Founder-led company building research software for checking on-chain signals.
Company brief for Qinlong Intelligence.
Qinlong Intelligence is an Asia-based company building an on-chain research system for BSC, SOL, and ETH signal review. The system records how a candidate was found, checked, blocked, or moved into validation before it is treated as a stronger research candidate.
The public company website explains product scope, technology, roadmap, security, and contact.
Company-domain contact for program review, investor, and technical discussions.
Founder with three years of hands-on BSC, SOL, and ETH on-chain research experience.
Product in plain language.
Qinlong is not a consumer trading app. It is a review system for messy chain signals. A raw alert is only the start; Qinlong keeps the surrounding evidence visible so a human can see what happened and why a candidate did or did not move forward.
What it watches
BSC, SOL, and ETH research lanes, wallet groups, token or pool context, route evidence, liquidity state, and sellability context.
What it records
Signal source, candidate state, quoteability checks, blocker reasons, validation records, review notes, and dashboard health.
What it improves
Cleaner evidence trails, less mixed data across chains, faster review of weak paths, and clearer operational boundaries.
Current system status.
Qinlong already runs internal research lanes and dashboards. The current work is to improve validation quality, monitoring, evidence storage, and report generation before wider operational use.
Built now
BSC / SOL / ETH lanes, candidate records, route and liquidity checks, blocker logs, validation ledgers, and internal dashboards.
Next 30 days
Improve validation evidence quality, route diagnostics, lane health review, and blocker attribution.
Next 90 days
Package clearer dashboard walkthroughs, audit trails, evidence exports, and plain-language review reports.
Where AI is used.
Qinlong uses AI after evidence has been recorded. The model writes short notes, labels common blocker reasons, and helps prepare reports. It does not override route checks, blocker rules, validation quality, or human review.
Signal notes
Summarizes raw candidate evidence into short notes for review.
Blocker classification
Helps label why a candidate failed, stayed blocked, or needs more evidence.
Reports and review
Supports report generation, strategy review, and later learning from preserved evidence trails.
Cloud usage plan.
Qinlong runs cloud-based research workloads for signal collection, route checks, dashboards, monitoring, and report generation. Program credits would be used immediately after approval for compute, data processing, storage, monitoring, and review-note generation.
Compute workers
Data processing
Monitoring
Evidence storage
Report generation
AI review support
Boundaries.
No custody
Qinlong does not ask website visitors to deposit assets, connect wallets, or transfer assets.
No public asset issuance
The company website is not used for public digital-asset issuance or asset-based fundraising.
No outcome promise
Public materials do not promise investment outcomes, trading performance, or advisory services.
No current automatic live-order execution
The current public product boundary is research, review, validation records, and operating controls.