Gathr 2026: Benefits, Features & Pricing

Most data processing tools lack the versatility and capabilities for GenAI use cases. But with Gathr.ai, we were able to readily build and manage multiple GenAI solutions. For instance, we’ve seamlessly leveraged data from Snowflake, CRM systems, and internal policy documents (brand guidelines, campaign SOP, and communication best practices), to create AI-driven, contextualized outreach campaigns.

We had data scattered across sources and systems. With Gathr.ai, we orchestrated end-to-end pipelines, extracting structured information, transforming it, and combining with other datasets. This helped us unify fragmented data and accelerate analytics across multiple business functions.

It provides the plateform to create the spark job very easily and we don't have to worry about monitoring and much validation.,

We’ve been able to seamlessly run robust ETL applications on Amazon EMR clusters, while monitoring detailed metrics for each task and instance.

Gathr.ai has allowed us to build and maintain production-grade pipelines that don’t require ongoing patchwork. It has improved reliability and reduced turnaround times when pushing updates or scaling usage.

We used to run separate workflows for text-heavy and tabular data. Gathr.ai helped us unify that effort, saving time and enabling richer use cases like customer 360 and support analytics.

It has enabled us to move away from patchwork scripting and manual maintenance. We now have more predictable, auditable pipelines feeding our analytics and AI workflows, with significantly less downtime.

Gathr has helped free us from time-consuming, tedious, manual plumbing work across data integration, pipeline orchestration and maintenance. We are now able to focus more on analytics and uncovering insights.

We’ve used it to scale data operations across business units without duplicating effort. It’s helped us roll out new pipelines and analytics use cases faster across the enterprise.