No-code Customer 360 with data
THE USE CASES
Customer 360 to monitor customer health
Customer 360 requires a single, unified view of customer data across Sales, Marketing, Product, Support, and Contact repository silos as well as the aggregation, and transformation of this data for a “golden record” that is timely, of high quality, and answers the diverse needs of various departments across the organization.
- Intelligently summarize your customer activities using an LLM
- Build a customer health score from sales, marketing, telemetry, and support data
Question and Answering System For Your Private Data
Building a Q&A system with a privately hosted LLM empowers organizations to harness the transformative power of their internal data. By asking questions and extracting meaningful insights, businesses can make informed decisions and unlock new opportunities. Seamless integrate advanced ML technologies, such as LLMs, to ask questions of private data wherever it may be stored.
- Pre-process data to get it to a shape where it can be used by LLMs using Conversational Data Transformation.
- Operationalize creating chunks — stored in an operational database, and embeddings – stored in a vector database.
- Privately host and fine-tune any commercially available LLM (like Dolly) to ensure the security and privacy of your data are never compromised.
Relational to JSON: Overcome barriers to application modernization
Enterprises around the globe are focused on modernizing their applications. Many of these projects struggle to convert their traditional structured relational database schemas to more modern, flexible, scalable JSON schemas.
Convert tabular data from a relational database into a JSON document stored in a NoSQL database to accommodate flexible schemas, enabling fast data retrieval and facilitating scalability.
- Define transformations of any complexity
- “Uni / Bi-directional”
No-code Apache Spark pipelines
Visually build Apache Spark jobs to run on data stored in your relational or NoSQL database, data lake, Databricks Lakehouse, or Snowflake. Using Dataflows, aka composable AI-enabled workflows, to define a set of steps that can be combined in a desired sequence.
- Create vector embeddings for an LLM
- Transform JSON data to make it analytics-ready