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Speed Up Diligence, Avoid Red Flags: The Importance of a Technical Diligence Data Room

The importance of a diligence VDR

Introduction

When preparing for technical diligence, preparing data room documents doesn’t typically seem like the highest priority item. When conducting technical due diligence at Ringstone, we see a lot of companies that skimp on it. While that’s not the end of the world, when you give your data room documentation short shrift, you miss out on a valuable opportunity to tell your story, speed up the process, and improve the outcome of the diligence. In this blog post, we’ll look at why preparing your data room is so important, how to make sure the documents help the diligence team as much as possible, and how to make sure the data room tips the odds in your favor.


The Process

Let’s look at how the data room fits into the diligence process. At Ringstone, when we’re brought on for a technical diligence, we’ll first get a brief from the investor on what they’re looking for and what the investment thesis is for the company.


Using this information, we put together an info request. This is a comprehensive list of questions and metrics we’d like to receive, tailored to the deal. The info request is sent to the company in advance, typically in the week leading up to the discovery interviews. We always let the company know that we don’t expect them to go to the trouble of making new documentation, but rather to make available whatever existing documentation they have that aligns with requests and questions in the info request. Following this, they’ll set up a secure online data room for the docs, and then we’ll go through them before conducting interviews with the target.

Ultimately, providing strong documentation that aligns with the request works to the target’s advantage

We ask that no new documents be created during this process, because we understand the company is still doing day-to-day business. Ultimately, providing strong documentation that aligns with the request works to the target's advantage. Let’s take a look at why.


Why Make A Great Data Room

Time during a diligence is always compressed. Typically, interviews span one to three days, but sometimes as little as one. Within that window, the goal is to understand the business, product, and problem domain at a high level and to identify the risks and opportunities a buyer would face in acquiring the technology and R&D team.


Assuming the business is strong, it’s advantageous to move through the initial orientation phase quickly - where the focus is simply understanding what the product does and for whom. A well-prepared data room can accelerate this significantly. The sooner that stage is complete, the sooner deeper technical discussions can begin, creating a stronger opportunity to demonstrate the company’s value.


It's a common concern that moving faster might expose more problems. After going through this process for almost seven years now, I can say with confidence that the opposite tends to be true. Gaps in information often raise more red flags than the problems themselves and signal concern to investors.

When information is limited, those estimates tend to be more conservative,
often resulting in higher assumed costs

Every investor builds a business model for the investment, which includes identified areas of improvement and cost estimates. When there is insufficient information, those estimates go up to account for uncertainty. A well-structured data room helps diligence teams get up to speed quickly so that, during discovery, it's possible to share the kind of details that prevent overinflated assumptions and hedge against uncertainty. 


Importantly, investors are looking for companies where capital, connections, and expertise can drive meaningful improvement. Making those opportunities visible strengthens the case for investment and showcases the company's potential under new ownership.


Typically, the data room is a set of folders organized by topic, filled with every available document related to each area. The problem is that there is limited time to go through these folders, and it can take considerable effort to identify where to start making sense of them. Let’s take a look at how to structure a great data room to get the diligence team up to speed faster.


First, Explain The Business

Even a strong product demo can fail if it's tailored to those who already understand the subject domain, leaving outsiders confused and critical details unclear

The first obstacle is simply understanding the business domain. Even a strong product demo can fail if it's tailored to those who already understand the subject domain, leaving outsiders confused and critical details unclear. Some additional context goes a long way. Ideally, this starts with a one- to two-page crash course on the business and what it does, even before the demo.


A great way to test this is to try it on people who don’t know anything about your field. Find out if, after reading it, they can then accurately paraphrase back to you what your business does and for whom. What often falls flat is repurposing pitch deck materials intended for customers or industry-savvy investors. Those slides are usually full of industry jargon and market framing, which aren’t helpful in this context. Evaluating the market isn’t the focus of technical diligence.

An effective initial mile-high overview of the business and problem domain for technical diligence includes:

  • What your customers do

  • What problems the product helps solve 

  • How the solution improves or simplifies that process

  • What the solution actually is in concrete terms (web app, desktop app, mobile app, platform, etc.)


Product Demo

Once there’s a basic understanding of the problem domain, the next step is to see the product. Ideally, this is done through a live demo, but if scheduling one will consume scarce discovery hours, providing a recorded version in the data room is more efficient.


There’s often a temptation to have sales staff lead the demo, since they deliver them regularly, but this can backfire. While sales reps may be great at giving sales demos to potential customers, they’re used to pitching to people who know the business, understand the category of the solution, and want to see all the features. In contrast, a diligence team may only have a surface-level understanding of the business. The goal isn’t to see every feature, but rather to understand what the solution is.


Take a CRM product, for example: a salesperson wouldn’t usually explain what a CRM is, assuming the audience already works in sales and lives in CRM systems. On the other hand, the diligence team could have never seen any software in that niche. The way car dealers decide on loans, trucks manage routes, or shipping companies handle containers are all examples of unfamiliar workflows that require additional context.


What’s helpful is a clear, high-level walkthrough:

  • What the product is (e.g., “it’s a web app”)

  • Who purchases it (e.g., “shipping companies”)

  • Who uses it (e.g., “dispatchers”)

  • What it’s used for (e.g., “to make optimal routes”)

  • How it’s used, in broad terms (e.g., “each morning, dispatchers look at the fleet on this screen, enter data here, review the system-generated routes, and approve the best ones to generate route maps”).


