娇色导航

Keith Shaw: Hi everybody, welcome to DEMO, the show where companies come in and showcase their latest products and platforms. Today, I’m here with Mahesh Kumar, the Chief Marketing Officer at Acceldata. Welcome to the show, Mahesh. Mahesh Kumar: Happy to be here.

Keith: The reason I paused a little is because it sounds like “Excel Data.” But we’re not talking about spreadsheets — it’s “Accel” as in accelerating data. So: accelerating your data initiative. Tell me about the company and what you’re here to show today. Mahesh: Sure.

We are an agentic data management company. We primarily work with large enterprises — those with hundreds of data pipelines and tens of thousands of tables and databases.

We manage that complex environment, whether it’s in the cloud or on-premises, and help deliver trusted data for your AI and other initiatives.

Keith: So we’re talking about a lot of data here, right? Mahesh: Yes, definitely. Keith: Within the companies you’re helping, who is the most likely to use this? What kinds of roles benefit most?

Mahesh: It’s multiple roles, honestly. With our agentic approach, it could be a CDO or 娇色导航asking questions and getting actionable answers. Data engineers and operators also benefit by getting deep insights into their data environments.

Keith: And by “agentic data management,” you mean AI agents helping manage the data? Mahesh: Yes, absolutely. Keith: So what’s the big problem you're solving? Why should people care?

Mahesh: Let me give you an example. One of the largest financial institutions had a pipeline failure that disrupted credit score updates. They were sending credit and cash offers based on outdated data — very costly.

To prevent this, you need deep insight into your data, pipelines, infrastructure, users, and associated costs. Our platform gives you a 360-degree view and identifies problems proactively using AI and other techniques.

Keith: If companies weren’t using your platform, would they just miss these insights? Or would it take too long to find them? Mahesh: Exactly. We were discussing this earlier — what we do in minutes might take hours or even days for teams to do manually.

In some cases, the damage is already done before they even realize there's an issue. For instance, another customer operates in 120 countries, gathering data from 100 sources in each. They were facing complaints about stale data — leading to reputational risk.

The goal is to be proactive — catch issues early before they snowball into bigger problems. Keith: Just before we jump into the demo, do companies need to consolidate all their data into a single warehouse or lake to use this? Mahesh: Great question.

Our architecture is powered by a Cross-Lake Reasoning Engine, which deploys directly into your data environment — on-prem or in the cloud. The data never leaves your environment; only insights are sent to our cloud platform.

That’s why five of the top 12 banks, three of the top seven pharma companies, and the largest telcos and CPGs use us — our product is secure and proven.

Keith: All right, cool. Let’s jump into the demo and see what you’ve got. Mahesh: Awesome. So this is the initial welcome screen. It greets you by name and provides prompts based on your role.

For example, I could be a 娇色导航or CDO and ask: “What should I pay attention to today?”

You can make the prompts generic or specific. You can adjust the temperature setting for summaries vs. deep dives. It adapts to your role and request.

It searches across the customer’s data stack, knowledge bases, historical incidents, and preferences. It plans, reasons, and remembers — unlike traditional copilots. It also gives you a running commentary of what it's doing, so it doesn’t feel like you're waiting blindly.

We’re querying the Data Quality Agent here. It returns up-to-date quality insights, identifies drift, quality score concerns, and even recommends actions. Unlike past copilots, this agent can actually take action — such as applying new policies to fix issues. It bridges the gap between business and technical users.

Keith: Anything that reduces the number of meetings is a win for me. Mahesh: Absolutely! Keith: What’s next? More examples? Mahesh: Yes.

In the interest of time, some examples are pre-populated. For instance, a data engineer might ask: “Show me all pipelines that ran last week.” You not only see the pipelines — you get errors, warnings, and failure histories. The system also remembers how issues were previously resolved.

It’s a lot of intelligence built in — ready for you to tap into. Keith: Would this have been possible a few years ago? Mahesh: Not really. We started as a data observability company looking at data, pipelines, infrastructure, costs, and users. We accumulated a wealth of insights.

With the advent of LLMs, we can now automate that reasoning. Business users can engage directly without needing to navigate complex UIs. Our old product had a powerful but “too complex” UI. Now, that depth benefits agents — giving them richer, more accurate results.

Keith: Security, privacy, and governance are always top concerns. Are those handled? Mahesh: Absolutely. We support Resource-Based Access Management. That includes locking down access at the table or even column level.

For example, with PII data, companies used to have 15 meetings just to define and access it. Now, our system identifies and masks PII in 30 seconds. You can say, “Never show me PII,” and the system understands and enforces that. Keith: Very cool.

Where can people go to learn more? And do you offer a trial? Mahesh: Yes, we offer a “try-before-you-buy” model. Most proof-of-concepts last just a few weeks — even for large enterprises — thanks to how quickly we deploy. You can visit acceldata.io for more information.

Keith: Mahesh Kumar, thanks again for joining us. Mahesh: You're welcome. Thank you. Keith: That’s all the time we have for this week's episode. Be sure to like the video, subscribe to the channel, and leave your thoughts below. Join us every week for new episodes of DEMO.

I'm Keith Shaw — thanks for watching. ?