to LLM variability. The platform includes an autopilot feature that can generate these deterministic workflow steps from natural language descriptions, making the platform accessible to non-technical users.
The integration layer supports multiple protocols including REST APIs, SFTP, SOAP, and Model Context Protocol (MCP). Interestingly, Ema developed its own tool protocol before MCP was released, which they claim is faster, more constrained, and simpler to set up. They now offer both options, with most builders starting with MCP for familiarity but potentially moving to Ema’s native protocol for performance. The platform maintains a catalog of pre-built integrations while allowing custom integration development.
Governance comes built-in rather than as an afterthought. Role-based access control (RBAC) is deeply integrated, with two types of authentication for integrations: shared service accounts for non-sensitive data and user-level OAuth for personalized access. When using user-level authentication, the system enforces that a user cannot request information they wouldn’t have access to directly, and maintains audit logs in the source system showing that specific actions were taken via an Ema agent on behalf of a particular user.
The platform supports three distinct builder models. First, Ema’s own team works with less technical customers, particularly in healthcare, to understand processes and deploy agents. Second, they partner with consulting firms like KPMG where partners train on Ema and deploy for their clients. Third, customers use the autopilot feature to build and maintain agents themselves. This multi-modal approach recognizes that different enterprises have different technical capabilities and preferences for ownership.
A critical aspect of Ema’s LLMOps approach is the distinction between three types of AI employees: chat-based agents that users converse with, dashboard agents that can be invoked via apps or APIs and are often single-shot with multiple input parameters, and orchestrator agents that coordinate multiple sub-agents. The orchestrator pattern is particularly important for user experience, as it provides a single interface for recruiters or other users who shouldn’t need to understand the underlying agent architecture.
The platform implements what amounts to an agentic harness with hundreds of tools across the product infrastructure, governance layers, builder layers, and UI layers. Users can bring their own tools and skills, with a key pattern being that enterprises enhance the base platform with domain-specific knowledge. A KPMG deployment might incorporate decades of finance knowledge while leveraging Ema’s platform capabilities for agent orchestration and management.
A particularly sophisticated aspect of the LLMOps implementation is the self-correction mechanism. When agents fail, they learn from those failures both at the individual user level and across the entire company deployment. This is especially important when enterprises bring their own custom APIs and tools. The first four or five users might encounter failures as the agent learns the nuances of these custom integrations, but subsequent users benefit from this accumulated knowledge. This network effect significantly improves agent stickiness and reliability over time.
The platform also supports autopilot-driven improvement for handling real-world drift. When customer questions or use cases shift in ways that weren’t predicted, the autopilot can analyze audit logs, identify improvement opportunities, and recommend changes with evaluation data showing that the changes won’t regress existing use cases. A human still approves deployment, maintaining a critical governance checkpoint while automating most of the improvement pipeline.
Evaluation is multi-layered and customizable based on user technical sophistication. Non-technical users might simply provide a year’s worth of historical tickets and specify desired performance levels across that dataset. More technical users can create formal evaluation beds with specific metrics and test cases. The platform supports evaluating entire AI employees as well as individual blocks within a workflow, enabling teams to isolate and improve specific integration layers like Apollo API interactions independent of the broader recruiting workflow.
The autopilot can automatically generate test data. In the recruiting workshop example, the system was asked to create test CVs and job descriptions and develop its own evaluation dataset. This generative evaluation approach reduces the burden on users to manually create comprehensive test suites.
The platform takes a nuanced approach to human-in-the-loop (HITL) checkpoints. Rather than inserting human approval at arbitrary points, Ema recommends identifying where human judgment is legally or ethically required. In regulated industries like healthcare and finance, AI cannot make clinical decisions or place market orders. In recruiting, while AI can source and screen candidates, final hiring decisions require human approval.
The key principle is to place human checkpoints at action boundaries rather than thinking boundaries. The AI employee should complete all analysis and preparation up to the point where state would change in a production system, then pause for human review. This maximizes the value AI provides while maintaining appropriate governance. The platform also trains agents to proactively request human input when encountering ambiguous situations rather than making low-confidence decisions autonomously.
Workflows can span multiple days with persistent state management. A procurement approval process might require ten different approvals from various stakeholders, and the agent maintains context throughout this extended process. The platform handles scheduling triggers, email monitoring, webhook responses, and conditional system monitoring to enable truly autonomous operation.
Security is implemented at multiple layers. PII and sensitive data obfuscation happens at source, meaning that if a user pastes an API key into a chat interface, it’s immediately obfuscated before being used downstream. Unlike some agentic platforms where users can paste API keys directly and the agent uses them, Ema enforces strict separation requiring proper credential management through the integration configuration interface.
