In this demo, we will showcase how to build an end-to-end ML-powered ‘data to outcome’ application for data at scale, using a drag and drop approach.
We will demonstrate this using a financial industry use case; but the challenges, solution, and benefits are applicable to all domains.
The customer, a leading bank in this case, needed to improve its credit card approval process. Though, the approvals were highly automated, in certain complex scenarios, a lot of manual analysis was required. This was proving to be risky; also, the process had opportunity costs, as high-quality leads were likely getting pushed to competitors due to approval delays.
In this data to outcome demo, we’ll show how to:
- COLLECT: Ingest batch & real-time data, from various sources such as CRM, call center, campaigns, and more
- TRANSFORM: Create pipeline to integrate, transform and prepare the data
- PREDICT: Infuse ML into the pipeline to predict which customers are most likely to take action
- RECOMMEND: Build supercharged dash, with real-time actionable insights and ML-powered recommendations
- ACT: Take actions, including calling the prospects, from the same dash
- MONITOR: Use the same integrated dash, to monitor campaign results in real-time
- AUTOMATE: Automate time-consuming credit card approval processes and workflows
And, accomplish everything, from a unified platform, without having to write a single line of code.
Gathr.ai’s capabilities on display in this demo
- Unified UX
- Drag and drop UI
- 300+ pre-built connectors
- 300+ pre-built transformations
- ML at scale
- Predictive analytics
- Insights delivery
- Process Automation
Data warehouse intelligence
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MySQL
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Starburst
Data pipelining
Data+AI fabric
GathrIQ : Data+AI Copilot
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