Author: Dylan Arslanian

The word “Google” became a verb in 2006, when it found its way into the Merriam-Webster dictionary. But like all shifts in language, it had caught on long before official recognition caught up to everyday usage.

That happened because search engines like Google had caused a seismic shift in the way humans access information. The algorithms read linking behavior between pages to enable information retrieval on an unprecedented scale. And it was all wrapped up in a streamlined, easy-to-use interface that gave users (even non-technical ones) what they wanted, fast: lists of websites semantically linked to their search query. 

Bad news for public libraries, but amazing for almost everything else. It’s become so ubiquitous that most of us struggle to imagine the “before time”.  

Why you might not be Googling for much longer (or at least not in the same way)

None of this is news to anyone, so why am I talking about it? Simple: we’re now at another inflection point, heralded by the arrival of powerful new technology. Generating lists of websites ranked according to relevance is useful, but it’s just the starting point. The user still needs to sift, evaluate, analyze and synthesize that information.

And as Google itself has acknowledged, there is now a pressing need to go further. Lists of websites are not enough: users want “deeper insights and understanding.” In this post, we’re taking a look at how generative AI is changing how we search, paving the way to richer insights and better decision making. 

But first, a brief diversion on semantic analysis. 

Semantics: building search queries, word by word

Every word has what is known as a semantic domain, a range of other words that connect to a shared substrate. For example, the word “vehicle” is part of a semantic domain that includes words like car, plane, ship, and many more. “Vehicle” can also signify a means of achieving something, particularly in medical and scientific applications. 

Search engines operate by identifying pages that contain keywords in the user’s search query. But one of the conventional limitations of keyword-based search is that it lacks the human intuition to situate the right part of the semantic domain. So, “vehicle”, taken out of context, can recall the entire semantic domain of “things that travel”, and the entirely different domain of “biological component that delivers drugs”. That’s too broad to be useful.

But there are many ways to refine a search query to get narrower and more relevant results:

• Quotation marks around a phrase to fetch exact matches of that phrase: “the costs of drug discovery” will fetch only results that contain those words and in that order.
• Hyphens to exclude certain results: if you want to get results about unicorn companies, but don’t want to deal with pages that talk about magical creatures, you can use: unicorn -creature 
• If you need information in a specific format, you can ask Google to only return results in that format, as in: tech talent shortage filetype: pdf

From keyword search to generative AI: bridging the human/machine gap

Behaviors like this simulate the context-driven decision-making that we take for granted when we communicate with other humans using a natural language. And they are behaviors that generative AI can now automate. 

Generative AI reads search queries using NLP algorithms that interpret language in the way that we do as humans – but on a much larger scale. This allows it to understand the intent behind a user’s query, and create content that matches it. In other words, generative AI makes that crucial step that search engines couldn’t make – interpreting the information on web pages for the human user. And, much like search engines did in the early 21st century, generative AI like ChatGPT combines this new power with a user-friendly interface that people love to engage with.

Why it isn’t quite over yet for search engines

As others have noted, search engines still have the edge over chatbots when it comes to crawling the web to find up-to-date information. Chatbots are trained on large but static data sets. For researchers in particular, this is a major limitation. 

And much like search engines, the output of generative AI is only as good as the input it receives. In other words, it takes knowledge and skill to create an effective prompt for the AI to use. We can all expect to hear a lot more about prompt engineering as an in-demand competency as the AI revolution continues to unfold. 

What’s needed is a way to leverage the real-time information gathering of a search engine, with the intuition and nuance of generative AI and NLP.

Similari: AI-based insights for the future

By leveraging the latest advances in AI, ML and NLP, Similari equips researchers and business development professionals with continually up-to-date insights on their industry and their competitors – all without the need for complex human-led prompt engineering.

Because it learns the habits and preferences of human users over time, Similari can refine searches by identifying the most relevant terms from a semantic domain, and tailoring results to specific business needs. 

Get in touch to learn how Similari combines the intelligence and power of an AI chatbot, with the flexibility and 360-degree line of sight of a search engine. 

In a sense, R&D professionals are spoilt for choice when it comes to automated search tools. There is now a wide (and growing) range of solutions that can streamline the process of monitoring technical data, and extracting insights from it. 

Here, we’re unpacking three solutions that augment human search capabilities: Google Alerts, ChatGPT3 and, of course, Similari, to find out where their strengths and weaknesses lie. 

