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Ema

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About

Ema is a universal AI employee platform designed to transform business operations by deploying sophisticated AI agents. Unlike simple chatbots, Ema functions as a dynamic team member that learns and adapts to specific enterprise roles. It utilizes a proprietary Generative Workflow Engine to automate complex business processes, moving beyond rigid automation to fluid, conversational interactions that handle both structured and unstructured data. This allows organizations to scale their workforce quickly without the overhead of traditional hiring. The technology is powered by EmaFusion, a proprietary 2T+ parameter model that intelligently blends various public and private LLMs to ensure high accuracy at a lower cost. It integrates with hundreds of enterprise applications, allowing for quick deployment of pre-built agents like AI SDRs or compliance analysts. The platform emphasizes trust and security, featuring a data governance layer that redacts sensitive information before it reaches public models, alongside top-tier encryption and compliance with global standards. Ema is ideal for large-scale enterprises looking to optimize roles in customer experience, employee support, and finance operations. Specific personas include Proposal Managers who need to automate document generation, Compliance Analysts monitoring regulatory shifts, and Pharmacist Assistants handling medical documentation. By delegating mundane, repetitive tasks to AI agents, human talent can focus on high-impact strategic work, effectively multiplying the workforce's output. The system is designed to be future-proof, meaning it adapts as business needs change. What sets Ema apart is its agentic approach and architecture. The platform continuously incorporates new models to avoid vendor lock-in and over-reliance on a single technology stack. Its ability to conversationally activate workflows and reason over vast datasets allows it to solve problems that traditional enterprise software cannot, ushering in a more flexible and intelligent era of business automation. This approach ensures that the AI remains accurate, cost-effective, and highly specialized for the unique needs of every business.

Pros & Cons

Redacts sensitive data before passing to public LLMs for enhanced privacy.

Uses EmaFusion to blend the best public and private models for accuracy.

Integrates with hundreds of apps for seamless workflow automation.

Supports complex reasoning over both structured and unstructured data.

Avoids vendor lock-in by constantly adding new AI models.

Requires a consultation/hire process rather than a self-serve sign-up.

Documentation suggests a primary focus on large enterprise implementations.

Limited transparency on specific pricing tiers without direct contact.

Full capabilities depend on the quality of existing enterprise data.

Use Cases

Sales teams can deploy AI SDRs to automate personalized outreach and response handling across multiple communication channels.

Proposal Managers can use Ema to automate the generation of complex documents and responses to RFPs using internal data.

Compliance Analysts can leverage AI agents to reason over vast datasets and monitor regulatory changes more efficiently.

Customer Support departments can implement AI agents to resolve tickets and manage common queries through conversational interfaces.

Platform
Web
Task
scheduling automation

Features

document generation

generative workflow engine™

pre-built ai agents

multi-model architecture

200+ app integrations

conversational activation

data redaction for privacy

emafusion™ model blending

FAQs

How does Ema ensure data security for enterprises?

Ema utilizes a dedicated data governance layer that redacts sensitive information before it is passed to public large language models. It also provides top-tier encryption and the option for customizable private models to meet strict compliance standards.

What is the technology behind Ema's accuracy?

Accuracy is driven by EmaFusion, a proprietary model with over 2 trillion parameters that intelligently blends multiple public and private models. This architecture allows the platform to maintain high performance while keeping operational costs to a minimum.

How difficult is it to deploy a new AI employee?

Deployment is simplified through the Generative Workflow Engine, which allows users to conversationally activate AI employees for complex workflows. The platform comes pre-integrated with hundreds of common enterprise applications, making configuration fast and accessible.

Can Ema handle both structured and unstructured data?

Yes, Ema’s AI employees are designed to reason over vast amounts of both structured and unstructured data. This capability allows the agents to handle complex business processes that require nuanced understanding rather than simple automation.

Pricing Plans

Enterprise
Unknown Price

Custom AI employee deployment

Access to EmaFusion™ model

Generative Workflow Engine™

Data governance & redaction

Private model options

Hundreds of app integrations

Enterprise-grade encryption

Pre-built AI agent library

Job Opportunities

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Ema

Software Engineer, Full-Stack

Boost enterprise productivity with universal AI employees that automate complex workflows across customer experience, finance, and internal operations in minutes.

engineeringonsiteSan Francisco Bay Area, US
$135,000 - $300,000
full-time

Benefits:

  • variable compensation

  • equity

  • benefits

Education Requirements:

  • Bachelor's degree in Computer Science, Web Development, or a related field, or equivalent work experience.

Experience Requirements:

  • A minimum of 4 years of professional experience in full-stack development

  • Focus on front-end engineering and API integration

  • Strong proficiency in HTML, CSS, and JavaScript

  • Extensive experience in React

  • Experience with gRPC for client-server communication

Other Requirements:

  • Excellent problem-solving and analytical skills

  • Strong communication and collaboration skills

  • Ability to work effectively both independently and as part of a team

Responsibilities:

  • Design, develop, and implement exceptional user interfaces

  • Collaborate closely with product managers, UX designers, and other stakeholders

  • Develop and maintain API integrations

  • Contribute to back-end development

  • Build, test, and deploy full-stack web applications

Show more details

Software Engineer, Machine Learning

Boost enterprise productivity with universal AI employees that automate complex workflows across customer experience, finance, and internal operations in minutes.

Benefits:

  • variable compensation

  • equity

  • benefits

Education Requirements:

  • Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.

Experience Requirements:

  • At least 2 years of industry experience in building and deploying production-level machine learning models

  • Deep understanding and practical experience with NLP techniques and frameworks

  • Deep understanding of any of retrieval, ranking, reinforcement learning, and agent-based systems

  • Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch

  • Excellent skills in data processing (SQL, ETL, data warehousing)

Other Requirements:

  • Excellent problem-solving skills

  • Attention to detail

  • Strong capacity for logical thinking

  • Ability to work collaboratively in a fast-paced environment

Responsibilities:

  • Conceptualize, develop, and deploy machine learning models

  • Implement advanced machine learning algorithms

  • Process and analyze large, complex datasets

  • Work across the complete lifecycle of ML model development

  • Implement A/B testing and other statistical methods

Show more details

Software Engineer, Backend

Boost enterprise productivity with universal AI employees that automate complex workflows across customer experience, finance, and internal operations in minutes.

Benefits:

  • variable compensation

  • equity

  • benefits

Education Requirements:

  • Bachelor's degree in Computer Science or related field.

Experience Requirements:

  • At least 4 years of relevant work experience.

  • Solid background in algorithms, data structures, and object-oriented programming

  • Experience in back-end development using languages like Python and Go

  • Knowledge of database technologies like PostgreSQL, Elastic, Redis.

  • Knowledge of designing and building API platforms and integration systems.

Other Requirements:

  • Strong problem-solving skills

  • Attention to detail

  • Ability to work independently and as part of a team

  • Experience working with global teams

Responsibilities:

  • Develop and maintain enterprise software applications

  • Write clean, efficient, and maintainable code

  • Build scalable and reliable back-end systems

  • Develop and maintain APIs using REST and gRPC

  • Build and maintain scalable and extensible data schemas

Show more details

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