Capella Systems

We created an AI that outlines long-term roadmaps to reach your objectives.
Our goal is to make charting the future possible for anyone.

We started our research in 2019.
So far we created version 0.5, V1, V2, and V1.5
We are in process of creating our launch product, V3.

Our initial target market is asset management industry with our long-term investment sequences.
It is the fastest route to immediate profitability that we identified.

Total addressable market is $8.0Tn U.S. ETF market (34.7% CAGR).
I expect we can realistically capture about $1Bn AUM in 10 years.

Our B2B software is part of the market research industry that has
total addressable market of $30.9Bn.
We can realistically capture ~$50MM of ARR in 10 years.

Conventional method and process used in data analytics is unsalable.
Rooted in our desire to understand how things work,
we create assumptions and collect data to create a model on how our world works.
All originated from a simple equation built in the 19th century.

This caused several issues:
1. Data vendor dependency.
2. Factors or assumptions may not be relevant in the future.
3. AI/ML process only found a way to brute force this process.

In summary, models does not adapt to changing world conditions.
I realized a new architecture is required.

Rather than endlessly finding factors that match our world, we reverse the entire process to extract the reasons from the world itself.
We built a "genome" map for data to create a new architecture for AGI that understands it's impacts.

Strengths:
1. Limited data vendor dependency. We only need target data.
2. Constant extraction and mapping of embedded factors.
3. Adaptable with minimal to none maintenance.
4. Outlines clear decisions required.

In small scale, crypto company "Stoic" could be our competitor.
They manage client's funds through their algorithms on web-3 chains.

At large scale, any quantitative funds can be our direct competitors.

1. We can print thousands of customized portfolios at the press of a button.
2. We can offer roadmaps in other domains such as word-trends and sentiment.
3. We offer long-term roadmaps rather than short-term.
4. Our returns are much higher compared to other quantitative funds.

Our revenue stream differs based on how we distribute our product.
Entire access to our model will be limited to B2B,
while one time use will be provided to B2C.

B2B:
Our forecast covers one year +.
To prevent one time payment and cancelation, we split the forecast payment into three parts.

One initial retainer cost: $232,000
Monthly service fee: $12,700
Cancelation fee: $232,000
(We plan to bundle 10 license)

For B2C product, we plan to take fees for each use.

Our business model is servicing cycles of uncertainty.
Initial focus will be financial services industry as it face highest level of uncertainty.

Cycle wise:
During the contraction cycle, we expect our B2B product to be in high demand. We plan to offer our sequence navigation software to businesses and governments to understand the world easier by following what our model recommends you to do.

During the growth cycle, we expect our B2C asset management product to be in demand.

1. Additional computation to accelerate our model's growth.
2. Expanding our data coverage and migrating to reliable data vendors.
3. Distribution to open our model to public.

On our comparable transaction valuation, we identified an algorithm focused on financial markets acquired for $30MM in 2023.
Our company has an algorithm that has salvage value.
We need assistance in setting up the correct entity structure.

Our non-US nationality may hinder our ability to gain government contracts.

With our first version 0.5, we won 1st place in Vanguard ETF Trading competition.
Using our winnings, we started our development of version 1 in 2019.