Archive360 Transforms From Archive Tool To AI Entry Point: CEO

‘The first evolution [of Archive360] was adding analytics, feeding data into some tools like [Microsoft] Power BI or Snowflake or Databricks so customers can do more analytics on archived data. The next step with this platform is really to become more of a data governance platform to not just feed analytics engines, but also AI models as well,’ says Archive360 CEO Jerry Caviston.

Archive360 began as a data migration services company but quickly transformed into a specialized archive platform provider. Identifying gaps in the market—such as lack of cloud-native offerings, vendor lock-in, and insufficient security—the company set out to develop a cloud-based, single-tenant SaaS platform that prioritizes customer data ownership and zero-trust security.

This unique approach enables granular data control and indexing, positioning Archive360 as an innovator in the field and opening the door to more advanced operations, said Jerry Caviston, CEO of the New York-based company.

“The first evolution was adding analytics, feeding data into some tools like [Microsoft] Power BI or Snowflake or Databricks so customers can do more analytics on archived data,” Caviston said. “The next step with this platform is really to become more of a data governance platform to not just feed analytics engines, but also AI models as well.”

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Caviston said that Archive360 remains a standalone archiving technology developer in an industry where many data protection vendors offer broad capabilities, including archiving. Archive360 positions itself as more than an archiving provider, describing its role as a comprehensive data governance platform, he said.

“We’re seeing this really unique opportunity in the market where you have some established large vendors that do a lot of things, but they’re missing some of these really key components, like governance or granular level access control or the smarts to actually manage this data,” he said

Internally, Archive360 has embraced AI by standardizing on enterprise tools such as ChatGPT, establishing a sovereign LLM for its own data estate, and implementing comprehensive AI training for all employees, Caviston said.

“Externally, we’re about to announce we’re releasing in Q1 our first agent,” he said. “We’re not going to be an agent development company. But for some of our customers, we manage tens of petabytes of data for them, and now we’re building an agent to help customers find some value out of this data.”

There’s a lot going on at Archive360 and the archiving industry. To get up to date, read CRN’s complete conversation with Caviston, which has been lightly edited for clarity.

What is Archive360? How do you define the company?

Our definition is evolving. We started as a services company migrating data and quickly evolved into an archive platform. We built an archive platform based on what we saw were some deficiencies in the current market. Offerings weren’t cloud-native. There was a lot of vendor lock-in. They were, we felt, not very secure. So we developed a very unique platform. It’s a dedicated SaaS. It’s a single instance. It’s a single-tenant SaaS environment. There are several opportunities for more customer value. Customers own the data. They’re not locked in. We have a zero-trust model from a security perspective. We built this platform differently from how anybody else did at the time. … It’s really unique in how we index and control the data. We can do more things with this data than when we started. The first evolution was adding analytics, feeding data into some tools like [Microsoft] Power BI or Snowflake or Databricks so customers can do more analytics on archived data. The next step with this platform is really to become more of a data governance platform to not just feed analytics engines, but also AI models as well.

Why does the industry need a standalone archiving company? Data protection companies, for instance, are doing data protection, management, migration, and archiving. These vendors offer a wider range of capabilities encompassing more than archiving. So why a standalone archiving company?

That’s a great observation. There’s a couple of trends that we are trying to get in front of. We’re really just not a standalone archiving company. We’re really more of a data governance platform. We’ve seen a couple trends happening right now. There are two words that I probably overuse, so full disclosure. One is ‘convergence.’ We’re seeing a lot of what used to be very disparate, separate areas that are converging. A good example is Veeam’s recent acquisition of Securiti. You got a historical data backup and data resiliency company buying a data governance and AI company to extend their portfolio and functionality. And then from an emerging perspective, folks are struggling. The sands are shifting so quickly on the AI front. So we’re seeing we’re heavily embracing AI TRiSM (artificial intelligent trust, risk, and security management. Data for your AI needs to be governed in ways that businesses haven’t been able to do or had the requirements to do before. We’re seeing this really unique opportunity in the market where you have some established large vendors that do a lot of things, but they’re missing some of these really key components, like governance or granular level access control or the smarts to actually manage this data. We’re much more, I use the term molecular level, but more object level versus the way others do it. And like I said, the Veeam example really paints a good picture at a broad level of what we’re seeing happening in the industry right now.

