Gathr.ai

Pros: 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.

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

Overall: 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.

Pros: 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.

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

Overall: 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.

Pros: 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.

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

Overall: 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.

Pros: 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.

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

Overall: 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.

Pros: 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.

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

Overall: 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.

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

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

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

Pros: 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.

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

Overall: 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.

Pros: 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.

Cons: 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!

Overall: 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.

Pros: 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.

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

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

Pros: 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.

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

Overall: 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.

Pros: 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.

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

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

Pros: 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.

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

Overall: 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.

Pros: 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.

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

Overall: 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.

Pros: 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.

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

Overall: 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.

Pros: 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.

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

Overall: 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.

Pros: It allows us to work with structured data like ERP tables alongside unstructured sources like PDFs and logs in a single flow. That’s been a major enabler for our analytics, end-to-end.

Cons: Nothing significant. It integrates well with our diverse data landscape.

Overall: 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.

Pros: When we started to prep our data for GenAI use cases, we faced many challenges. We needed a solution that could help us work with our structured and unstructured data at scale. We used Gathr.ai on a pilot basis, and it just clicked. With Gathr.ai’s ready-to-use connectors, GenAI building blocks, and low-code/no-code UI, we have successfully built and scaled multiple GenAI applications.

Cons: On the technology front, Gathr.ai is easily one of the most advanced and versatile products in the market today. So, no major complaints there. The only minor issue they can work on is providing more native connectors so that we don’t have to build new connectors from scratch in certain cases.

Overall: 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.

Pros: Made it easy to ingest and process large volumes of structured and unstructured data—from databases like Oracle and HBase to docs and PDFs. It handled diverse data formats efficiently and streamlined transformations with a low-code interface and built-in operators.

Cons: While Gathr.ai offers a solid set of built-in connectors, one of our sources didn’t have native support. The team helped us build a custom connector quickly, so integration remained seamless.

Overall: 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.

Pros: Gathr.ai streamlines real-time ingestion, transformation, and metadata management—helping us maintain fresh, reliable data at all times. This has been essential for time-sensitive use cases like campaign analytics and operational alerts.

Cons: Nothing stands out—it’s been effective across our analytics and AI use cases.

Overall: It’s helped us eliminate fragmented workflows across marketing, sales, and operations. We’re able to ship analytics faster, with clearer definitions and a consistent understanding of data across teams.

Pros: Gathr.ai’s built-in function automatically generates metadata descriptions for all tables and their columns. What’s more, you can easily enter instructions for querying each table. This is super helpful!

Cons: Looking for some more in-depth demos/tutorials, will reach out to the support team and see how it goes.

Overall: Given the massive datasets we work with, entering metadata details manually for database objects was very time-consuming. We can now ingest and prepare our data much faster!