The Point AI Case Study

The Point is a pioneering platform designed to assist founders in effectively validating their startup ideas. It addresses the critical need for structured idea validation, often overlooked due to a rush towards solution development without a deep understanding of the problem and market.

Duration

3 weeks

Providing founders with guidance through a validation process with zero man hours.

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Problem

Client's challenge

Idea validation is a nuanced process that requires thorough knowledge of specific methodologies or a skilled facilitator's guidance. Founders frequently leap into developing solutions without a solid grasp of the problem they aim to solve or an understanding of their target market.

The Point’s problem validation step encompasses two pivotal phases:

Exploration: This phase is dedicated to shaping the idea into a well-defined problem statement. It involves delineating the problem landscape by identifying key actors, their motivations, and underlying assumptions.
Lean Market Research: This stage encompasses both quantitative and qualitative analyses to outline the target market's characteristics, customer personas, and their journeys, aiding founders in pinpointing their potential customers.

Solution

Proposed solution

Instead of leaving the user to navigate the validation process alone, an AI assistant aids them in various tasks, reducing the time needed to validate an idea and eliminating the necessity for an experienced facilitator to guide the process.

The solution used OpenAI Platform and the assistant (with GPT3.5 model) was given knowledge of the phases and methodologies from the validation process, an example of a validated start-up and how the information was structured, and instructions on how the generated information should be formatted.

During the Exploration phase, the assistant is capable of:`

Generating startup names.
Crafting a concise problem statement from the user's idea and problem-related inputs.

In the Lean Market Research phase, it provides:

Customized discovery interview scripts for each actor identified within the problem landscape, considering their specific attributes such as industry, size, and digital profile.
Creating user personas for each actor category based on interview data.

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Linnify's Approach

Team involvement

The backbone of this approach was a meticulous preparation of instructions for the AI, ensuring it generated content that was both relevant and valuable. Balancing creativity with the need for structured outputs required a nuanced approach, particularly in tasks like naming startups and creating personas, which demanded a high level of originality. Conversely, generating interview scripts necessitated a more formulaic approach, adhering to specific formats and guidelines to ensure comprehensiveness and coherence.

To fine-tune this balance, Linnify adopted a trial and error methodology, experimenting with various instruction configurations and inputs to optimize the AI's performance across different scenarios. A key aspect of this experimentation was adjusting the 'temperature' setting of the model, a parameter that controls the balance between randomness and determinism in the AI's outputs. This allowed for a tailored approach that varied from highly creative and divergent outputs to more predictable and structured ones, depending on the task at hand.

This AI-assisted approach not only made the idea validation process more efficient and less reliant on experienced facilitators but also enriched the user experience by providing a more dynamic and interactive engagement, akin to a real-world consultation.

Development Timeline

The entire solution, from research to development, was executed in 3 weeks by a collaborative effort between a tech lead and a backend engineer. They focused on refining the model's instructions and integrating the AI assistant into the existing platform.

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