Gathr.ai Reviews

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.

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.

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

Gathr.ai is helping my team save a lot of engineering effort. By using NL to perform complex data transformation tasks, we’ve been able to expedite pipeline building for multiple downstream use cases.

Love that I can use simple, natural language prompts to search for relevant data assets, transform data, and even build pipelines.

Not really a lot of negatives so far. Been using Gathr.ai on a daily basis for 2+ years and still going strong.

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

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.

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

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.

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.

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

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.

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.

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

With fewer pipeline failures and shorter debugging cycles, we can spend more time innovating and tackling certain use cases we didn’t have time for earlier. Also, reduces manual effort of creating data pipelines.

Before working with Gathr.ai, we had every kind of data issue—drift, late events. Gathr.ai made our pipelines resilient without overengineering.
Easy to create data engineering pipelines.

No deal breakers yet. There's could be more learning material available online though.
There must be learning videos or sessions on YouTube or on internal company's learning portal, so it may be easier to learn Gathr.ai for new user in a team.
Session connection can be made more faster without frequent disruption.

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.

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.

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

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.

Gathr’s data integration capabilities are outstanding. Three things that stood out for me and my team: #1 Handles both batch and streaming data at any scale. #2 Tracks data lineage in real-time, with visibility into each transformation step. #3 Offers an easy-to-use, drag-and-drop UI with built-in operators for smooth data preparation.

Even though the ingestion and integration process are smooth, setting up custom connectors requires a bit of additional effort. Also, if Gathr provided more templates, it would help analysts develop applications faster. Although, both are minor issues and not deal breakers!

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.

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.

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

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.

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.

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

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’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.

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

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.

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.

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

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.

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.

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

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.

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.

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.

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.

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.

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

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

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

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

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 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.

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

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.

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.

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.

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.

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.

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

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.

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.

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