So we are really expanding. We’re part of the overall ecosystem, but we are expanding not just as an archive, but as a data governance platform. And we’re seeing more vendors. I’ve been here now going on seven years. It used to be that customers would put data in an archive after three or four years. We’re now seeing what Gartner calls ‘archive first.’ Customers know they’ll keep this data, and the most recent data is the most valuable. So we’re seeing a lot of the data come in more from live feeds via tools like Guidewire. We’re actually seeing more data in the system sooner so they can control it under a single repository. They don’t have all these siloed bits of data throughout the organization. Tt least among our installed base, folks are recognizing the value of combining more recent data with what has traditionally been known as archived data.

If there’s this move towards that convergence, when can we expect Archive360 to bring in data protection?

I don’t know if we’re going to go in the data protection space per se. But you will see in some of our roadmap a lot more stuff around the metadata, the data management, the governance of that data both from a content perspective as well as a metadata perspective, the ability to do vectorization indexing to feed the analytics and AI engines out there, those models we’re seeing customers already developing. And we believe we’re going to see agentic models really take off. People are still trying to understand where’s the real value. But we haven’t seen hype like AI in quite some time. But we’re starting to see very specific tasks in other areas within an organization that can leverage it for real benefits. And I think the industry overall is still very nascent as to what’s going to come out of all of this. So I think you’re going to see us expand to other areas, not necessarily data protection per se as it’s known today, but across the data management ecosystem, specifically with data governance and associated things.

How has AI impacted Archive360 both internally and externally?

I think it’s forced us, like a lot of companies, to look at things much differently and much more quickly make AI decisions. Internally, we have a lot of initiatives going on. We’ve standardized on the ChatGPT enterprise tool internally. We have our sovereign LLM using that, sovereign being our own data estate. We have an internal training academy already in place, and we have AI training for all of our internal employees. We’re seeing the benefits early on. We use it with our chat bot and on our lead gen side on the front of the house. But we also use it on the support side, and the engineering, the DevOps guys are also incorporating into it. So internally, we are bracing it very much. Compared to some of the folks I talked to and my peers, I think we might even be a bit ahead of a lot of what other folks are doing with it. We have our policies and governance. We have our own challenges on making sure what employees can and cannot do and what they can and cannot put into these models.

Externally, we’re about to announce we’re releasing in Q1 our first agent. We’re not going to be an agent development company. But for some of our customers, we manage tens of petabytes of data for them, and now we’re building an agent to help customers find some value out of this data. Our first agents will be an AI investigator. If you look at the e-discovery space, we are a leader in the [Gartner] Magic Quadrant for digital communications and governance. In that area, a number of customers that have captured all their modern communications in our platform, in this large repository, and they’re used to doing traditional e-discovery. They’ve applied some logic and machine learning. But now by incorporating AI, building an agent around that, you can reduce what a paralegal or maybe an attorney might do in a search, creating a case, exporting this file. You can consolidate that to where you just need the individual to review and make certain you got what you needed. That’s our first foray into actually leveraging this in a real proof and a real go-to-market. You’ll see that coming out in Q1.

Who owns Archive360?

A real quick history. We were completely self-funded for the first 12 years of existence. [In 2001], we took private equity company Leeds Equity Partners as a majority shareholder. The rest is employee-owned or incorporated. And we’ve been growing tremendously. We really did that specifically so we could hire above the curve and invest in the company. Since then, we’ve grown almost 340 percent or something in that magnitude. We are growing faster than the market. I think it’s because, and I could be controversial at times, but I think in the traditional SaaS multi-tenant model you’re going to see a lot of changes over the next couple years. The model is not well suited to AI. For example, if all of your data is tied up in some third-party vendor’s multi-tenant solution, being able to access that and run that against your AI is going to pose some challenges. What we’ve seen early on, maybe some companies will address that. We have a single-tenant model. Your data is already sitting in the cloud where your AI model, especially if you’re using Azure or Copilot or ChatGPT. It’s a much more seamless methodology to be able to leverage that than you will see in some of the traditional legacy SaaS-type environments.

