AI Voice Agent Deployment
Deploying an AI Voice Agent to capture 100% of inbound calls, schedule appointments, and answer FAQs around the clock — eliminating missed calls and freeing staff from phone duty.
The Challenge
Every Missed Call Is Lost Revenue
A service-based business was missing 30-40% of inbound calls during peak hours and 100% after business hours. Each missed call represented potential revenue lost to competitors who answered first.
Staff were interrupted from their primary work to handle phone calls, and callers received inconsistent information depending on who picked up. There was no after-hours coverage, meaning every call outside business hours went to voicemail — and most of those callers never called back.
The business needed a solution that could answer every call, provide accurate information, and book appointments without adding headcount or pulling staff away from billable work.
The Solution
An AI Agent That Knows the Business
Rex Black configured the AI Voice Agent with the company's full information set — services, pricing, hours, locations, FAQs, and scheduling availability. The agent was connected to the calendar system and trained on the company's tone and protocols. Deployed within 3 weeks from kickoff to live production.
What we delivered
- +AI Voice Agent configured with full company information including services, pricing, hours, and locations
- +Calendar system integration for automated appointment scheduling
- +FAQ knowledge base training on company tone, protocols, and common inquiries
- +Call routing logic for scenarios requiring human escalation
- +Post-call summary and lead capture delivered to the team in real time
- +Deployment, testing, and go-live within a 3-week timeline
The Results
By the Numbers
Measurable outcomes from AI-powered phone coverage.
Missed Calls
Every inbound call answered, day or night
Deployment
From kickoff to live production in under a month
Coverage
Full phone coverage without staffing constraints
Scheduling
Appointments booked directly into the calendar system
Outcome Summary
Zero missed calls. Every inbound call is answered with accurate, consistent information regardless of time of day. Appointments are booked automatically around the clock, directly into the company's calendar system.
Staff were freed from phone duty and redeployed to billable work. The AI Voice Agent handles the full spectrum of caller needs — from simple FAQ responses to appointment scheduling — with escalation to a human only when genuinely needed.
Facing a Similar Challenge?
Whether you're losing calls or looking to automate front-line operations, we can help you deploy AI that works from day one.
Resources for this kind of program
Reading material that goes deeper on the methodology behind this engagement.
- Whitepaper
Starting AI Adoption: A Sequence for Mid-Market Engineering Teams
The order of operations we use with mid-market engineering teams that have been told to ship AI and do not know where to start. Six stages, named exit criteria, the anti-patterns that predict failure, and the first-90-days view that ties architecture, evaluation, and model economics into a coherent adoption sequence.
Read → - Whitepaper
Evaluation Before Shipping: How to Test an AI Application Before It Hits Production
The release-gate playbook for AI features. Covers the five evaluation dimensions, how to build a lean golden set, where LLM-as-judge is trustworthy and where it lies, rollout mechanics with named exit criteria, and the regression suite that keeps a shipped AI feature from quietly rotting in production.
Read → - Whitepaper
Choosing the Right Model (and Knowing When to Switch)
A practical framework for matching LLM model tier to task. Covers the four axes (capability, latency, cost, reliability), cascade routing patterns that cut cost 60 to 80 percent without measurable quality loss, switching costs you did not plan for, and the worked economics at 10K, 100K, and 1M decisions per day.
Read → - Whitepaper
Workflow or Agent? A Decision Framework Before You Architect Anything
Most production 'agents' are workflows that overshot. This paper distinguishes deterministic LLM pipelines from autonomous agents, names the four questions that decide which one to build, and covers the failure modes specific to each path. Includes the 'earned autonomy' principle for promoting workflows to agents only after instrumentation justifies it.
Read → - Whitepaper
The Case for Investing in Testing: A Board-Level Argument for Enterprise Test-Function Capability
Enterprise organizations regularly face the question of whether to invest in their test-function capability — in hiring, in tooling, in automation infrastructure, in process maturity. The question is often answered by default rather than by analysis, and the default is under-investment relative to the economic case. This whitepaper presents the board-level argument for investing in testing, structured around the four business outcomes that robust testing produces, the cost curve that makes early investment asymmetrically valuable, and the specific organizational patterns that distinguish organizations that treat testing as strategic from those that treat it as overhead.
Read → - Whitepaper
Deciding When to Bring in External Help: A Framework for Training, Consulting, Staff Augmentation, and Outsourced Testing
Most enterprise decisions to bring in external testing help succeed or fail based on whether the right form of help was selected, not on whether the particular vendor performed well. This whitepaper covers the four categories of external testing help — training, consulting, staff augmentation, and outsourced testing — and the decision framework that matches each form to the problem it solves, with cost, capability, and exit-cost implications for modern enterprise test programs.
Read →
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