AI WORKFORCE
AI Workforce
Scoped after Discovery Call • 8-16 weeksAI is introduced as operational labor — not chatbots or standalone experiments.
Boundaries, escalation paths, and accountability are designed in from the start.
AI is not a feature. It's operational labor with constraints.
AI is not magic, and it's not a chatbot. In operational contexts, AI is labor—it does work, makes decisions, and takes actions. Like any worker, it needs clear boundaries, accountability, and oversight.
AI Workforce Deployment integrates AI agents into your orchestrated workflows. These agents handle specific operational tasks: qualifying leads, processing documents, managing follow-ups, answering questions.
The key difference from typical AI deployments: our agents operate inside systems, not outside them. They're governed by the same orchestration rules as everything else.
Why This Matters
- -Ungoverned AI creates unpredictable behavior at scale
- -AI without boundaries will make decisions you didn't authorize
- -Chatbots are not operational systems—they're interfaces
- -AI needs escalation paths just like human workers do
Common Symptoms
Signs you might need this service
What we do
- —AI intake and qualification agents
- —Follow-up and status update agents
- —Document triage and routing
- —Voice AI where appropriate
- —Defined escalation rules
What you get
- —24/7 operational capacity
- —Consistent response quality
- —Reduced manual workload
- —Clear human oversight
How It Works
Task Mapping
We identify which operational tasks are suitable for AI handling based on risk, complexity, and value.
Boundary Definition
We define what AI can and cannot do, what requires escalation, and what triggers human review.
Agent Architecture
We design AI agents with specific roles, capabilities, and constraints for each task type.
Orchestration Integration
We embed AI agents into existing workflows, governed by the same state machines and rules.
Training & Calibration
We tune agent behavior using real operational data until performance meets standards.
Monitoring & Escalation
We implement AI-specific monitoring: confidence scores, drift detection, and escalation triggers.
The Transformation
Before
- -Humans do repetitive cognitive tasks
- -Quality varies by who handles work
- -No coverage outside business hours
- -AI attempts feel experimental
- -No visibility into AI decisions
After
- +AI handles defined operational tasks
- +Consistent quality with clear standards
- +24/7 operational capacity
- +AI governed inside systems
- +Full audit trail of AI actions
In Practice
Case Example
Real-World Application
Scenario
Commercial Insurance Brokerage
The Problem
Brokers spent 2+ hours daily on initial lead qualification—asking the same questions, gathering the same documents. High-value time was consumed by low-value repetition.
The Solution
We deployed an AI qualification agent that handles initial outreach, asks qualifying questions, gathers required documents, and scores readiness. Brokers only engage when leads are qualified and complete.
The Outcome
Brokers reclaimed 15+ hours weekly. Lead-to-quote time dropped 60%. The AI handles 24/7 intake while maintaining quality standards.
Typical Results
40-70%
Tasks Automated
Of targeted repetitive work
<2 min
Response Time
24/7, including off-hours
15-25%
Human Escalation Rate
Complex cases to humans
Is This Right For You?
Ideal For
- +Companies with high-volume repetitive tasks
- +Teams with orchestration already in place (L2)
- +Organizations ready for AI as labor, not experiments
- +Businesses needing 24/7 operational capacity
Not Ideal For
- -Companies without stable orchestration (L2 first)
- -Tasks requiring deep domain judgment
- -Organizations wanting AI without governance
Engagement Type
Deploy
Ready to get started?
Start with clarity. No commitment required.