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What's new in the Skypoint AI platform

We're excited to announce our newest updates and releases! The release notes of the Skypoint AI include improvements to existing features, resolved issues, and new features that improve your experience with the platform.

All production releases

These release versions contain an overview of the new features, improvements, and fixes in the most recent releases of Skypoint AI. For details about earlier releases, click Previous releases.

Release date
  • Our latest update introduces structured support for Combo Prompting, empowering users to access and leverage structured Lakehouse data seamlessly. Additionally, we've implemented enhanced error messaging for our Private Copilot, providing clear guidance during system failures or technical errors, enhancing user experience.
  • We've upgraded our data retrieval mechanism with Levenshtein distance-based matching, improving accuracy and latency. Moreover, users now have the flexibility to toggle AI features for Copilot directly within Skypoint AI Studio, enhancing usability and efficiency.
  • With support for Blueprint SFTP integration, users can automate data extraction, transformations, and loading processes, including folder level entity ingestion. This addition streamlines data management, providing users with enhanced capabilities for efficient data handling.
  • Improved markdown formatting capabilities and enhanced configurator features empower users to create polished and personalized Copilots effortlessly, elevating user experience and interaction.
  • The import enhancement feature enables users to remove unwanted entities from imports and Lakehouse, enhancing data organization and cleanliness for improved data management efficiency.
  • SherloQ, our advanced Text2SQL model, brings unparalleled accuracy and customization to SQL conversions, providing users with enhanced query understanding and data extraction capabilities.
  • With the addition of Broadspire and InfuseFlow connectors, we're expanding our platform's compatibility, enabling seamless integration with diverse data sources, enhancing data accessibility and flexibility for users.
  • Significant enhancements are coming to the Skypoint AI platform, providing users with more control and efficiency. Admins can now tailor Private Copilots with self-serve customization options, including access endpoints and branding elements. Enhanced conversational question grounding ensures smoother interactions, while improved retrieval accuracy for unstructured data with ease.
  • Two new updates and integrations streamline data processes. The PostgreSQL Connector enables seamless data ingestion from PostgreSQL databases, with effortless configuration and incremental ingestion support. Additionally, integration with PayNW's services via the PayNW Connector allows users to integrate online payroll and HR management platforms into their workflows.
  • Exciting updates to the Skypoint AI Developer Portal empower users to integrate Skypoint AI APIs into their interfaces, facilitating custom Copilot creation. By providing a more intuitive experience, developers can efficiently leverage the platform's capabilities to achieve their goals.
  • Enhancing the login process for Private Copilot and introducing combo prompt sources support exemplifies our commitment to enhancing user experience and transparency. These updates streamline access and provide users with clear, credible information, fostering trust and ease of use across the platform.
  • From the introduction of the SharePoint Connector for Self-Serve AI Config to enhancements in data import processes and improved connectors like QuickBooks Online - General Ledger, our platform is geared towards seamless data integration and efficiency. These enhancements empower users with streamlined workflows and deeper insights, driving productivity and success in data management endeavors
  • Skypoint AI introduces Enhanced problem-solving capability with a divide-and-conquer tactic, segmenting large complex queries into multiple small unstructured queries, boosting the efficiency of generated answers.
  • The studio now supports new connectors of Yardi and PointClickCare.
  • Skypoint AI introduces significant advancements including enhanced Short-Term Memory for improved conversational coherence, superior tabular data extraction capabilities for accuracy in unstructured data analysis, and the option for longer conversation names for better organization.
  • The platform now offers a Self-Service AI Configuration feature, allowing users to easily and intuitively configure unstructured data sources for AI model training, simplifying the process of enhancing AI capabilities.
  • For Skypoint Copilot, We've upgraded our response formatting for even clearer and more structured answers to your unstructured prompts.
  • Our Copilot has refreshed color palette which not only enhances the visual appeal but also provides a more comfortable and eye-friendly interface, making your interactions more enjoyable.
  • Skypoint Copilot's latest update revolutionizes user engagement with direct feedback options, seamless conversation sharing, precise timestamped conversation histories, and enhanced interaction during wait times.
  • Paragraph Formatting in Unstructured Responses .
  • Embrace the power of our new Usage Report Implementation, a comprehensive system for in-depth monitoring and analysis of your structured and unstructured data usage.
  • Unveiled the Skypoint CoPilot, a substantial upgrade aimed at refining user experience. This release features a more sophisticated response presentation, enhanced chat functionalities, and a comprehensive conversation history management system.
  • Revamped 'New Support Request' Section: An upgrade to the 'New Support Request' section within the Studio App focuses on making the feature more intuitive and user-friendly, streamlining the process for our users.
  • We've reengineered our system architecture using AstraTunnel, ensuring a more robust and efficient performance tailored to each client's unique needs.
  • Each tenant now benefits from a dedicated Astra DB provisioning, offering enhanced data management and security.
  • SQL Query Support has been provided for Skypoint AI responses for structured data. Structural Decomposition has been enabled for more context in Constructing SQL Queries. A tool has been built, which efficiently validate SQL queries from LLM.
