Gathr.ai Reviews

Summary: We were finding it tough to formulate an explainable, data-driven action plan for certain sales use cases. Gathr.ai’s data warehouse intelligence helped us test various hypotheses, validate the responses, and deliver data-backed recommendations.

Positive: Gathr.ai’s latest data warehouse intelligence capability has been a real game-changer! Our team is able to ask Redshift data sales related questions in natural language and get meaningful, data-driven answers. The insights it delivers are much deeper than what we were getting from other tools.

Negative: Just started using the data warehouse intelligence capability recently – but so far, it’s been smooth sailing.

Summary: Gathr.ai has been extremely helpful in retrieving and processing data from multiple sources. Their comprehensive library of connectors has saved our engineers a lot of time and supported our pipeline development initiatives.

Positive: Gathr.ai’s wide variety of built-in connectors have made it easy for us to unify data from our databases, SaaS applications, marketing tools, social media, etc.

Negative: So far we’ve had a smooth experience with the tool and have gotten timely responses from the support team as well.

Summary: Before using Gathr.ai, a huge amount of time and effort went into building data pipelines and orchestrating workflows. We can now manage the end-to-end process much more efficiently.

Positive: Using Gathr.ai, we were able to build pipelines and configure workflows in no time. My team quickly created complex data pipelines and automated multi-pipeline workflows using the tool’s drag-and-drop UI.

Negative: My team and I are quite happy with the platform so far. We’ve been using it for several months now.

Summary: Gathr.ai helped us aggregate data of thousands of influencers and analyze attributes like audience demographics, engagement metrics, and category fitment. We could confidently recommend the most suitable creators for our clients’ campaigns.

Positive: I really liked Gathr.ai’s no-code/low-code approach for analytics. It helped us generate data-driven insights from social media platforms and zero in on the best-fit influencers for our clients.

Negative: Gathr.ai is both affordable and highly capable. I can’t complain.

Summary: Gathr.ai offers different categories of validation functions for different data types. This helps us profile our data efficiently and maintain data quality for all downstream use cases.

Positive: I found Gathr.ai’s data pipelining product very useful for data quality management. We could easily define quality rules for different types of data and quickly perform necessary actions on faulty data.

Negative: My team and I are quite happy with the product so far. We’ve been using it for several months now.

Summary: We were able to process thousands of records per second, monitor suspicious user behavior, and configure real-time alerts for effective threat detection.

Positive: It helped us detect behavioral anomalies across data from thousands of applications in real time, using Artificial Intelligence/Machine Learning.

Negative: Working well so far , haven’t come across any major issue till date

Summary: Gathr.ai has empowered our business users with easy access to high-quality intelligence. They used to rely heavily on analysts to test and validate theories — especially ahead of leadership reviews or planning sessions. This dependency has reduced significantly.

Positive: Our business team can finally explore warehouse data freely and derive high quality insights – no need to repeatedly raise ad-hoc requests with analysts. They can converse with their data in plain language and get instant, high-quality answers.

Negative: So far it seems to be working fine. The results are far better than the generic, shallow insights we were getting from earlier tools.

Summary: Earlier, it used to take us months to build, test, and ship AI-driven analytics. We are now able to do this in weeks without worrying about pipeline maintenance or plumbing hassles.

Positive: With Gathr.ai, we can instantly and effortlessly feed our full data context to AI. We no longer spend hours hand-stitching data context and can deliver high quality AI-driven analytics products much faster.

Negative: We’ve used Gathr.ai’s data warehouse intelligence to build and operationalize three use cases last week. Quite happy with the time to market and results so far!

Summary: Saves hours of plumbing effort — just snaps into the existing stack. We can now focus on delivering high-impact analytics use cases.

Positive: We could easily connect Gathr.ai’s data warehouse intelligence product directly to Snowflake and start building AI-driven analytics products. No glue code or re-platforming was needed.

Negative: Their data warehouse intelligence works well overall, will explore it further in the coming months.

Summary: We needed a way to monitor fast-moving data across systems and react quickly. Their product has reduced the lag between data generation and action, improving how we handle everything from campaign performance to operational metrics.

Positive: Gathr.ai's data pipelining product helped us bring together data from multiple fragmented systems and convert it into live, decision-ready insights. It handles real-time processing well and made it easier for our teams to act on data as it arrives — not hours later.

Negative: Their product is solid overall. It would be helpful to have more advanced resources or walkthroughs focused on real-time analytics patterns.

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

Positive: Gathr.ai’s data pipelining product has helped our team seamlessly ingest petabyte-scale data and configure custom ETL applications. We could easily drag and drop components to build ETL applications – saving both time and effort.

Negative: There’s not much to dislike. Sometimes I wish they would update the documentation more frequently.

Summary: It’s helped us reduce the time and effort needed to operationalize pipelines. We’re able to deploy AI and analytics use cases faster, with fewer reworks and smoother transitions from dev to production.

Positive: It supports building and deploying reliable data and AI workflows that hold up in production. We’ve used it across multiple functions, and the outputs remain stable and consistent over time.

Negative: We haven’t run into issues moving workloads to production. It’s been stable and consistent.

Summary: We’ve moved multiple critical data and AI workflows to Gathr.ai. It’s helped reduce post-deployment firefighting and brought greater consistency in how we manage, monitor, and scale production jobs.

Positive: Gathr.ai allows us to build data and AI applications that are production-grade from day one. No throwaway work or migration needed when moving from exploration to deployment.

Negative: It’s met our production needs well. No major issues to note.

Summary: 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.

Positive: It handles diverse data modalities with ease, even when the source systems are inconsistent. That has helped us normalize and standardize inputs without creating more pipelines or duplicating work.

Negative: The platform has held up well under complex conditions. It’s been dependable across scenarios.

Summary: Gathr.ai has helped us move beyond traditional structured reporting to explore insights from qualitative sources. This has opened up new possibilities, especially in customer-facing functions.

Positive: Gathr.ai doesn’t treat unstructured data as an afterthought. It’s been easy to incorporate call transcripts, support tickets, and other text into our analysis pipelines.

Negative: Nothing worth noting—we’ve had success using it with a variety of data types.