For regulated industries, the platform provides pre-built compliance features. Healthcare deployments like Prime Therapeutics for prior authorization and Hospital of Special Surgery for scheduling operate under HIPAA requirements. The governance layer allows chief security officers to configure restrictions that automatically prevent builders from creating agents that would violate policies, removing the burden from individual developers to understand all compliance requirements.
The platform supports email domain whitelisting, custom license management for bring-your-own-model scenarios, and extensive RBAC configurations. API key management and external launch controls allow fine-grained control over how agents can be accessed and invoked.
Customer deployments range from dozens to hundreds of AI employees within a single tenant. The largest observed deployments have 300-400 distinct agents. This proliferation is intentional rather than problematic because it reflects proper organizational boundaries. Different teams own different agents based on their domain expertise, and breaking workflows into discrete AI employees enables better governance, maintenance delegation, and evaluation.
The platform supports sub-tenant creation and new deployment provisioning, particularly valuable for partners who deploy Ema to hundreds of their own customers. This multi-tenancy architecture enables partners like consulting firms to manage numerous client deployments from a central interface while maintaining proper isolation.
Workflow duration varies dramatically based on business process requirements. Some workflows complete in seconds, while others span multiple days waiting for human approvals or external system responses. The platform handles both synchronous and asynchronous patterns equally well.
Ema employs two primary pricing models. For specific use cases, they use outcome-based pricing tied to business value rather than technical consumption. Examples include pricing per employee experience ticket deflected from human agents, per sales proposal generated, or per customer support inquiry resolved. This aligns incentives with customer value rather than infrastructure costs.
For customers building custom solutions, usage-based pricing allows purchase of credits that can be applied to any use case. This flexibility supports experimentation and custom development without requiring upfront commitment to specific outcomes.
ROI measurement varies by use case type. For cost center applications like IT support or HR queries, ROI calculation is straightforward: fewer human hours required for the same work. For productivity applications like sales proposal generation, the approach combines speed measurements (one case showed 3x productivity improvement even for tenured employees) with output volume increases (20% more proposals from the same team size).
The platform’s integration capabilities extend beyond APIs to include browser automation when systems lack programmatic interfaces. The precedence order is direct API integration for performance and reliability, MCP for standardized tool access with slightly higher latency and cost, and browser automation as a fallback for systems without APIs or MCPs. This pragmatic approach ensures that even legacy systems can be incorporated into AI employee workflows.
The retrieval layer supports bringing in both structured data from systems like HubSpot, Salesforce, and ServiceNow, and unstructured data from Confluence, Notion, and spreadsheets. Frequent data synchronization keeps agents working with current information. For recruiting use cases, the platform includes collaborations with people data companies, allowing searches across large candidate databases without requiring separate API keys.
The autopilot feature represents a significant LLMOps innovation by making the entire platform accessible through natural language. Rather than requiring users to understand the UI, database schemas, or configuration files, builders can describe what they want in prose and the autopilot generates the integration configurations, workflow steps, test cases, and evaluation frameworks.
In the workshop demonstration, a single prompt requested creation of an Apollo integration with specific tools (search people, enrich people), two AI employees for sourcing and screening, an orchestrator to coordinate them, and automatic testing with generated CVs and job descriptions. The autopilot navigated the entire platform infrastructure, created the integration, configured authentication, built the workflows, and established evaluation criteria without manual UI interaction.
This accessibility has enabled what Ema calls “citizen developers” - business experts with zero coding experience who can now deploy sophisticated multi-agent workflows. A recruiting expert can build and maintain recruiting agents without technical training, as long as they deeply understand the recruiting process they’re automating.
The platform includes versioning capabilities critical for regulated industries where processes change on predictable schedules. Clinical policies for prior authorization change annually on specific dates, for example. In these cases, users can work with autopilot to create new versions, run evaluations comparing old and new versions, and deploy updates with confidence that existing functionality won’t regress.
For unpredictable drift in customer questions or process patterns, the autopilot monitoring of audit logs enables proactive identification of improvement opportunities. The system can recommend changes, generate evaluation data showing impact, and request human approval before deployment. This creates a semi-automated improvement loop that maintains quality while adapting to changing business conditions.
AI employees can be accessed through multiple channels including Slack, Microsoft Teams, custom dashboards, APIs, voice interfaces, and generative UI. Vipro uses Teams, Slack, and an SDK-based floating icon on internal websites. This channel flexibility means users interact with AI employees in their existing work contexts rather than adopting new tools.