While not all customer-facing features need to be shown, it’s important to include the various screens, inputs, and outputs of the software to convey the scope of what was involved in building it. Ideally, the person giving the demo understands this perspective and is “under the tent,” so they aren’t caught off guard by requests to move forward or focus on specific areas of interest.


Data Room Structure and the IRL

The usual procedure after receiving the info request is for everyone to work on a copy of the spreadsheet in a shared folder where updates can be seen in real time. The spreadsheet includes a column for the diligence category for each item (e.g., architecture, infrastructure, customer support). One issue is that there are always some questions answered by multiple documents, and some documents that answer multiple questions and thus belong in more than one category. In my experience, the best way to make things easy to find is to structure the data room with folders that match these categories—placing each document in the best-matching category folder and updating the spreadsheet with a link to that document. If a document fits in more than one category, it’s completely fine to place the same file in multiple folders. In those cases, spreadsheet notes and filename hints indicating it’s a duplicate are very helpful.

That said, the documents in the data room are typically reviewed before the info request spreadsheet and its links. Sections are usually taken one by one, with files opened and skimmed to identify particularly useful ones. Only then is the info request revisited—this time with better context.

This has two ramifications worth factoring in: first, it’s impossible to know what will be read first; second, some portion of the limited time will inevitably be spent browsing through all available files.

One helpful step is to sort the contents within each section into a couple of bins: documents that should definitely be read, and those that serve as supporting evidence to back up specific claims. Files like test runner output, scan results, and screenshots of Jira dashboards are absolutely useful in demonstrating that nothing is being exaggerated. However, it’s much more efficient during an initial read-through if these aren’t mixed in with primary materials. Ideally, supporting evidence files are placed in a subfolder and clearly named so their contents are obvious at a glance. File names like Jira_tickets_new_report_epic_2025-02-05.png are especially appreciated. No need to worry about them being long!


Telling Your Story

To write a great architecture orientation, picture a CTO catching up with a CTO friend over coffee - explaining how the new role is going and where the technical challenges lie

The second important step to improve this process is to create a high-level orientation file for each category folder. This doesn’t need to respond to a specific IRL question; it’s an opportunity to tell the story in a self-directed way. It's helpful to make the file name distinctive so it's clear it should be read first - ideally naming and dating it so it appears first in directory listings. This is the place to provide a high-level overview of what exists, why it exists, and what’s planned for the future. Each overview should be written as if addressed to a peer in that function: the architecture section intended for a CTO or architect, the support section for a head of support, and so on.


While it’s best not to assume familiarity with the problem domain, it’s safe to assume familiarity with the discipline. For example, a strong architecture orientation might take the tone of a CTO explaining to another CTO over coffee how the new role is going and where the technical challenges lie.


Highlighting Changes

One of the ongoing challenges is keeping up with changes in the data room as documents are updated. While most data room software includes a notification system, relying on it alone is less than ideal. Sometimes there’s a barrage of alerts for every folder, including areas like legal diligence, which often involve a high volume of documents. Other times, document updates are visible, but upon opening them, it's hard to see the actual changes.


It’s far more helpful to have a human-generated summary that distinguishes between meaningful updates requiring immediate attention and minor housekeeping or supplemental evidence. While sending an email update is appreciated, reflecting those changes in the central spreadsheet is even more effective. Ideally, document updates are pushed in batches, making the volume and timing of updates more manageable. Regardless, highlighting updates in the shared spreadsheet, with the date of change and a brief comment on what was changed, is extremely useful.


Getting Going 

Now that a strong data room structure is clear, it's important to begin preparing it before diligence officially starts. There’s no need to wait for the info request to begin drafting the overall business overview and the category overview files for each section. If these overview files are written without including confidential information, or if non-sensitive versions can be created, they can also be valuable in prediligence. Sometimes, a third-party opinion is requested before an acquirer enters exclusivity, and sharing these materials with potential deal teams at that stage can be advantageous.

It’s also possible to begin preparing technical diligence product demos and compiling supporting files that highlight areas of strength. Regardless of who leads the diligence process or how it’s conducted, having this material ready in advance will be beneficial.


Conclusion

Diligence is always a stressful affair, but a well-prepared data room can take the edge off
and improve the overall outcome

Diligence is always a stressful affair, but a well-prepared data room can take the edge off and improve the overall outcome. Following the guidance in this post sets the stage for a smoother process and helps get the diligence team into the conversations that matter most - faster.


If you’re anticipating diligence in the next year, you can always get in touch with us at Ringstone for assistance in preparing a great data room. Whether as part of a larger sell-side preparatory diligence or as some independent consulting, we’re happy to help.

About The Author

Iain C.T. Duncan has spent the last 20 years working in the software industry as a CTO, developer, software architect, and consultant, building B2B and internal web applications at startups, agencies, and early-stage companies in Canada and the U.S. 


As a practitioner at RingStone, he works with private equity firms globally in an advisory capacity, conducting technical diligence on early and mid-stage companies and preparing companies for the diligence process. He has worked in the diligence sector for six years and has been involved in hundreds of diligence efforts as a practitioner, reviewer, and trainer. Before the diligence sector, he worked on software in e-commerce, association management, non-profit fundraising, genomics, and online education, among others.


An active open-source developer and researcher, Iain is currently completing an interdisciplinary PhD in Computer Science and Music at the University of Victoria, working on applications of Scheme Lisp to algorithmic music and music pedagogy. He is the author of the Scheme for Max open-source programming extension to the Max/MSP audio programming platform and is the founder and developer of the online music education platform SeriousMusicTraining.com.


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