On‑Chain Analytics 2.0: How Nansen AI, Arkham and Powerdrill Replace Manual Research

Crypto on-chain research that investors and traders know today is considerably simpler than it used to be, and the big reason behind it is the use of on-chain analytics tools. One good example is Nansen on-chain analytics.

According to Nansen (2026), manual crypto research operates as a slow serial process where a human can only evaluate 5 to 10 tokens daily before exhaustion limits them. In contrast, modern agentic trading systems eliminate this manual friction by continuously monitoring hundreds of assets simultaneously to surface actionable alpha spikes before the opportunity disappears within minutes

Before those became available, investors had to conduct research the hard way. Users had to manually enter wallet addresses into block explorers and study transaction histories. It was a one-step-at-a-time process that took a long time to do, and then, when you got all the information, you had to piece together market behavior yourself using a bunch of scattered data points.

The process was slow and repetitive, not to mention that it heavily depended on the user’s experience and technical knowledge. Fortunately, developers recognized these problems and have created another approach. Now, platforms combine address labeling, entity mapping, and behavioral clustering with AI-powered engines to transform those random data points into actual information. This makes the process faster, meaning that the speed is no longer limited by human capabilities.

Core Frameworks of the Modern AI On-Chain Toolkit

The biggest change in how modern on-chain analysis works does not revolve around the ability to access the data. After all, the data on the blockchain has always been public and transparent. The change comes from the ability to organize it, which makes it easier to interpret and act on it quickly enough. Human limitations in researching and connecting the pieces usually meant that, by the time they became aware of the opportunity, it was already gone. Thanks to some of the best on-chain analytics tools for crypto 2026, the process is now significantly faster. For example:

Nansen AI

The first to consider is the previously mentioned blockchain analytics platform Nansen AI. Its main strengths lie in the fact that it offers a massive database of labeled blockchain addresses. With over 500M+ labeled addresses in its library, the user doesn’t just see a random wallet address consisting of strings of numbers and letters, but the wallet’s established identity, if it has one.

Seeing and knowing immediately who the wallet belongs to speeds up the process, as the user now knows if it is an exchange wallet, a VC fund, or some other entity.

This system lets users keep track of behavioral patterns of various entities, rather than just isolated transactions. For example, if multiple venture fund-owned addresses start buying a specific token without a clear reason, analysts can see it quickly and highlight the growing trend before it becomes obvious to the entire market.

Nansen’s use of AI-powered bots also expands what the bots are capable of. Traditional bots were capable of following specific instructions, such as to buy when the price falls 10%. With AI agents behind the bots’ trading decisions, they can evaluate multiple market signals to make a better, more educated decision, making their activity more similar to how a human would analyze the market. Of course, they are still not entirely independent, and human oversight is still necessary, but this approach significantly reduces the need for manual research.

How does Nansen AI assist investors in making better moves?

Nansen AI has integrated Google Cloud’s unified, enterprise-grade machine learning and AI platform, Vertex AI. With it, it can quickly build and deploy AI-driven features that can help bring an intelligent era to blockchain investing.

Arkham Intelligence

Next is the Arkham Intelligence on-chain analytics platform, which is dedicated to entity mapping.

The platform believes that one of the biggest challenges in blockchain analysis is the pseudonymity of addresses. While all information is transparent on the blockchain, that just means it is visible to everyone. However, without knowing who the owner of addresses is, it is impossible to determine what is going on in the market - at least, it can’t be done early enough for this information to be actually useful.

Arkham Intelligence combines blockchain activity with publicly available information to conduct behavioral analysis. This allows it to connect wallet addresses to organizations and/or individuals they belong to, allowing users to monitor the behavior of VCs, exchanges, and other blockchain participants, including even wallets connected to major hacks.

Giving context to wallet behavior is the biggest and most practical benefit of the approach. Any transaction, big or small, means very little on its own. Knowing who the address belongs to can put those moves into context and help identify trends early on.

Powerdrill Bloom

The last in this on-chain data analytics tools comparison is Powerdrill Bloom, which represents a broader shift toward conversational data science.

Powerdrill Bloom emerged because traditional crypto data analysis requires database queries, spreadsheets, custom dashboards, or some other way of scripting knowledge. For a lot of investors, this introduces a barrier, since they lack the technical knowledge needed to do this, even if they have a basic understanding of the markets.

Bloom emerged to solve this by allowing users to ask simple questions using plain language and receive the information they need. The approach relies on text-to-SQL workflows, which translate the user’s natural-language request into structured database queries.

The result is the data that the user needs, which can be displayed clearly through dashboards that update the information as it changes. This is advantageous for researchers as they don’t have to spend their own time building reports, and can simply focus on interpreting the results.

Tracking Whales and Smart Money Flows

Today, one of the most common uses of on-chain analytics is keeping an eye on what large crypto holders (whales) do with their funds. Investors, and especially traders, have always kept a close eye on what the wealthy do, as their moves often had a strong impact on the broader crypto market.

Moving money between wallets and exchanges can be particularly impactful, as it often signals what might be coming next. And, while no single transaction guarantees future price behavior, patterns do.