The platform also supports custom app development with generative UI capabilities, deployable on separate URLs for specific use cases. While not covered in the workshop, this represents another deployment option for enterprises with specific interface requirements.
The hands-on workshop focused on building a complete recruiting workflow with three AI employees: a sourcing employee using Apollo to find candidates, a screening employee to evaluate candidate fit with job descriptions and pause for human approval before outreach, and an orchestrator allowing recruiters to interact with a single interface without understanding the underlying agent structure.
This architecture demonstrates key LLMOps principles: decomposition of complex workflows into specialized agents, integration with external systems (Apollo API), human-in-the-loop at appropriate decision points, and abstraction of complexity from end users through orchestration. The fact that non-technical workshop participants could build this in 30 minutes using autopilot demonstrates the platform’s accessibility while maintaining production-grade capabilities.
The recruiting use case also highlights the importance of proper scoping. The Ema methodology recommends discovering and decomposing jobs into tasks, identifying which should use AI versus deterministic logic versus remaining with humans, gathering past examples for evaluation, configuring integrations, breaking workflows into maintainable blocks, and identifying appropriate human checkpoints. This structured approach increases the likelihood of successful production deployment.
While the presentation is naturally promotional, several operational challenges emerged. Access control during the workshop initially failed, illustrating that even mature platforms face deployment complexity. The need for multiple authentication models, version management, cross-tenant isolation, and governance configuration suggests significant operational overhead for enterprise deployments.
The self-correction mechanism that learns from failures implies an initial period of lower reliability when custom integrations are first deployed. The claim that “four or five people use it, the next hundred people it’s going to work well” indicates customers must accept early adopter friction.
The multi-cloud, on-premises deployment capability adds operational complexity compared to SaaS-only offerings. While necessary for regulated industries, this increases the platform’s operational burden and likely requires significant DevOps expertise from customers or Ema’s team.
The distinction between the builder interface shown in workshops and the full admin/governance interface suggests that achieving production-ready deployments requires coordination between multiple roles (builders, admins, reviewers, onboarders) rather than a simple self-service model. The platform’s power comes with organizational complexity.
Ema represents a holistic approach to LLMOps that goes beyond typical RAG or chatbot implementations to provide true business process automation with AI. The platform’s key differentiators include the shift from reactive assistants to proactive agents, deep system integration with proper governance, persistent state management across complex workflows, multi-model orchestration through Emma Fusion, and accessibility through autopilot for non-technical builders. With thousands of AI employees in production across Fortune 2000 enterprises and 90%+ automated invocation rates, the platform demonstrates that autonomous AI agents can successfully handle production workloads in regulated industries when proper LLMOps infrastructure supports deployment, monitoring, evaluation, and continuous improvement.
Snorkel developed a specialized benchmark dataset for evaluating AI agents in insurance underwriting, leveraging their expert network of Chartered Property and Casualty Underwriters (CPCUs). The benchmark simulates an AI copilot that assists junior underwriters by reasoning over proprietary knowledge, using multiple tools including databases and underwriting guidelines, and engaging in multi-turn conversations. The evaluation revealed significant performance variations across frontier models (single digits to ~80% accuracy), with notable error modes including tool use failures (36% of conversations) and hallucinations from pretrained domain knowledge, particularly from OpenAI models which hallucinated non-existent insurance products 15-45% of the time.
Medable developed Agent Studio, a comprehensive platform for deploying AI agents in clinical trial operations to address the lengthy drug approval process that currently takes over 10 years. The platform enables both internal teams and customers to build configurable multi-agent systems that tackle problems like document classification in electronic trial master files and clinical research monitoring across multiple data systems. By taking a platform-first approach with support for model-agnostic agents, RAG knowledge integration, MCP connectors, workflow functionality, and robust evaluation frameworks, Medable has deployed multiple agentic applications that help clinical research associates process over 80,000 documents per year and monitor data across 13+ disparate systems, with the ambitious goal of reducing clinical trial timelines from 10 years to one year.
Stripe, processing approximately 1.3% of global GDP, has evolved from traditional ML-based fraud detection to deploying transformer-based foundation models for payments that process every transaction in under 100ms. The company built a domain-specific foundation model treating charges as tokens and behavior sequences as context windows, ingesting tens of billions of transactions to power fraud detection, improving card-testing detection from 59% to 97% accuracy for large merchants. Stripe also launched the Agentic Commerce Protocol (ACP) jointly with OpenAI to standardize how agents discover and purchase from merchant catalogs, complemented by internal AI adoption reaching 8,500 employees daily using LLM tools, with 65-70% of engineers using AI coding assistants and achieving significant productivity gains like reducing payment method integrations from 2 months to 2 weeks.
