Satigi Case Study

Summary

This case study focuses on a startup I co-founded, aimed at using AI to speed up the research synthesis process for UX designers, a task that is time-consuming but valuable.
Key challenges included determining the essential features for an MVP and discerning between the buyer and the user.

The following case study is about the initial stages of a startup I co-founded. Our primary aim was to use AI to help UX designers speed up their UX research synthesis process.

Timeline discussed in this case study: 4-6 weeks
Startup timeline: About a year
My Role: Co-founder (1 of 2) / Desiginer / Developer

Background

  • UX Designers have to do many jobs at once: research, usability testing, UI design, content design and more
  • One of the jobs that consumes a lot of time, provides a lot of value, but is not immediately justifiable to the client is the research work. Specifically the synthesis of the research.

End Results/Findings

  • Determining what to include in the MVP for an application can be challenging given the time constraints
  • Determining a price point can be difficult
  • The difference between the buyer and the user is crucial to discern
  • AI is and will be a productivity booster

Key questions to ask before starting out

  • Is your solution at least 10 times better than the current way your ICPs (ideal customer profile) are solving it? In time, money saved or other.
  • Why is now the right time to solve this problem? Technological, regulatory, behavioural shift?
  • Is your user the same as your customer?
  • If they are different, do you have access to the people that would actually pay? (Can you name 10 potential customers?)
  • Why or how would your first 10 customers tell other potential customers?

Research

We interviewed about 50 Product Specialists in a space of 2 weeks.

Target Audience

UX Designers, User Researchers, Design Agencies, product managers

Objective Key Results

•  To understand how designers conduct user interviews and usability studies
•  To understand their current frustrations in their job
•  To understand how designers synthesise data from user research

Questions To Ask Participants

  • As a UX Designer, what would you say are the top 3 frustrations or issues you have with your job?
  • Could you walk me through the process of how you conduct user interviews and synthesise the data?
  • Could you walk me through how you create affinity diagrams and what tools you use?
  • What about user personas?
  • When you have synthesised the data, how do you come up with solutions for the problems that arise?
  • Do you ever turn to psychology to come up with solutions or to explain to stakeholders?
    How do you often do this?
  • Have you ever used the software Dovetail?(heymarvin is also a competitor)
  • What were some of the manual work that you did that took a lot of time?

Research Results

Direct Quotes from UX Designers include:

General observations from the research include:

Prototypes

The following is a rough sketch of prototypes i made in order to guide my thinking for the MVP.

Homepage

The initial designed aimed at showing the features the product provides as well as the copy

Projects Page

Showing the number of projects a designer in an agency is working on

Dashboard View

Dashboard shows a view of ongoing research or completed research, it can be viewed by multiple parties including senior stakeholders

Affinity Diagram

Automatically generated affinity diagram based on the transcripts provided.

Startup Direction

We decided to call our startup “Satigi”. Satigi was derived from the sentence Save Time Get Insights. Which is fitting to what the application does.

Our target market was design agencies as it would be easier to get them to make a commitment than designers who work in corporate functions.

Once we completed our user interviews and market research, we decide that the startup should provide the following offerings:

High Fidelity Designs- 1st Iteration

Figma- Workflow Page

  • We decided to use the colour blue as blue invokes:
  • Intelligence and Expertise
  • Dependability
  • Professionalism
  • The workflow page shows 3 main menu items- Projects, Team, Client as this is a typical segmentation for an agency’s workflow
  • In this flow we decided to start from the projects page as it helps to showcase the flow of the primary use case

Figma- Project Page

  • The projects page was designed to show a typical agency’s view. It showcases the different projects they may be working on.
  • Not too much emphasis was put on this page (e.g. should we segment it by clients or projects) as this was not our primary focus at the time

Figma- Dashboard

  • This page was our main selling point
  • The idea was to show all of the insights from the interviews conducted on that project in one view
  • We also wanted to make the page interactive, so you can click on some comments next to each theme and find out more
  • You can also click each theme and it will take you to the affinity map
  • Additionally you can ask questions about the data on the dashboard

Figma- Affinity Map

  • The affinity mapping was created to help users automatically sort themes so they don’t have to
  • The page also gives the user the ability to determine how many groupings they want. At the top, it defaults to “5” but a user can change this to their preference

Testing Results

Unmoderated testing with agencies yielded positive feedback, leading to minor design tweaks. Within four days, two letters of intent were secured as well as endorsements from three reputable advisors, signalling strong demand for the product.

Unmoderated Testing + Letter of Intent + Endorsement

General Introduction

  • In a race for time, we decided to test some of the designs before proceeding with development
  • As mentioned previously, the design direction was to target design agencies
  • We did some prototype testing on potential customers to get some general thoughts on the application and any feedback they had
  • We also used the testing as a way to guide our Proof of Concept app which would ultimately guide our MVP

Results

  • The interviewees were generally pleased with the initial prototypes we presented to them. A few tweaks to the journey was recommended but generally they were pleased
  • We were able to get 2 letter of intents from 2 agencies in 4 days which is important for investors as it signals that there is demand for the product
  • We were also able to get 3 reputable advisors that could help us on our journey

Updated Design

In the updated version, as seen in the video, we improved the flow of the initial mockups. Most notably:

Proof of Concept

I integrated OpenAI's GPT-3.5 Turbo API for document synthesis, using Pinecone to manage token limits, which reduced as OpenAI expanded capacity. Results were generally reliable despite some variability. I also used Bubble for building the MVP, which, despite limitations, proved effective for the proof of concept.

OpenAI API

  • GPT 3.5 Turbo
  • With the introduction of GPT 3.5 Turbo, I was able to connect the API from OpenAI to the application for our proof of concept
  • On the front end as soon as the user uploads the documents, I created a series of actions to be taken on the backend to facilitate the synthesis
  • Prompt engineering proved extremely useful in order to get the desired results.
  • Limitations
  • Token size was a big limitation when the API was first introduced.
  • In order to solve for this, i used the pinecone API to help split the document into different tokens
  • As time went on, OpenAI generally started to increase the token limit and the dependence on pinecone reduced
  • LLMs are probabilistic not deterministic, getting the same results every time is not certain but most of the time it was good

Bubble Low Code App Builder

  • Web Application Builder
  • Bubble is a popular web application builder that is perfect for building web applications for Proof of Concepts or MVPs
  • I had some familiarity with the tool which is why I chose it
  • It is quite powerful and highly customisable
  • Limitations
  • Cannot extract the code
  • Self learning and resolution. The online forum can help but sometimes you have to figure out the solution yourself
  • It is quite powerful and highly customisable
  • Results
  • Got it to work for the proof of concept

Conclusion

Summary

  • Satigi aimed to help sole UX Designers speed up their User Research process, from recruiting participants to insights gained
  • Satigi’s MVP aimed to reduce the amount of time a sole UX Designer on a team spent synthesising transcripts and data collected from user testing

Lessons

  • This project was an exercise in how to assess good ideas
  • It was also an exercise on how to guide potential customers to use an application they have not experienced before
  • Given it was a startup, it was an exercise on how to work in an ultra fast paced environment
  • An exercise in figuring things out yourself as what you are building is entirely new