Google Alerts: your trusty research sidekick for the last 20 years 

Google Alerts can be a powerful tool for gathering information about new and emerging trends, technologies, and ideas that impact innovation strategy. 

By simply signing in and setting some parameters, users can track industry keywords, flag key trends and stay up to date with the news in their field. They can even keep tabs on competitors, provided the information makes it into the news cycle (more on this later).

And it’s all served to them, daily, weekly, or however they prefer, via email. All in all, it’s a useful tool that can help innovators to at least keep up with the curve, even if staying ahead of it remains just out of reach, for reasons we’re about to explore. 

Everyone has a blindspot (even Google)

For all its (completely free) benefits, there are some limitations to keep in mind when it comes to using Google Alerts for innovation intelligence.

Available sources: vast, but limited in important ways

Sources are limited to pages indexed by Google, and within that, mostly news sources (recall what we said earlier about competitors). That may be enough for certain use cases, but it leaves a lot of relevant information out of the frame, especially when it comes to scientific and technical literature. 

Not everything worth knowing makes it into the news – and if it isn’t there, it won’t make it into a daily Google Alert.

The brute facts aren’t enough on their own

Users have noted long standing challenges like false positives (unrelated content that matches the keywords) or false negatives (relevant content that does not contain the exact keywords). 

Additionally, Google Alerts does not offer much customization, so users can’t combine multiple searches into a single alert. And because it’s email-only, there’s no easy way to combine all emails and news stories into a single source. Reporting on the facts, and analyzing their significance – that’s left up to researchers to do the old-fashioned way.

From data to insight: the missing step

Perhaps most importantly, Google Alerts can’t automate that crucial step from data to insight – and insights are what fuels innovation. The work of figuring out the story the data is telling remains with the user. In other words, it’s a valuable tool for exploration, but not exploitation.

ChatGPT3 and the future of AI-enabled research

Meanwhile, at this very moment, the internet’s favorite chatbot is having human-like conversations with millions of users. What are they talking about? In short, everything, including a wide range of business use cases: automating marketing & sales, debugging code, and most importantly for our purposes – R&D. 

But it’s not just the impressive NLP that has the whole world abuzz. It’s also the extraordinary ease of use that ChatGPT3 provides. The user simply gives an input, asks a question, and sits back while the generative AI works its magic. Unlike email-based alerts, this experience is conversational. The user can ask follow-up questions or demand justification. 

Scientists and researchers can curate their own corpus of technical information and feed it to the system, and leverage its computing power through that simple interface. 

ChatGPT’s limits are harder to find, but they exist

Since it appeared in 2022, ChatGPT has been used by millions of people, and it’s received intense scrutiny in the process. This has provided useful input for the platform itself, while pointing up its limitations. 

The problem of data (again): historical and reactive 

As we saw with Google Alerts, historical data can only take you so far. ChatGPT has limited visibility past 2021. And while that could change in the future, its text-based output is best suited to providing a historical account of past data.

Innovators seek uncommon knowledge, not the common ground

ChatGPT is adept at drawing on billions of data points to come to a single answer. It can tell users, usually with great accuracy, where the common ground lies in a specific scientific dispute. It can even point you to its sources (if you ask nicely). This is all extremely impressive. But it’s not what researchers need to help them innovate, because innovation is about challenging the status quo, and finding new ways to interpret the data. 

And even with a private corpus of texts to work with, ChatGPT’s output is always text-based. That’s ideal for marketing, or generating source code, but not for scientific research, where data visualization plays a vital role in decision making. 

Similari: insights at your fingertips

We would be remiss to not tackle the question of where Similari fits in this picture. In short, Similari allows researchers to go deep where other tools favor breadth. In a matter of minutes, researchers can configure Similari to start watching, learning and generating deep insights about their specific field of inquiry. 

Picking up where search analytics leaves off: an insight mechanism that learns over time

Traditional search analytics platforms excel at finding data and presenting it in a predefined way. But they leave the heavy lifting to the researcher or analyst who must spend time curating that information, deciding what is and isn’t relevant, and juggling multiple disparate data sources. Similari replaces that workflow with a unified, live feed of up-to-date insights.

And over time, Similari learns from the human user, absorbing their preferences and imitating their behaviors – all without explicit instruction, thanks to its sophisticated underlying ML framework. That’s how Similari is able to slash manual data monitoring time by 80% or more, while actually enhancing research outcomes and fuelling innovation. 