For example, if whales start withdrawing money from exchanges, this is a signal of a supply accumulation. This means that token holders are removing assets from places where they can be sold quickly, thus reducing the available supply, as well as the likelihood of a sale. This is usually a positive signal, which suggests that the whales have faith in the asset and are ready to hold onto it for a longer period of time.

The opposite - moving funds from wallets to an exchange - signals the desire to sell, which could indicate that the token’s value is about to drop. The whales have either lost faith in the asset and want to get rid of it, or the very act of selling it might mean a price drop, since the available supply is about to increase in the open market.

Either way, whales moving money to an exchange from their private wallets is a signal to start selling, and to do it quickly. However, the context of the move matters too, as a single whale buying or selling could just be their individual decision, made for any reason. The signal is much more trustworthy if multiple entities start doing the same thing.

This is why platforms like Nansen matter, as they aim to identify what they call “smart money” activity. Specifically, if multiple wallets that the platform has classified as high-performing traders make the same move in a short period, such as 24 hours, then that is seen as evidence that market participants have identified an opportunity.

With that said, these smart money signals should not be treated as a guarantee that doing what they are doing is the right move. The best approach is to compare this information to other indicators, such as liquidity trends, volume growth, and broader market conditions. Traders should never base their decision on a single indicator, even if that indicator is typically considered reliable.

How To Build a Practical On-Chain Research Workflow

While many of the biggest analytics platforms that are commonly used today aim to do similar, or sometimes even the same things, the fact is that no single platform can answer every research question. If you want to build a practical on-chain research workflow, you have to use several tools together. Each of them has a specific purpose, with Nansen being good at identifying market trends and tracking smart money activity, while Arkham offers insight into who is behind the wallets.

Meanwhile, Powerdrill is best used for analyzing large sets of data and resting reports without having to build dashboards and database queries manually. With that said, a solid research workflow should look something like the following:

Step 1: Finding Opportunities Through Real-Time Signals

The first step is to spot an unusual activity early on, before it becomes obvious to the entire market. As mentioned, once everyone can see it, it is likely already too late. Look for things like smart money purchases or large wallet movements. Keep track of money, how it moves, inflows and outflows, and sudden surges in trading activity.

Use the alerts to spot things that truly matter. For example, you could instruct the analytics tools to sound the alarm for large transactions going beyond $100,000, or if specific, highly liquid assets start seeing unusual activity.

Step 2: Validating Signals With Cross-Market Data

Once something catches your attention, your next step is to check if it is the real deal. Verification must always come before action, as not all anomalies are worth acting upon. Start by looking at other signals and metrics to see if the movement you detected represents an actual rush to buy or dump the tokens, or if it might be short-term speculation.

Look for indicators like changes in the number of token holders, trading volume, exchange reserves, and the like. Basically, any signal that a lot of people are moving the token or tokens in question. Along the way, pay attention to whether the activity is bullish or bearish.

Finally, keep in mind that systematic exit planning is also important. Some traders keep a close eye on the number of token holders, and how this number changes. A sudden increase in holders suggests bullish behavior, while a sudden drop means that investors are getting rid of the funds. Others also look at sustained three-day exchange inflow trends as warning signs that the accumulation period might be coming to an end, and that it is time to sell.

Step 3: Identifying the Wallets Behind the Activity

Finally, the third step is to determine who is actually making the waves. The way to do this is to determine who is the owner of the wallets that are moving the funds. Large transactions carry more weight when the wallets they come from are tied to major known entities with a long history of setting trends.

This is where Arkham’s tool and its information play a massive role, which, again, goes back to the need to use multiple tools simultaneously. Researchers can investigate if activity that they noticed comes from VCs, market makers, exchanges, protocol treasuries, high-performing traders, hackers, or some other entity, and base their next move on that, at least partially.

This is also where historical profitability comes into play, as it adds additional context. If a wallet, or a group of wallets, that are moving large amounts of money to buy the same asset have a strong history of making profitable moves, they are likely relatively safe to follow. It is certainly safer than following a random unknown address, even if it did move larger amounts.

How AI Is Reshaping Crypto Market Intelligence

On-chain research is about more than just noticing the money movement. These days, it is all about speed of detection, reliability of the entity behind the move, and generally, collecting as much information as possible about the move and who is responsible for it. This is why high-quality analytics tools play a huge role in detection and inspection of this type of blockchain data.

The key part is speed, however, as knowing all the details and drawing the right conclusions means little if you can’t do it in time. Manual research requires a lot of time, often hours, if not days of work, and that’s assuming that the researcher can stay efficient and productive the entire time. Due to human limitations, however, this is usually not the case.

This is why AI-assisted analysis is the new norm, as it speeds up the process through entity mapping and detecting signals quickly. With AI-assisted analysis, data is no longer just visible - it is constantly processed and turned into patterns that quickly notify the trader of market behavior that can be useful to them. And, as these tools continue to mature, it becomes less important for the human user to collect the data, and more important to accurately interpret it.