Kerdos AI is not a consultancy, agency, content site, or outsourced development shop. It is a founder-led product company building a web platform that helps users turn fragmented market data into structured research signals, forecast scenarios, and risk-aware summaries.
Public facts for startup-program reviewers.
Founded: November 2025
Founded: November 2025Stage: Working MVP / public beta.
Stage: Working MVP / public beta.Business model: Subscription plans and credit-based access to AI research workflows.
Business model: Subscription plans and credit-based access to AI research workflows.Funding plan: Preparing to seek venture funding.
Funding plan: Preparing to seek venture funding.Google Cloud credits: Has not received Google Cloud startup credits beyond the standard free trial.
Google Cloud credits: Has not received Google Cloud startup credits beyond the standard free trial.Startup type: Digital-native proprietary software platform, not an agency, consultancy, dev shop, or content site.
Startup type: Digital-native proprietary software platform, not an agency, consultancy, dev shop, or content site.The product is built for self-directed investors, active traders, and independent market researchers who need faster ways to review equities without losing sight of risk, uncertainty, and source context.
Market research is scattered across price data, filings, news, sentiment, and analyst narratives.
Retail and independent researchers lose time reconciling conflicting signals before they can form a view.
Traditional dashboards often show more data without explaining regime, risk, source context, or uncertainty.
Kerdos AI is already available as a working MVP with public product modules, authenticated user flows, payments, and backend workers that support research signals and forecasts.
A market dashboard that combines regime detection, stock rankings, sentiment, and signal verification in one view.
Daily ranked research candidates with risk labels, catalyst summaries, confidence scores, and source context.
A forecast workspace that runs multi-perspective AI analysis with bull, bear, and neutral scenarios.
Kerdos AI is designed as a structured software workflow for public-market research. Each module adds a deeper layer of context, moving users from market overview to focused scenario analysis with traceable assumptions.
Start with regime context, daily rankings, sentiment, and signal verification to understand where the market is concentrated and which areas deserve attention.
Use Recommendations to compare ranked companies, risk labels, catalysts, confidence scores, and research horizons before selecting opportunities for deeper review.
Move interesting candidates into Forecasts to examine bull, bear, and neutral scenarios, assumptions, risks, and possible outcomes before making your own decision.
The platform is built as owned software with productized user workflows, billing, data pipelines, and AI research systems.
Equity profile, historical price, fundamentals, ranking, and market-context data are normalized behind Kerdos services.
AI flows synthesize market evidence into structured research summaries, forecast scenarios, and signal explanations.
Risk disclosures, methodology notes, AI limitations, regulatory-status pages, and informational-only warnings are part of the product surface.
The app is built with Next.js, TypeScript, Firebase Auth, Firestore, Cloud Run workers, BigQuery, Cloud Storage, and Stripe billing.
Kerdos AI is built around a proprietary product and repeatable software workflows, not one-off client services.
Kerdos AI builds and operates its own proprietary web platform.
The product includes authenticated workflows, billing, AI research pipelines, market-data processing, public modules, and cloud-native infrastructure.
It sells access to software workflows, not consulting, agency work, brokerage, custody, personalized investment advice, or content publishing.
Kerdos AI is operated by Michael Keskinidis from Germany. The founder is responsible for product engineering, cloud infrastructure, AI workflow development, data-pipeline operations, and compliance-led product documentation.
Michael is an AI full-stack engineer with a cross-stack background across Java / Spring Boot, Flutter, React / Next.js, applied AI, LLM workflows, and product delivery.
His work spans enterprise software, mobile products, backend services, integration-heavy systems, dataset preparation, evaluation workflows, practical AI application design, debugging, and hands-on release execution.