Harnessing the power of AI to know everything, all the time

Augmenting human capabilities with AI is now a priority shared by businesses in almost every industry. And as the market matures, it’s providing more and more targeted solutions for highly specified business needs. Similari is one of them. And, for innovation professionals at least, it’s one of the most effective. 

To learn more about how Similari enhances and streamlines research for R&D and innovation teams in the life sciences and beyond, schedule a demo with our team. We’re ready to show you everything you could be missing with traditional search analytics – and much more besides.

Globally, we’re producing more data than ever before. But the events that catalyzed and accelerated that historic increase caught us by surprise, so the methods that we use to handle it are still catching up. None of this is news to anybody, but it’s less obvious why this is a problem.

After all, data is the lifeblood of innovation, and the better informed we are, the higher the quality of our decision making. But if you don’t have the right tools to organize that data and extract insights from it, it’s very much a case of “water, water, everywhere, but not a drop to drink.”

Data overload: too much of a good thing?

What we’ve described is the unfortunate situation that many life sciences businesses find themselves in, and now it has a name: data overload. Companies that rely on old, manual methods, may find themselves overburdened by the volume and velocity of data. So how should companies be leveraging big data, without triggering data overload? 

The answer lies in AI technologies like Natural Language Processing (NLP), that enable teams to digest huge amounts of information, quickly, and distill actionable insights from the data stream. 

In this article, we’re going to explore the key problems that data overload causes, and how solutions like Similari leverage AI and NLP to address these challenges. 

Data overload woes for R&D and innovation teams

Data overload happens when there is too much data to effectively process, analyze, and make decisions from. In the age of “Big Data”, companies urgently need a way to sift this information before they can use it. Ultimately, it’s not about how much data you have, but how you use it to achieve your goals.

Back in 2001, Doug Laney defined Big Data using three essential characteristics:

Volume: the amount of data that needs to be processed and stored

Velocity: the rate of real-time data creation

Variety: this data can be structured, semi-structured or unstructured

And with Big Data now bigger, faster and more varied than ever before, businesses need to guard against risk factors like these:

Inefficiency

More clinical trials, publications and drug patents are good news in a sense: they increase the amount of information available. But the sheer volume of available data makes it difficult for businesses to process it, let alone extract insights by selecting the most relevant and actionable information.

This inefficiency arises because traditional search methods are manual and reactive (or historical). They involve sifting through the results of events that have already taken place. But in a dynamic environment (remember: big data is generated in real time), this isn’t enough. What do you do when one set of scientific results supersedes a previous one? AI resolves this problem neatly – we’ll get to that a little later. 

Missed opportunities

In the face of increased volume and velocity of data, companies that rely on manual or database search methods inevitably have to choose between limiting the scope or the depth of their research. When your focus is too narrow, or too shallow, you risk missing out on key market trends and opportunities. But the opposite problem is equally dangerous: choice paralysis is a very real problem, as articulated in the so-called Hick’s Law: an excess of options can actually slow down decision-making. This is crippling for humans, but not for AI (more on this later). 

Poor decisions

Perhaps worse than missed opportunities and decision paralysis, data overload can lead companies to take decisive steps – but in the wrong direction. Fixating on a single data point, or misunderstanding facts in their particular context, can lead to inaccurate conclusions.

Addressing data overload with Natural Language Processing

NLP is an interdisciplinary field that brings together artificial intelligence, linguistics and computer science. It focuses on the interaction between computers and human language. NLP techniques are used to analyze, understand, and generate human language in a way that AI can understand and process. If it’s sophisticated enough, NLP can read and comprehend written texts, extracting the most salient points and distilling insights for the human decision-maker using the system.

NLP alleviates the burden of data overload by automating away mundane, time consuming workflows, allowing data intelligence professionals to focus on making sound decisions. Crucially, it also replaces reactive search with proactive insight: when facts change, an AI-powered system can register the change almost instantaneously. As we discussed earlier, that overcomes one of the most profound weaknesses of traditional methods. 

Key use cases for NLP in the life sciences

Literature mining: R&D teams can use NLP techniques to extract information from large volumes of scientific literature, such as research papers and patents. This can be used to identify new drug targets, understand disease mechanisms, and track the progress of scientific research.

Clinical trial management: NLP can be used to extract information from clinical trial protocols and reports, such as inclusion and exclusion criteria, adverse event reports, and treatment efficacy. This information can be used to select appropriate sites and improve the design of clinical trials. It can also help companies avoid costly and redundant dead ends. 