Archive360 is a single-tenant application, not multi-tenant? What does that do?

Yes, single-tenant. The end user controls their data. They control the encryption keys. There’s no lock-in. In this industry, with almost everyone, if not everyone, of our competitors, if you want to move out of their system, you have to pay them extraction fees for your data. But in our model, it’s your data. You own the data. You control your data. You manage your data. We manage it for you, but it’s your data.

Is Archive360 profitable?

Good news. We’ve been profitable since day one.

Any plans for an IPO?

I have no idea what the future is going to hold for us. Like I said, we’re seeing convergence. I know some people with their egos want to go IPO. And you know, I personally wouldn’t want to deal with the stress of managing a publicly traded company. So we’ll see where it takes us. We’re just focused now on innovating. I think we’ve got some really interesting stuff compared to what folks think of traditionally what an archive can do. We’ve got some really innovative stuff on that front. We’ve got some really happy customers and some great channel partners that are bringing this to market. In the short term, that’s what we focus on, keeping all of them happy.

How much of your business is indirect versus direct?

A majority of our business is indirect. The channel is our lifeline. If you want a number, about 80 percent of our business comes through the channel. We have a direct sales force, but they’re really more facilitating the channel. And the reason the channel is so important is partners help in a number of ways. First, they help with the normalization of customers’ data estates. With these companies, we don’t just take their modern communications data. We also take their legacy application or ERP data or live feeds. Our partners are already in there. They already have established relationships. They’re probably the ones that installed the SAPs or the Oracles or other applications, and they’re extensions of the team, so they help with the modernization of those data estates. And it’s a great relationship because I don’t have a large services team. We really are a platform company. We’re a data governance platform. And we rely heavily on partners to take the platform to the customer base.

What does Archive360 do to make it easy for data to be fed into your archive? Develop your own connections with other vendors? Partner with other vendors that can feed data to Archive360? APIs?

So basically, all of the above. We’re not going to build thousands of connectors. At a very high level, we do interact with things like Azure Data Factory. We allow them to connect with their 300 connectors to the data they have, and I just have to make one connection to ADF. For example, we have individual connectors for your standard Microsoft applications like Teams and Chat and email. Those things are natively connected. There’s a number of tools—I know it’s an old term, it’s been around forever—but for any ETL (extract, transform, load) tools, we can grab data from them. We also have a very robust API set, and we also have a concept known as drop zone where customers place their data in a format that’s already specified into a drop zone or a file location, and we can sweep that up on a regular basis depending on the scheduled interval. So there’s a number of ways to get the data in. And the important thing is, we don’t clean the data, but all the analytics and AI applications are looking for normalized clean data. We don’t necessarily clean it, but it’s clean data once it’s in our system, otherwise it would be rejected or need to be modified. So it’s normalized before it gets into our system. That way, you are using reliable, trusted, clean data for your models.

Is there anything else you think we need to know about Archive360?

One of the biggest challenges is data fragmentation. Businesses have data sitting all over the organization. I hate to keep going back to the Veeam example, but a lot of that data sitting on tape or other backups somewhere. It’s really difficult if you have your data in all these different locations. And if the data is not in a normalized format, it’s hard to offer access control. Everybody’s rushing to put everything into their AI models. Wait a minute. Are you really putting PII (personally identifiable information) in there? Are you really including social security numbers? You need to control access to this data. Those controls, those guardrails, are not placed on a lot of this data today. Those are some of the challenges. And having vector indexes to be able to feed your models is also something most companies don’t have in place today. I can go on and on with challenges, but I think that highlights most of them.