productizing PC-BE

[hyunjimoon]

Title:

Improving Entrepreneurial Decision Making with Bayesian Reasoning and Intelligent Assistance Distribution channel: Salesforce app exchange platform

Pain Point:

Addresses the complex, fast-paced decision-making challenges faced by entrepreneurs in product development, market selection, and operational execution, where traditional models often fall short due to limited information and rapidly changing environments.

Value Proposition:

1. Provides a decision-making toolbox that complements existing frameworks while improving information processing using Bayesian reasoning.
2. Offers an iterative approach to experimentation, observation, inference, and decision-making, allowing entrepreneurs to systematically update beliefs and strategies as new information is gathered.
3. Integrates Large Language Models (LLMs) for data acquisition and cognitive support in carrying out complex decisions.

Technology:

1. A probabilistic program that can formalize and automate tasks such as designing business models, valuing equity, tuning parameters, and simulating rare events.
2. Integration with LLMs to translate English to probabilistic codes, answer user questions, and provide cognitive support.
3. Optimization and simulation tools to gain insights into dynamic, iterative decision processes.

Key Features:

1. Marketing Experiments Module: Helps entrepreneurs balance product capabilities with market needs, using metrics like Market Adoption and Revenue Potential.
2. Sourcing Experiments Module: Assists in supply chain strategy decisions, considering factors like in-house vs. outsourced production and local vs. global manufacturing.
3. Bayesian updating framework for continuous learning and adaptation.

Customers:

1. Corporate Innovation Labs
2. Business Schools and Universities
3. Startup founders and entrepreneurial teams
4. Venture capital firms and angel investors

Use Cases:

1. Product-market fit experiments for pivoting decisions (e.g., EV startup choosing between different range options and target markets)
2. Supply chain sourcing decisions (e.g., Tesla Roadster case study on manufacturing strategy)
3. Continuous adaptation of business models in fast-changing technological and market landscapes

[hyunjimoon]

angie's first pitch

BE x PC's value proposition: start with certain assumptions (prior), social product partnership and market, we

• experiment and learn about updating business models based on market data , learning from partnership outcomes
• convince people about these things data-driven investor pitches, persuading partners with mutual benefits
• figure out how to teach risk assessment training, relationship management training

charlie's first feedback

1. if you're going to be have an entrepreneurial business venture, you need a crisp idea. Who's the customer, who's going to pay for this product, what's your thinking there?

based on below (credit: orbit): corporate innovation labs. Especially for their collaboration with startup to hedge risk

#	Title/Descriptions	Corporate Innovation Labs	Business Schools and Universities	Startup Founders and Entrepreneurial Teams
1	End User	Innovation managers, R&D teams	Professors, students, researchers	Founders, product managers, startup teams
2	Task	Enhance decision-making in innovation projects	Teach and research entrepreneurial decision-making	Make informed decisions in product development, market selection, and operations
3	Benefit	Improved project outcomes, faster innovation cycles	Enhanced teaching and research capabilities	Better decision-making, increased chances of startup success
4	Urgency of Need	High - Constant need for innovation and competitive advantage	Medium - Academic cycles, but growing interest in entrepreneurship	High - Critical for startup survival and growth
5	Example End Users	Google X, IBM Research	MIT Sloan, Stanford GSB	Early-stage startups, Y Combinator alumni
6	Lead Customers	Google X, IBM Research	MIT Sloan, Stanford GSB	Y Combinator, Techstars
7	Willingness to Change	High - Open to new tools and methodologies	Medium - Academia can be slow to change, but open to new research tools	High - Startups are agile and open to new solutions
8	Frequency of Buying	Medium - Purchase tools and software periodically	Low - Budget cycles may limit frequent purchases	High - Constant need for new tools and solutions
9	Concentration of Buyers	Medium - Several large innovation labs	Medium - Many universities and business schools	High - Numerous startups
10	Other Relevant Market Considerations	High growth industry, high R&D budgets	High employee turnover, high growth in entrepreneurship programs	High growth industry, high failure rate of startups
11	Size of Market (# of end users)	1K's	10K's	100K's
12	Estimated Value of End User ($1, $10, $100, $1K)	$1K	$100	$1K
13	Competition / Alternatives	Traditional decision-making tools, internal R&D tools	Traditional academic tools, other research software	Traditional decision-making tools, other startup tools
14	Other Components Needed for a Full Solution	Integration with existing R&D tools, training and support	Integration with academic tools, training and support	Integration with existing startup tools, training and support
15	Important Partners	Salesforce, R&D tool providers	Salesforce, academic tool providers	Salesforce, startup tool providers
16	Other Relevant Personal Considerations	Alignment with corporate innovation goals, ease of use	Alignment with academic goals, ease of use	Alignment with startup goals, ease of use

charlie's second feedback

2. there must be a huge amount of competition in adding AI to CRM products. So we would need a specific angle

ai based query in relational database (tech. being developed in PC lab) would be our specific angle. probabilistic representation can mitigate the variability of LLM replies which is becoming more problematic. Moreover, Matin and Joao (from PC lab) were interested in relational database querying technologies. It's my guess there are existing competitors for probabilistic reasoning for tabular data, but much less in relational data.