Drug discovery: NLP can be used to extract information about chemical compounds, proteins and genes to identify new drug targets, and better understand the mechanism of action of existing drugs. And it can get this all done fast – and more accurately – than any human could, by automating key components of the process:

• Surveying biomedical literature for specific genes related to therapeutic outcomes
• Identifying white spaces for specific disease targets
• Searching patent data concerning specific technologies

Further applications for NLP in life sciences research and innovation 

Executive buy-in: R&D and innovation can cut through the noise of data overload and find the precise insights that bolster the business case for each new initiative. Using this information, they can motivate resource allocation to the C Suite based on sound, up-to-date insights. 

Build partnerships: Using NLP techniques, business development and partnership teams can parse scientific literature and extract key information from these documents, such as the names of researchers, institutions, and technologies. All of this data can be used to identify potential partnerships and opportunities for collaboration. 

From data overload to data motherlode: how Similari harmonizes data to supercharge innovation

The proliferation of data isn’t going to slow down – in fact, we can expect to see over 180 zetabytes in 2025, as the curve gets steeper. Next generation AI technologies make it possible to keep up, by translating huge swathes of data into graphs and presentations that make sense for the humans who use them.

Similari empowers scientists, business leaders and data professionals to harmonize data, reveal insights and make the sound, innovative decisions that will shape the future of life sciences.  

Get in touch with our team to learn more, or schedule a demo to see exactly what Similiari can do.

2022 is now firmly in the rearview mirror, but many of its most important trends are still playing out. Here, we’re picking out of a few of the most significant highlights and lowlights, and discussing what they could mean going forward.

Necessity: the mother of invention? 

One of the prevailing themes of 2022 was “doing more, with less”. From the very beginning, business faced historic talent shortages, exacerbated by “The Big Quit”. Geopolitical destabilization made a bad situation worse, disrupting access to Ukrainian tech talent. All in all, 2022 was a rough year for hiring, and it’s still looking very much an employee’s market in Q1 of 2023.

But it’s not all doom and gloom. Valuable lessons were learned, particularly when it comes to optimizing for innovation. Companies have turned to AI solutions to alleviate the pressure of R&D talent shortages and adapt to a leaner, more agile way of working. Those who have adapted in this way have begun 2023 on a stronger footing, and are better equipped to keep innovating in an uncertain climate.

Turning over a new leaf: sustainability, prioritized across the board

While it has been – and still is – hotly contested, there were major developments in the ESG space last year. 2022’s proxy season saw a record number of ESG proposals, primarily focused on environmental issues, but also social causes.

This renewed focus on sustainability goals was also reflected in M&A activity. IBM’s acquisition of Envizi signaled a commitment to move towards more sustainable business practices. Envizi’s software enables companies to coordinate and consolidate the hundreds of data types needed to analyze and manage their ESG goals. By integrating Envizi with IBM’s asset management, supply chain and environmental intelligence software, companies will now be able to automate much of this workflow and scale their efforts.

The view from life sciences and pharma: M&A down, partnerships up

While M&A deals in life sciences dropped off, licensing partnerships saw a small but significant increase, from $178 to $179 billion, an early harbinger of what is to come in 2023. As funding dries up, and life sciences companies face uncertain economic conditions, collaboration and ecosystem building have become critical priorities.. 

Through 2022, buyers also demonstrated a growing appetite for licensing agreements that spread the risk, rather than outright acquisition. This cautious approach was exemplified by Roche’s strategic collaboration with Poseida Therapeutics, combining Poseida’s novel cell therapy techniques with Roche’s development and commercialization capabilities. 2022 set the tone that 2023 is following: partnership and licensing agreements are key business development priorities that call for next-generation solutions. 

Life sciences companies have yet to make DnA a part of their DNA

But when it comes to adopting those solutions, the life sciences lagged behind other industries throughout 2022. Mckinsey notes that just over half of Digital and Analytics (DnA) leaders in pharmaceutical companies report having implemented digital applications at scale. The situation was even worse for MedTech. By their estimate, full adoption of digital solutions could bring the industry up to $190 billion in gains through the life sciences value chain, from streamlining clinical trials to enhancing drug discovery.

