Early-stage software lab based in China

Research software, cognitive tools, and AI-assisted product development.

Kingrain AI Lab builds practical software experiments with a focus on quantitative research infrastructure, cognitive training applications, and disciplined use of modern AI coding systems.

What exists QuantLab and a Cognitive Training Android app are active prototypes.
Why AWS Hosting, artifact storage, monitoring, model access, and future APIs.
Stage Self-funded, early-stage, infrastructure-first product development.
View products Why AWS
Current Focus Research platforms and mobile training apps

Lightweight, deployable systems designed for iteration, measurement, and reliability.

About Kingrain AI Lab

A small software lab building focused products from real experiments.

Kingrain AI Lab is an early-stage software lab operated under an individual business license in China. The lab owns a domain name and already runs server infrastructure on AWS Lightsail.

The current goal is to develop reliable product prototypes, validate technical workflows, and prepare cloud infrastructure that can support future users, backend APIs, model-assisted features, and hosted research artifacts.

Placeholders to replace: 654634.store, registered individual business in China, and admin@654634.store.

Business status Operated under a registered individual business in China; personal name, address, and license identifiers are not published on this website.
Web presence Owned domain and a lightweight static site suitable for AWS Lightsail and Nginx.
Current infrastructure Existing AWS Lightsail server with planned expansion for storage, monitoring, and APIs.

Current Status

Existing prototypes, not just an idea.

Kingrain AI Lab is self-funded and early-stage. The immediate focus is to operate existing prototypes more reliably, improve the engineering workflow, and prepare a small AWS-backed foundation for future product releases.

QuantLab prototype

Existing research software for quantitative strategy backtesting, experiment tracking, result comparison, and report organization.

Cognitive Training App prototype

Existing Android app focused on working memory, attention, and short-term memory training through structured exercises.

AWS server already running

Current AWS Lightsail usage provides a starting point for hosting, deployment practice, and future infrastructure expansion.

Products

Two active software projects with complementary infrastructure needs.

Research platform

QuantLab

A research-oriented quantitative strategy backtesting and experiment management platform for organizing strategy runs, test results, reports, and reproducible research workflows. It is not financial advice or an automated profit system.

Read QuantLab details
Android app

Cognitive Training App

A mobile application focused on working memory, attention, and short-term memory training through structured exercises and progress tracking.

Read app details

QuantLab

Backtesting and experiment management for quantitative research.

QuantLab is designed to help manage research workflows: data preparation, strategy configuration, backtest execution, result comparison, report storage, and experiment history. The platform is intended for research, testing, and software development purposes.

Important disclaimer

QuantLab is not financial advice, an investment advisory service, or a profit guaranteeing trading bot. Backtesting results are historical simulations and do not predict future performance.

Cognitive Training App

Structured exercises for attention and memory training.

The Cognitive Training App is an Android application for working memory, attention, and short-term memory practice. The product roadmap includes cleaner analytics, improved exercise design, secure account features, and more reliable release operations.

Working memoryTask-based practice sessions
AttentionFocused repetition and timing
Short-term memoryProgressive exercise difficulty

Why AWS

AWS is the practical infrastructure path for the next stage.

The lab needs a reliable cloud environment to host a public web presence, run lightweight product demos, store generated research artifacts, monitor prototype services, and evaluate model-assisted development workflows.

AWS Activate credits would reduce early infrastructure cost while the projects are still self-funded, allowing the lab to build a small but production-oriented setup before larger user-facing services are introduced.

OperateKeep website, demos, and future APIs online.
MeasureUse CloudWatch for logs, uptime, and operational visibility.
StoreUse S3 for QuantLab reports and experiment artifacts.
ExperimentUse Bedrock for controlled model access and AI-assisted development.

AWS Infrastructure Plan

Credits will be used for a small, realistic cloud foundation.

Kingrain AI Lab already uses AWS Lightsail for a running server. AWS Activate credits would help expand the infrastructure needed to operate prototypes more reliably and prepare for production-grade backend services.

1 Current

Static website and server operations on AWS Lightsail.

2 Near term

S3 artifact storage, CloudWatch monitoring, and improved deployment separation.

3 Next

Small backend APIs and Bedrock-assisted development experiments.

Lightsail / EC2 hosting

Host the public website, product demos, future APIs, and internal development services.

Amazon Bedrock

Access foundation models for AI-assisted development workflows and future product experiments.

Amazon S3

Store QuantLab experiment artifacts, reports, exported results, and static product assets.

Amazon CloudWatch

Monitor server health, application logs, uptime signals, and operational issues.

Future backend APIs

Deploy secure services for accounts, experiment sync, mobile app data, and admin workflows.

Reliable deployment

Use AWS infrastructure to separate experiments, staging services, and public production assets.

Credit Use Priorities

Why AWS Activate credits matter now

  • Keep the public website and product demos online while prototypes are still self-funded.
  • Store QuantLab backtest outputs, generated reports, and experiment archives in S3.
  • Add CloudWatch monitoring before opening future demos or APIs to external users.
  • Evaluate Bedrock-supported development and product experiments without large upfront model costs.
  • Prepare a small backend API environment for account, sync, and admin workflows.

AI-assisted Development Workflow

AI tools are used to speed up engineering while keeping human review central.

Development workflows use Codex, Claude Code, Gemini, and ChatGPT for implementation support, code review, documentation drafts, test planning, and architecture exploration. Human review remains responsible for product direction, security decisions, deployment, and final code acceptance.

Amazon Bedrock is planned as part of a more controlled cloud AI workflow, including model access, internal development assistance, and future experiments that may connect AI capabilities with product features.

Roadmap

Near-term milestones are focused on credible infrastructure and usable prototypes.

  1. Public company website Publish a clear web presence on the owned domain with privacy and disclaimer pages.
  2. QuantLab research workflow Improve experiment storage, report generation, and reproducible backtesting runs.
  3. Cognitive Training App iteration Refine Android app experience, analytics, release process, and future account services.
  4. AWS-backed services Prepare secure backend APIs, monitoring, S3 artifact storage, and Bedrock experiments.

Contact

Contact Kingrain AI Lab

For AWS Activate verification, product questions, or business inquiries, please use the contact email below.

admin@654634.store

Domain: 654634.store

Public operator: Kingrain AI Lab

Registered operator details are supplied through AWS Activate or verified business channels when required.