Principal Consultant – AI Sales Call Intelligence & Contact Center Funnel Design (Remote)

Role Overview

Project Overview

We are a high-growth, technology-forward marketing and data company operating in the U.S. mortgage sector. We generate approximately 10,000 inbound consumer leads per week across multiple enterprise clients.

We are building a next-generation Closed-Loop Funnel Intelligence System that will:

• Analyze millions of inbound sales call transcripts
• Classify each call into a structured funnel stage
• Extract layered diagnostic flags
• Identify root-cause failure patterns
• Feed competitive and operational reporting
• Power an AI-driven performance optimization engine

We are seeking a senior contact center architect or transformation expert to help us design the foundational taxonomy and diagnostic framework correctly.

This is not a QA tuning project.

This is a structural architecture engagement.

The Core Challenge

We are designing a scalable, multi-client call classification system that will:

1. Identify the highest fully completed stage of each sales call

2. Differentiate controllable vs uncontrollable outcomes

3. Separate execution failure from structural constraints

4. Enable competitive loss intelligence

5. Integrate with AI/LLM-based transcript analysis

Our working funnel model currently includes stages such as:

• Live Engagement
• Process Control Established
• Solution Presented
• Mutual Advancement

Each call may include layered diagnostic flags across categories such as:

• Sales execution failure
• Customer objection type
• Competitive displacement
• Policy or eligibility constraints
• Capacity or operational breakdown

We need an experienced operator to pressure-test, refine, and architect this system so it scales across millions of calls.

Scope of Work

The consultant will: Funnel Architecture Validation

• Evaluate and refine our stage-based model
• Stress test "highest fully completed stage" logic
• Ensure stage definitions are operationally defensible
• Align model with large-scale BPO best practices

Diagnostic Taxonomy Design

• Design hierarchical flag structures
• Separate signal from noise
• Define primary vs secondary diagnostic layers
• Ensure flags map cleanly to action

QA & Calibration Framework

• Recommend calibration methodology
• Define consistency controls (inter-rater reliability approach)
• Advise on AI + human hybrid governance model

Scalability & Implementation Guidance

• Design for cross-client deployment
• Anticipate edge cases
• Provide guidance on governance and maintenance
• Ensure the framework supports downstream reporting and performance management

Ideal Consultant Profile

We are seeking someone with: - 12+ years in contact center operations, architecture, QA, or transformation

• Experience inside large BPOs (e.g., Concentrix, Teleperformance, Alorica, Sitel)or
• Experience at contact center technology providers (e.g., NICE, Genesys, Five9, Talkdesk)
• Direct experience designing call classification systems or QA frameworks
• Sales call center expertise (not customer support only)
• Familiarity with transcript-based analysis
• Experience integrating AI into call center workflows (strong plus)

Executive-level candidates preferred (former Director, VP, or Principal-level consultant).

Deliverables

By the end of the engagement, we expect:

• A validated and clearly defined funnel stage architecture
• A production-ready diagnostic taxonomy structure
• A documented classification logic model
• QA governance and calibration recommendations
• A scalability blueprint for multi-client rollout

Engagement Details

• Duration: 8-12 weeks
• Estimated commitment: 15-20 hours per week
• Remote
• High executive visibility

This project will directly shape the core architecture of our AI-driven performance engine.

What Success Looks Like

A system that: - Cleanly separates execution failure from structural constraints

• Enables actionable diagnostic reporting
• Maintains consistency at scale
• Supports AI automation without losing operational rigor
• Can be deployed across multiple enterprise clients