Qail.AI is an applied research system for autonomous lead qualification and marketing efficiency
We are building a new class of marketing infrastructure: a coordinated network of specialized agents that inspect, verify, and qualify every agent or human lead in real time, protect ad algorithms from corrupted signals, and connect marketing activity to real business outcomes.
We are pushing the practical limits of automation in real-world operations, delivering broad, general-purpose lead intelligence rather than isolated fraud checks or scoring features.
Multi-agent system
The platform currently operates with dozens of specialized agents, each responsible for a specific capability such as data validation, intent analysis, behavioral modeling, device and pattern recognition, algorithm feedback control, and quality assurance.
New agents are added continuously. Together, they function as a single system that evaluates, filters, and refines inbound demand with minimal human involvement, before bad data reaches sales teams or ad platforms.
Agents: UI or API
Qail.AI’s agents are available in two forms:
- Qail Interface
Agents operate inside a unified dashboard where teams can review, approve, and analyze lead quality in real time. - API Integration
Agents can be triggered directly from external systems such as ad platforms, CRMs, forms, chat tools, or call tracking software.
Leads remain fully accessible and editable in downstream systems after agent processing.
Both methods use the same underlying architecture and produce consistent, auditable outputs.
The output is not a filtered lead
The system does not block bots or label leads.
Agents validate data, assess intent, detect dark patterns, classify “intend” prospects, and decide whether a conversion signal should be sent back to ad platforms.
Only verified, high-quality signals are allowed to train ad algorithms.
Human intervention is required only when teams choose to override or refine decisions.
Designed to be extended for its users
Qail.ai is built as an adaptive infrastructure layer.
Users can define custom rules using simple configuration, no code required, so the agent network evaluates leads according to their specific business reality.
The architecture is designed to adapt to markets, not enforce assumptions.
Progress is measured by autonomy
Our primary metric is the Autonomy Ratio:
The percentage of lead qualification and validation actions executed by agents versus humans.
Each release increases the share of inbound demand processed autonomously—reducing manual review, wasted sales effort, and algorithmic pollution.
For multiple high-volume use cases, Qail.ai already operates at full autonomy.