  • Skypoint AI has been added in the product listings dropdown of "Help + Support" Section .
  • Chat model has been upgraded from 8k to 16k Token Limit to support more extensive reasoning traces.
  • Citation Support - Enhances reference inclusion within unstructured responses, improving content reliability and research integrity.
  • Optimized data model for select customers, thereby, improving the accuracy and performance of AI responses from structured sources.
  • Enhanced AI Performance - Model tuning for selected customers' data improves overall system performance.
  • Introduced support for data model context that enhances text2SQL precision.
  • Automated provisioning of key spaces in Skypoint AI's enablement workflow.
  • Using the MultiQuery retriever for unstructured querying, We can now produce a variety of queries based on different viewpoints for a single user input query. By creating diverse angles on the same query, we are able to surpass some constraints of distance-based retrieval, yielding a more comprehensive set of outcomes
  • The ADLS Gen2 V2 connector has been improved to facilitate incremental data ingestion with CSV files.
  • Enhancements to the user interface have been implemented in the Granular Scheduler, simplifying the configuration of Pipeline Schedules.
  • When reviewing records within the data unification process, we now have the capability to view the details of both matched and unmatched records, not just their respective counts.
  • The OS Langchain was forked, a toolkit developed to extract table schemas and annotations from Unity Catalog Tables using Databricks RestApi, then integrated into the Skypoint project, resulting in a substantial 40% improvement in structured data response precision.
  • Users can now adjust email verification time and send DSR verification reminder emails in Skypoint Studio, with the default time previously set at 24 hours.
  • Enabled the use of Service Principal for Databricks Lakehouse Provisioning.
  • Enhancing coding standards and implementing changes to enhance efficiency.
  • The Scheduler enhancements enable automated and orchestrated task execution in the pipeline.
  • WelcomeHome connector activities load to be support incremental run
  • DSR to support email for verification, and additional settings for expiry.
  • Beta version of Skypoint AI plugin released
  • Databricks runtime is upgraded to 12.2 LTS
  • dbt Core is upgraded to version v1.5
  • The Python version for all Azure functions is upgraded to version 3.10
  • Previous 3 versions of map now available for users to compare
  • Run history now displays Master Data type.
  • Run history option is now available for timelines.
  • Folder based entity ingestion using Azure Data Lake Storage Gen2 Connector
  • DBT Transformations
  • Type of refresh in resolve
  • Rule Match accuracy improvements
  • Higlight of Map attribute
  • Usage based report
  • MDM support for predictions
  • Improvements in CLV model
  • UKG import connector
  • Warning to user in map screen when import is updated/
  • Invitation email updates
  • Displaying datatypes on map screen and enabling entity wise run
  • SkyPointGPT can be enabled for usage on instance data
  • WelcomeHome import connector
  • External tables in Lakehouse accessible in all downstream processes/
  • View different master data on Dashboard.
  • Usage of the Lakehouse SQL-created tables.
  • Deidentification of PII and PHI data.
  • Added SkyPointGPT.
  • Domain selection in instance.
  • Allow the same priority for multiple attributes in ML match.
  • Added exceptions in the ML match.
  • Added Snowflake DLT integration.
  • Added Type in master data.
  • Autosuggestion in a SQL editor for Audience creation.
  • Data is available to the user for newly ingested tables or inherited tables.
  • Deletion of the instance/tenant will delete respective storage, Cosmos, Unity Catalog/Schema/Tables, and SQL warehouse.
  • Create audience from the prediction models.
  • Added topic modeling and filter on sentiment model view page.
  • View multiple master profiles.
  • Migration of hive_metastore to Unity Catalog.
  • Merge execution per master data.
  • Added Transformations.
  • Multiple Master data creation per instance.
  • Added RFM model view page.
  • Added SQL query editor in Audience.
  • Added DateTime option in Metrics.
  • External table creation in Lakehouse with the user-defined name.
  • RFM (Recency, Frequency, Monetary value) model is added under Predict > Built-In.
  • Added search field in the dropdown to select the desired value.
  • Added multiple timeline journeys per instance.
  • Setup instructions for import connectors.
  • Added profile filter ("Email approachable" or "Phone approachable") to create a quick audience.
  • Real-time status update on Audience page.
  • Usage of tables across all downstream processes.
  • Real-time status updates for Exports, Audience, and Prediction models.
  • Bulk Processing of Data Subject Request (DSR).
  • Data ingestion through Delta Live Tables (DLT).
  • Convert Pandas data frame into Spark data frames.
  • Import connectors redesign.
  • Segregating data in Bronze, Silver, and Gold.
  • Prediction model is migrated from Azure ML to Databricks using ML flow.
  • Net Promoter Score (NPS) calculation in Business Metrics.
  • Auto provisioning of Lakehouse SQL during the instance creation.
  • Run history shows all stages of ingestion.
  • Removal of CDM dependency for all downstream processes to improve pipeline performance and stability.
  • View profile card from Audience.
  • Timeline creation from Audience.
  • The storage layer is restructured and organized into three layers named Bronze, Silver, and Gold.
  • Replaced the Standard cluster with an Automated cluster to support optimized autoscaling.
  • Added Refresh personal access token in SQL Access to configure the expiry time for an access token.
  • Support Change Data Capture (CDC) in Lakehouse SQL.
  • Added reason for the discarded entities in map tables under lakehouse.
  • Added access token to integrate with Visualization tools through Lakehouse SQL.
  • Refresh Hive metastore is added for syncing new entities in Lakehouse SQL.
  • Skypoint Lakehouse made available for bidirectional integration with third party tools like Fivetran, Dbt, Power BI, Tableau, etc.