Lack of intuitive research tools
No Available platforms for sourcing that fits their specific needs
Difficulty to forecast ROI
What metrics to look at?
How to conduct Real-time data analysis & measure Success traction?
The Data Aggregation Engine finds new startups as soon as they are mentioned for the very first time on social media, and its input is used by the Data Analysis Engine to produce a report containing relevant metrics and a ROI prediction.
Find blockchain startups with good fundamentals while they are still accepting seed and private investments. Get AI-powered ROI predictions and make informed, data-driven investment decisions.
Find IDOs from your favourite market sector within seconds. Use our dashboard to quickly discover all resources provided by a company to do your research.
View your own scores and competitors in order to identify and fix your weak points. Post ads and job offers on our platform to attract the people you need. Find the right promoters for your project and compare them using unbiased metrics.
Discover potential clients when they need you the most.
Initial planning, writing initial scraper code.
Implementation of basic framework structure and fetching simple chat/channel attributes in Telegram.
Implementation of more complex features like sentiment analysis and whitepaper summarization. Writing module to detect data from pie charts via OCR.
Start writing code for competitor analysis, category labelling and roadmap analysis.
Bugfixing, data collection and optimization as well as business planning.
Definition of a sustainable business model. Team selection, creation of the project website, whitepaper and pitchdeck. Incubation by Scaleswap.
Team onboarding. Training and testing first startup success prediction model.
Foundation & Refinement
Business Model refined to align with the startup's vision and goals.
Website & pitch deck Improvement.
Improved startup success prediction AI model for more accurate results.
Technical Advancements
Software transitioned to AWS for enhanced scalability and performance.
Further development and refinement of AI models for better startup analysis.
Technical Advancements
New minor features introduced and existing ones refined.
Increase the volume of data mining to capture a wider range of startups.
Start of the integration process with large language models (LLM) to enhance CST's capabilities.
Technical Advancements
Seed Fundraising started to support the MVP and future developments.
Official public launch of the MVP web application and onboarding the first set of users to the platform.
Introduction of analytics for team performance and insights.
Start improving and adding features according to customer feedback.
Concluding strategic partnerships.
Diversification & Technical Enhancement
Extend the amount of sources for data mining and optimizing data accuracy.
Finetune LLaMa2 for success predictions based on text input.
Implement downloadable cryptographically signed PDF reports to provide verifiable and tamper-proof success predictions.
Mobile UI/UX improvements to cater to a growing mobile user base.
Advanced Analytics & Revenue Generation
Implementing tokenomics analytics, market sector classification, and competitor analysis AI models
Introducing Visual Quality Grading AI model to grade the visual quality of online presence for startups
Transition from relative success scores to absolute ROI predictions and in-depth risk analysis
Start of revenue generation through the VC plan
Market Expansion & Branding
Refine platform features based on user feedback
Introduce a retail plan to cater to a broader audience
Expanding to all market sectors, adding traditional web2 startups' scoring under the brand StartupSearchTools
Increase marketing activities to enhance brand visibility and user acquisition
Financial Stability & Future Planning
ieve a cash flow positive status, ensuring the financial stability of the platform.