Scend.ai
Scend automates selling businesses.
Scend provides investment banks with the workflow tools they need to execute
sell-side M&A processes, allowing them to focus on what they do best.
Background
Investment bankers are very instrumental in the buying and selling of businesses, oftentimes working 100+ hours in order to meet important deadlines.
However, with the rise of AI and LLM technology, there is an opportunity to automate these repetitive tasks and let bankers make deals more efficiently.
Scend is an early-stage startup at the forefront of creating the first-ever M&A operating system to automate selling businesses.
Time Frame: 100 hours
Role: UX Designer, UI Designer, Brand Designer
Founder: Anirudh Sathya
Website: https://www.scend.ai
Methodology: Design Thinking
Problem: Traditional M&A processes are disorganized and labor intensive
If you’ve ever met an investment banker who works on Mergers & Acquisitions (M&A), you understand how demanding and exhausting their work lives can be.
One of the many steps in the M&A process is compiling a potential buyer list for the company they are trying to sell. Because they often use outdated methods, it can take weeks to scrape the internet and leverage their contacts to discover the most ideal buyers.
Even after these bankers have compiled their buyer list, there are many steps like outreach, valuation, and wiring of funds that take long hours and several different checks before a deal can be considered complete and successful.
Solution: Meet Scend
Scend is an operating system that uses AI technology to:
save bankers weeks of work by automating workflows
create more competitive auctions
reduce errors and improve compliance of M&A deals
EMPATHIZE
EMPATHIZE
Research Methods
User Interviews
User interviews were conducted to gain an understanding of real-life user experiences of investment bankers.
Over 40 different bankers were interviewed to gain insights on the pain points associated with current M&A processes.
The interviewees ranged from analysts to executive directors from investment firms like Wells Fargo and Houlihan Lokey.
Competitor Research
Market research was conducted to learn about the current platforms aimed at workflow automation, and to discover what is working for them and where they are lacking.
While there are no current M&A operating systems, there are existing platforms that involve financial management and research.
User Research Findings
1
Despite the massive scope and size of the M&A industry, limited innovation has existed in the past 10 to 15 years.
2
Investment analysts work long hours on M&A deals, sometimes even reaching up to 130 hours a week. Many of these tasks that take up their time are non-intellectual and repetitive work.
3
Trust is paramount in the business of M&A, given the substantial financial stakes involved and the critical role of sustaining a competitive edge for the success of banks. In this realm, human connections are often perceived as more reliable than automated systems or machines.
4
Buyer outreach is a significant component of the initial phases of a deal. Bankers typically depend on their personal networks, leading to a lack of uniformity across deals. This absence of structure leaves bankers feeling that they may be overlooking potential opportunities.
Competitor Analysis
DEFINE
DEFINE
Establish Goals
IDEATE
IDEATE
Redesigning the Buyer List Formation Process
When a banker is tasked with selling a company, one of the initial steps is to create a list of potential buyers. Through user interviews, we learned that compiling this list is a significant pain point due to the tedious online search for contacts and the iterative nature of it, which typically takes 2-3 weeks. Even after completing the list, there remains uncertainty about the buyers' interest. To address this, we aim to leverage AI to automate the process, saving time and increasing accuracy.
Existing Experience: 2-3 weeks
New Experience: 10-15 minutes
Legend
PROTOTYPE
PROTOTYPE
Low-fidelity Sketches
I mocked up some initial sketches of the buyer list formation process, focusing on simplifying the amount of details required from the user. Because bankers do a majority of their work on their desktops, so I prioritized a desktop version of the platform.
Wireframes
Using Figma, I translated these concepts into a digital format and added more content. When the user selects the option to create a customized buyer list, they will be guided to a form wizard to provide basic seller details. With AI integrations, the user only needs to enter the company name, and Scend will automatically fill in the rest of the information, which the user can review and edit for accuracy. After reviewing, Scend will generate recommended buyers for the company based on relevant industries/acquisitions, fund size, and target checks. It will also provide the user with the contact information of the most relevant person for potential deals.
Visual Design
After creating wireframes, I did some branding research to make decisions on the visual design elements. I started by listing out the brand’s values and then gathered images that fit with those values to create a mood board. Since Scend is still an early-stage startup, it's important to align with industry standards. During my competitive research, I noticed the recurring use of black-and-white color schemes with simple fonts. While these may seem like a safe choice, I also considered the fact that this is an AI-based platform. It's important for our customers to feel that they can trust our software and understand that it is operated by humans. To achieve this, I included an accent color and varied the neutral colors and fonts.
High-Fidelity Screens
During the final stage of the prototype phase, all visual design elements were incorporated into wireframes, and the initial set of high-fidelity UI screens was created. Additional screens were added to complete the task flow, and the form wizard was separated by required user input and auto-populated information.
TEST
TEST
Usability Testing
Investment bankers from the following firms were recruited to test out the MVP of our prototype:
Wells Fargo
Houlihan Lokey
Objective, Investment Banking & Valuation
Leerink Partners
Jefferies
We asked each of the usability testers to go through the prototype and identify any issues with the formation process of the buyer list. Additionally, we asked them to provide any suggestions for additional features, drawing from their extensive experience in M&A deals.
Testing Results
The usability tests gave us valuable insights into the effectiveness of certain features and how we could improve the Minimum Viable Product (MVP).
All participants provided feedback that automating the buyer list formation process would save weeks of work. They also mentioned that the auto-populate feature simplified the amount of information they were required to input.
However, some participants pointed out that they often have potential buyers from external lists, such as from networking or other sources, that they would like to combine with the list provided by Scend. This opens up the opportunity to use AI to read imported documents and translate the data into a compatible format
Additionally, they expressed the need for a simple way to add and remove items from the buyer list. While this can be done manually, we can explore other options using AI to input a request and have Scend make those changes automatically.
FINAL SOLUTION
FINAL SOLUTION
Buyer List Formation
After the user logs into their account, Scend will open their most recently worked-on deal. On the dashboard, they will find several options to work on the M&A process. The first step is to gather potential buyers for the company they are looking to sell.
When the user selects "Generate buyer list," a form wizard will open up, prompting them to enter basic information about the company they want to sell. The AI-integrated function will then auto-populate the remaining details such as industry, service, and financials. Additional information about desired buyer types will be assumed based on the company details, which can then be reviewed and edited by the user.
Once the user has reviewed and confirmed the information, Scend will gather potential buyers based on all inputs and list out the buyer's name, fund size, relevant acquisitions and industries, target checks, and contact information.
Project Takeaways
This project was instrumental for my development as a designer since I was the only product designer on the team. As Scend is a very early-stage startup, there were many uncertainties regarding the direction we wanted to take with this product. Our goal was to develop a tool to automate M&A workflows, drawing from the extensive experience of the co-founders in banking and finance. However, the challenge arose when we needed to come up with a concept that set us apart from our competitors. This required extensive communication between the founders and the software engineer to explore what was feasible and how we could demonstrate to potential investors why we were worth investing in.
Given the feedback from user testing, looking forward, we plan to integrate the following features:
A Retrieval-Augmented Generation (RAG) function that can read external documents uploaded by the user and convert the information into Scend’s platform. For instance, if the user uploads a pre-existing list of buyers, the RAG function would be able to read that file and add those potential buyers to the Scend buyer list.
An AI chatbot feature that enables users to modify the buyer list by typing in their desired changes, and Scend will automatically execute the function (e.g. remove 3 buyers and add 5 buyers).
Additional elements of the M&A process: working group list, outreach, cap tables, valuation, deal management, and wiring of funds.
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