AI & Automation is not about tools. It is about systems.
Most companies automate tasks. We automate outcomes. Humans remain in the loop, but no longer in the way.
The deeper problem
- Fragile 'Zapier-style' rules
- Static triggers without context
- Optimizing for tasks, not revenue
- Systems that break under change
Traditional automation follows 'Rule → Trigger → Action'. It is brittle and unaware of business goals. Real value requires 'Signal → Context → Decision → Action'.
GetConvi’s point of view
We believe in Level 4 & 5 Automation: Agentic and Self-Optimizing Systems.
Our Formula: Optimal Action = argmax(Expected Business Value | Context, Time, State).
AI must be valid, revenue-aware, and operate in real-time, not post-hoc.
How we build ai-automation
Data Intelligence
Real-time enrichment and identity resolution. Calculating Intent Score = Σ (Event Weight × Recency Decay).
AI Agents & Decision Engines
Role-based agents (Sales Qualification, Market Research) with specific goals, memory, and tools. Not generic bots.
Workflow Orchestration
Event-driven pipelines using n8n and Temporal patterns. AI decides the path; workflows execute the logic.
Revenue Automation
Automating lead ID, qualification, and demo delivery, tied directly to revenue lift and attribution models.
We use the associated frameworks: Sense → React → Reach → Signal → Ray → Nova.
What this enables
- Revenue-aware operations
- Contextual, real-time decisioning
- Scalable agentic workforces
- Self-optimizing loops
Why this compounds
Our Agent Framework (Sense → React → Reach) is reusable. Once an agent is trained to qualify leads or conduct interviews, it can be deployed across any industry. Intelligence scales with data, not headcount.