Introducing TRAIN
A no-code machine teaching platform
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Leverages structured and unstructured data.
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Works even with minimal or noisy data.
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Zero data labeling efforts.
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Generates custom ontologies and knowledge graphs.
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Delivers use case-specific APIs for downstream integration.
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TRAIN
With TRAIN, you can jumpstart domain training with zero-labeled data and build AI models to represent your use case with greater accuracy, speed, and lower cost
Train Faster
Train any domain a minimum of 5x faster than using any pre-trained models like GPT3 and BeRT models, significantly driving speed to market.
Improve Quality
Parabole customers achieve >90% accuracy and reduced re-training.
Reduce Costs
PLATFORM OVERVIEW
One platform enables an end-to-end domain learning lifecycle
Knowledge Simulation
Knowledge Base
API’s & Integration
Corpus Builder
Auto Labelling
Auto Ontology
Security & Scale
Cloud Architecture
DATA PREPARATION
Corpus Builder
Starting with only a small set of sample documents representing the domain, Corpus Builder automatically expands the corpus by aggregating similar documents available within the enterprise repository or from public sources. Documents capturing the essence of your business operations such as product manuals, design documents, policies, regulations, procedures, systems is all that is required to learn your domain.
DATA PREPARATION
Auto Labeling
Auto labeler automatically labels large volumes of textual content using semantic and language modeling techniques. Unsupervised long-text annotation based on deep learning tags textual content at the document, paragraph, and sentence levels. This feature simplifies data preparation, generating tremendous efficiencies and speed to learning.
SEMANTIC MODELLING
Auto Ontology and Domain Modeling
Using the examples generated from earlier steps in the process, Auto Ontology automatically generates domain ontologies capturing the subject, the language, and the use case features representing organizational axioms, relationships, and their contexts together with descriptive concept networks that are compatible with standardized W3C Ontology structure. These models and concept-specific networks become the nerve center for all enterprise smart applications.
KNOWLEDGE SIMULATION
Reinforcement learning
In this stage, your SME ensures that the model has been fitted appropriately by validating the machine output until any errors have been minimized.
APIS & INTEGRATIONS
Connectors
Enable call projects from downstream applications via REST APIs or GRAPH QL.
ARCHITECTURE
Deploy in your private cloud.
Operate in an entirely private stack —only your products, models, and data will reside within your instance. Run your application on Amazon Web Services (AWS), Google Cloud, or Microsoft Azure’s highly secure infrastructure platforms.
DECISIONS AND INSIGHTS
Smart applications
Build use-case-specific workflow or end-user applications. This layer derives insights from the knowledge layer to solve complex data-driven analytics.
Related Resources
Automated Ontology. The future of AI
NLP: Brute forte or intelligent design
Why context is key in knowledge extraction?