Generative AI and the future of BI

No review of 2022 would be complete without a nod to the rise of generative AI. 2022 was a bumper year for AI, across the board. But Openai’s GPT-3 took center stage towards the end of the year. The potential use cases for business intelligence are still unfolding, but it’s already being used for a variety of applications:

• Data analysis: GPT-3 can help analysts by quickly summarizing large amounts of data, generating reports, and performing exploratory data analysis.

• Predictive analytics: GPT-3 can be used to generate forecasts and predictions based on historical data and business trends.

Similari knew all of this before we did (and much more).

From a human point of view, 2022 felt like a whirlwind – even one and a half months later. But for Similari, it’s all been processed neatly and accurately into readily accessible data – data that business leaders can tap into for reliable and actionable insights. 

To find out how Similari helps businesses find certainty in uncertain times, schedule a demo with our team. We’ll guide you through Similari’s key features and show you how intelligent market surveillance and proactive analytics equip companies to tackle the future with confidence. 

As of November 2022, 434 thousand clinical trials had been registered, approximately 35,000 more than 2021. This growth shows no signs of slowing down, and the proliferation of clinical data that these trials produce is driving innovation and drug discovery. That’s all to the good, but it also creates an urgent problem for the pharmaceutical industry at large: with so many trials ongoing, how do companies avoid duplicating research that has already been done?

In this article, we will discuss the costs and risks of redundant or duplicate clinical trials, and propose a much-needed solution for the problem of research waste. 

What exactly is a redundant clinical trial?

As the costs of clinical trials continue to balloon, cutting down on research waste should be an urgent priority for the entire industry. Regulators in many countries agree: China’s top regulator recently warned that it would be taking a harder line on redundant trials that waste resources. 

Broadly speaking, a clinical trial is considered redundant if the question it poses can or has been resolved on existing evidence. There is an important distinction between this kind of trial, phase IV trials that investigate side effects of treatments that have already entered the market. Phase IV trials are essential to assessing drug safety, and are, of necessity, justified by a review of existing data. Redundant trials, on the other hand, are not.

A significant 2020 study found, alarmingly, that half of Randomized Clinical Trials (RCTs) did not cite a Systematic Review (SR) before commencing the trial. This oversight radically increases the risk of research waste, with dire financial and ethical consequences.

The costs of redundant trials: the obvious and the hidden

The first and most obvious cost of wasted research is that of conducting the clinical trial itself. This includes all activities from planning to execution:

• pre-study preparation and writing protocols
• preparing for enrollment and recruiting participants
• conducting the study, administering interventions and collecting data
• analyzing results and writing reports
• obtaining regulatory approval for any products used during the clinical trial

In addition, further costs emerge after the trial: auditing by an independent third party, and the costs of marketing new products when they’re ready. The critical point here is that, for a truly redundant trial, all of this can and should have been avoided. The biggest cost of all, lurking behind all of the ones we have listed, is the opportunity cost incurred by pouring resources into dead-end research. That may be difficult to quantify, but it can be mission-ending.

Costly and harmful: a lose-lose for the pharmaceutical industry and patients alike

Perhaps more importantly, redundant trials expose patients themselves to unnecessary and unjustifiable risk. Risk is inherent to all clinical trials, and justifying that risk is an integral part of the systematic review process. Where that process is inadequate, or omitted altogether, human life and well-being are jeopardized. Recent research from China demonstrated the human cost of wasteful research: in one case, thousands of adverse events and hundreds of deaths. As regulators around the world move to close in on redundancy, companies can expect to face more scrutiny – and more penalties – for wasteful research in future.

Leveraging AI to safely navigate oceans of data

The way out of this crisis is obviously not to halt or even slow down innovation. What’s needed is more intelligent innovation that can avoid duplicating research and wasting resources. To achieve that, R&D professionals need a way to accurately survey existing data and identify genuine white spaces. But the rapid proliferation of data points has already made traditional methods of monitoring technical data obsolete. 

Artificial Intelligence is the solution that the industry desperately needs, and it holds the promise of revolutionizing the way research is conducted in the life sciences. Traditionally, pharmaceutical R&D teams are forced to rely on labor-intensive research practices that they simply cannot scale without drastically increasing personnel numbers. Artificial Intelligence is now able to replace much of that workflow, proactively scanning millions of data points and presenting them to the humans who need to make critical business decisions. 

Know everything you need to know, moment to moment, with Similari

At Similari, we’ve brought together industry leading expertise from AI, machine learning and NLP to create exactly the kind of innovation intelligence that enables pharmaceutical researchers to reliably identify what’s been done before, what’s currently being done, and where they can move in the future. 

