VC firms receive more than 1,000 proposals for investments each year, and only a few of them will actually make it to an investment. We often talk about due diligence being the crucial part of the process, where deeper investigation into the startup occurs, and check every single detail to make sure it’s worth the investment. Of course, it takes time, sometimes months or even longer for a startup. Can part of this process be faster, simplified, or operationally automated? Let’s discuss that.


Each fund conducts due diligence differently, meaning there’s no unified standard or even close to it. There’s a lack of clarity in founders’ minds on what exactly will be examined by VCs and at what stage of the dialogue, especially when we’re discussing early-stage startups. On the other hand, VCs look at different things: metrics to consider, how to evaluate teams, criteria, and scoring models are a black box. There’s a shortage of widespread methodologies, or it’s all treated as a ‘trade secret’ in other areas of business where certain standardizations exist.

You can evaluate the quality of a car, a product on the shelf, or medicines, as there are clinical trials and regulatory bodies like the FDA that provide approvals. But how do you evaluate a startup, since there isn’t a clear and open approach, especially when it comes to early-stage companies.


This leads to the second problem, which is that startups are not ready to engage with investors after their initial pitch deck got a green light, to the next step. Simply because nothing in the process is clear except for how to present your 3-minute pitch. At least this part is somewhat standardized. There are many examples of pitches that successful startups have made, and we can learn from. But we all know that a pitch is just the first step in a long and time-consuming fundraising effort.

So going behind the closed doors into investor meetings, a lot of founders find themselves in a situation where they don’t have answers for questions coming their way, their data rooms are far from being complete, each VC tends to have some internal forms and sometimes bias, and things get into complications, larger than founders have time and resources to deal with.

A typical VC will ask over 100 questions during interviews and have several additional information and reference requests before making a final decision.

And because each VC firm tends to have its own approach, founders end up spending way more time adjusting and preparing for due diligence than they should, causing them to prolong the fundraising process and divert their focus from running a business or building a product.


The third point to note is bias. When there’s no standard and no clear, understandable methodologies for evaluating startups, VCs come up with different questions for different founders, depending on their education, experience, gender, background, and so on.

All of this is based on the unique experience and vision of General Partners. If we try to do this now, for example, in the HR market, companies that don’t approach the hiring process systematically or structurally will face significant challenges. Before technology helped solve these issues, hiring was messy, and every company would build its own process, set of criteria, and scoring systems for candidates. Now, instead of manually scrolling through thousands of resumes, HR managers use technology to filter and sort all candidates, allowing for a better match and less time wasted from both sides. We’re not claiming this process is perfect; it has room for improvement, but it feels like early-stage startup due diligence is still sort of a wild west.

Taking best practices and making things more transparent for founders would help create a more efficient and time-effective process for all.

Should some parts of the due diligence process be automated to simplify and expedite the process? This question we recently asked among our LinkedIn community. Check the answers yourself:)

Yes, due diligence should and actually can be partially automated through the use of AI technology and data analysis tools. While some aspects of due diligence, such as assessing the team and evaluating the future opportunity for massive impact, may require human judgment and interaction, other tasks like data scraping, market analysis, benchmarking, competitor landscape, financial forecasting, etc., can be streamlined and accelerated with automation. Additionally, machine learning algorithms can help identify patterns and trends within large datasets, assisting in risk assessment and decision-making.

It seems like VCs invest in innovations but don’t use those tools enough in their own daily practice.

We believe that automation and AI can speed up certain aspects of due diligence, creating much deeper, data-driven decisions, saving time and energy from founders and VC teams to focus on what’s important — the human aspect of things. So, we will be seeing the VC landscape changing and processes becoming more tech-intensive and structured than they are right