Machine learning
Computers learn from data without a program for every case. Algorithms find patterns and make predictions, and they improve through supervised and unsupervised learning.
Proven solutions tailored to your business
The multifaceted capabilities of AI
Emerge Growth Solutions uses core AI technologies to tailor solutions to a business. Machine learning reads the data. Deep learning takes on complex patterns. Vision AI interprets images and video. Generative AI creates new content from what it learns.
Deep learning
Multi-layered neural networks learn complex patterns and extract hierarchical features on their own. That supports accurate work such as image and speech recognition.
Vision AI
Machines interpret images and video. Tasks include object detection and facial recognition, in fields such as autonomous driving and medical imaging.
See an exampleGenerative AI
Creates new text, images, or music by learning patterns in data. In this comparison, traditional AI analyzes data that already exists.
Knowledge graph
A network of what the business knows
This model organizes large amounts of information into a structured network, so relationships that sit in separate systems can be found.
Semantic representation
Knowledge is stored as nodes and relationships, a network that follows how people already connect ideas.
Data integration
Separate sources link up as entities and attributes, instead of staying in their own lists.
Inference and reasoning
Graph algorithms infer new relationships and support question answering and recommendations.
Contextual understanding
The links supply context, so a response can stay relevant to the question in front of it.
Example graph
Nodes are types, not a live account. Hover, focus, or tap one to see what it connects to.
Select a node. Customers, deals, documents, and calls light up the relationships between them.
Emotion AI
Signals in voice, face, and text
This model recognizes and interprets human emotion from facial expressions, voice tone, and text.
Emotion recognition
Detects and classifies happiness, sadness, anger, fear, and surprise from facial expressions, voice patterns, and text.
Empathy and understanding
A response can follow the emotional state of the person, so the exchange feels more natural.
Personalized engagement
What comes back, and the content around it, can be tailored to that state.
Application versatility
Fits customer service chatbots, virtual assistants, and mental health support tools.
Example transcript
A sample of how text can be labeled. Not a customer recording, and not a measured score.
Sentiment: Anger
Predictive intelligence
History, read forward
This model studies historical data and patterns to forecast trends and outcomes.
Data analysis
Processes large datasets to find trends, correlations, and anomalies that may shape what happens next.
Predictive modeling
Builds statistical models from historical data and from what is happening now.
Scenario planning
Generates possible futures so a decision can be prepared for more than one of them.
Risk assessment
Identifies risks and their probabilities, early enough to manage them.
Illustrative example
These curves are a sketch of scenario planning. They are not a forecast, a client result, or a measured outcome. The axis has no units.
An example where the pattern in the history carries forward, with no new shock.
Our ecosystem
Runs where you already work
The solutions are built to integrate across environments, with the flexibility to adapt as that environment changes.
AWS is one of the cloud platforms these solutions are built to deploy on, and to scale with.
Cloud platforms
Compatible with Azure, AWS, and Google Cloud, so deployment and scale can follow the platform you choose.
On-premise
Integrates with infrastructure you already own, so that investment stays in the design.
Your ecosystem
Deploy where you prefer. The aim is performance, with as little disruption as possible.
Vision AI
Images and video, interpreted
Vision AI lets machines interpret images and video, for tasks such as object detection and facial recognition.
Images and video
Vision AI lets machines interpret still images and moving video.
Object detection
A model can mark the things in a frame. The boxes on this photograph are an example of that idea.
Facial recognition
The same family of models can work with faces, in fields such as autonomous driving and medical imaging.
Example detection
Boxes on an Emerge office photograph. They are a sketch of object detection, not output from a model, and not a client result.
emerge.ai
Interactive document intelligence
This model turns document work into a conversation you can have while the document is still open.
Real-time data extraction
Pulls data from a document as it is created or edited, so the insight is available while the work is open.
Intelligent question answering
Answers questions about the document in ordinary language.
Interactive analysis
A question can point at the relevant passage and explain it.
Contextual understanding
Natural language processing keeps the reading tied to the context of the document.
The product is emerge.ai.
Example interface
emerge.ai product photo. The note below is a separate example.
Sample note
The project note covers three open items: a service review, a pricing question, and a follow-up call. The note says a decision is expected next quarter.
Not a client document.
Prompt
Example answer
The note says a decision is expected next quarter. It does not say what was decided.
Related reading
Related insights
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Emerge headquarters, 412 Linden Ave, Rochester, NY