To learn more about how to future-proof your innovation projects with cutting-edge data surveillance and monitoring tools, schedule a demo with the Similari team. We’re always ready – just like our technology – to show how much humans can achieve when they have all the information they need.

Within the field of pharmaceutical research, there are an overwhelming number of variables to consider within research and development (R&D) as well as production and manufacturing. 

Between these considerations, navigating government and industry regulations, existing and impending medical patents, and staying ahead of competitors while keeping abreast of current trends that can impact trial results and outcomes make manual research a logistical nightmare for CI professionals and their teams. 

On top of this, the fight to find the data necessary to make swift, strategic decisions means it’s even harder for C-suite executives to ensure that R&D budgets stay on track. Projects extend beyond deadlines due to the never-ending snowball effect of vast new sets of incoming data and information that impact research and clinical trials and testing.

AI is reshaping the way data is curated and mined for insights

With such an overwhelming abundance of unstructured and unusable data that needs to be identified, extracted, and analyzed, it’s unfortunately far too easy for invaluable insights to slip through the cracks, setting teams back and potentially costing businesses millions in lost revenue.

The only scalable solution for efficiently managing vast amounts of incoming large-scale datasets is to leverage the power of AI and machine learning to streamline and automate the research process. 

Today, we’ll be making the case for why AI and automation should be front and center in your research strategy to speed up, simplify and reduce the costs of your research process, no matter your organization’s size or scale.

AI’s competitive edge in research capabilities

AI-powered systems have the ability to rapidly collect, analyze, interpret and huge volumes of incoming datasets, presenting them as accessible, actionable insights that enable swift and powerful decision-making. 

AI’s ability to trump human input when it comes to research and analysis output is not anecdotal or circumstantial. In a recent case study by Hubspot, researchers examined the effects of Intelligent Literature Monitoring (ILM) which augments literature searches with AI and Natural Language Processing (NLP) capabilities compared to a controlled study that was manually conducted. 

Compared to the manual research process results, AI-assisted ILM reduced research time by between 88% and 92%, while still achieving 99.8% sensitivity and 95% specificity. 

By utilizing AI and machine learning to power their research operations, CI professionals and teams can transform the way they conduct research, resulting in efficiency gains, greater research accuracy, and reduced expenses that ease strained R&D budgets. 

AI systems can quickly and efficiently mine enormous amounts of data from various medical publications, articles, press releases, research papers, and other sources. Using deep learning, it can accurately interpret both printed and handwritten text as well as chemical representations and figures, test results, scans, and imaging to glean essential data while filtering out unimportant data, presenting it as accessible insights.

Research teams no longer have to spend endless hours manually researching and interpreting relevant data to inform development strategies. 

With the data they need at their fingertips, they can reinvest this recovered time into strategic planning and modeling, such as identifying new business opportunities, managing potential risks to pipeline products, and analyzing competitor actions, all of which translates into more business revenue and allow for better budget guard railing.  

AI as a scalable research solution

Thanks to a complex interplay of 2020’s global recession coupled with ongoing rising inflation and curbed consumer spending, 2022 has seen a slew of layoffs across the tech sector. Companies looking to conserve their resources are increasingly streamlining and automating their operations where possible. 

A common misconception is that adopting AI and machine learning is a more costly investment decision as opposed to simply hiring more staff. While scaling your teams might seem like a more cost-effective strategy upfront, the ever-increasing data volumes they’ll be facing means that, for your teams to keep up, you’ll continuously need to invest in expanding your team, driving up overheads and stretching budgets.

AI is a viable alternative that is inherently scalable and self-sustaining, able to manage increasing data volumes without affecting its output, accuracy, and efficiency. Equipping your teams with a scalable research solution, as opposed to perpetually hiring additional staff members, solves both the underlying challenges of scale and cost.  

Similari: Your partner in research innovation

At Similari, we harness AI and deep learning capabilities to streamline and simplify your research operations to enable faster, more agile decision-making and proactive, instead of reactive, development strategies to keep you one step ahead of competitors. 

Our platform seamlessly tracks, consolidates, and analyzes enormous volumes of datasets in seconds to bring the data you need to you, eliminating the costly, time-consuming hunt for vital information necessary to take swift action, capitalize on opportunities and mitigate potential risks with ease.