Acodis Features

Provides a wide range of possible data connections, including cloud applications, on-premise databases, and big data distributions, among others

Allows businesses to easily connect to any data source

Offers API connections for cloud-based applications and data sources

Permits profiling of data sets for increased organization, both by users and machine learning

Indexes metadata descriptions for easier searching and enhanced insights

Tools to (re)structure data in a manner that enables quick and accurate insight extraction

Allows self-service joining of tables

Provides the ability to combine data sources into one data set

Allows users and administrators to easily clean data to maintain quality and integrity

Offers collaborative functionality for sharing queries and data sets

Ensures user access management, data lineage, and data encryption

Provides a wide range of possible integrations, including analytics, data integration, master data management, and data science tools

Allows businesses to easily integrate with analytics, data integration, master data management, and data science tools

Operationalizes data workflows to easily scale repeatable preparation needs

Pull any required data from a variety of sources, including email, web pages, PDFs, and other documents.

Organize extracted data into a more easily digestible structure.

Amass extracted data in a variety of data formats like spreadsheets and .csv.

Clean extracted data by removing duplicates, clearing excess characters, grouping by characteristic, and more.

Stores data in cloud storage for access at any point.

Gives user a metric to determine the quality of data labelers, based on consistency scores, domain knowledge, dynamic ground truth, and more.

Ensures that labeling tasks are accurate through consensus, review, anomaly detection, and more.

Ensures the data is of a high quality as compared to benchmark.

Gives user the ability to review and edit labels.

Uses models to predict the correct label for a given input (image, video, audio, text, etc.).

Automatically route input to the optimal labeler or labeling service based on predicted speed and cost.

Has the ability to place imaginary boxes or polygons around objects or pixels in an image.

has the ability to detect objects within images.

Track unique object IDs across multiple video frames

Supports a range of different types of images (satelite, thermal cameras, etc.)

Gives user the ability to extract entities from text (such as locations and names).

Gives user the ability to tag text based on its sentiment.

Gives user the ability to label and verify text data in an image.

Allows the user to transcribe audio.

Gives user the ability to label emotions in recorded audio.

Supports Microsoft Windows operating system

Supports macOS operating system

Supports Linux operating system

Supports a variety of file types (E.g. DOC, XLS, PPT, PDF, JPG, PS, TIFF, etc.)

Processes data from unstructured and semistructured data sources

Provides AI and machine learning tools to accelerate data analysis

Provides image enhancement before processing (E.g. rotation, image cropping, removing noise, enhancing contrast, etc.)

Integrates with other applications to support document storage, document management, process automation and/or workflow management

Supports the conversion of uploaded documents to other file types

Supports operations on both Windows and Mac

Provides the ability to import data from a variety of sources and in multiple data formats.

Consistently maintains, updates, and tests data sources to ensure quality.

Allows users to analyze data easily without the need to code.

Ensures privacy and security of customer data.

Visualizes text data through charts and graphs.

Automates back-end technical manual processes.

Identifies entities such as organization, person name, location, etc

Extracts keyphrases to determine patterns and themes within text.

Automatically identifies and organizes text based on topic or subject matter.

Utilizes sentiment analysis to capture user feedback.

Identifies the language in which text was written in.

Provides the ability to identify syntax and parts of speech.

Allow capabilities to be customized (key-phrase, topics, sentiment, named entity) by adding keywords or exceptions.

Allow user to add custom functions to Analysis capabilities

User created models can be used as features/pre-built in other models

Pull any required data from a variety of sources, including email, web pages, PDFs, and other documents.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Allows the user to process documents in various formats such as PDF, DOCX, and JPEG.

Gives user the ability to convert different types of documents into machine-readable text using Optical Character Recognition (OCR).

Enables the user to identify and extract specific entities like names, dates, and addresses from documents.

Gives user the ability to categorize sections of text within the document based on preset criteria.

Provides the user with the capability to validate the extracted data for accuracy.

Enables the user to process multiple documents in a single batch operation.

Gives user the ability to connect to automation technology, such as robotic process automation (RPA).

Enables users to search through the content of processed documents easily.

Allows the user to track changes and manage different versions of a document.

Gives user the ability to train custom machine learning models for specific data extraction tasks.

Provides the user with the ability to analyze the sentiment expressed in the textual content of the document.

Allows the system to understand the context of words and phrases for better data extraction and classification.

Provides the user with the ability to integrate the IDP system into other applications through APIs.

Assures the user that the system complies with industry regulations like GDPR.

Ensures that all data processed is encrypted for security.

Supports integration with multiple data sources for seamless data input.

Enhances output accuracy and speed through efficient ingestion and processing of training data.

Generates actionable insights by applying learned patterns to key issues.

Continuously improves and adapts to new data using specified algorithms.

Capability to perform complex tasks without constant human input