Executive Summary
From missed calls and declined work to an always-on front-counter operating system
Independent repair shops lose revenue at the front counter long before a technician touches a vehicle: missed calls, incomplete intake, unworked estimates, declined repairs, and after-hours demand that disappears by morning. I led the design and pilot of Bradham AI Front Counter — a governed combination of AI Receptionist and AI Service Advisor that answers every contact, captures structured customer and vehicle data, follows up on open work, and escalates sensitive decisions to humans.
The Business Problem
Bradham's front counter was constrained by the same economics affecting independent repair shops everywhere: a loaded human service advisor costs approximately $94,250–$152,250 annually, yet the shop still missed or delayed calls during busy periods, had no systematic follow-up on open estimates, and relied on staff to translate technical DVI findings into plain language one customer at a time.
The operational gap was also a revenue gap. In the pilot baseline, after-hours calls depended on voicemail, open estimates could sit without a structured sequence, and declined work was effectively deferred revenue with no reliable recovery motion. The business needed more than a chatbot — it needed a governed front-counter operating model with clear boundaries around safety-critical work, high-value repairs, complaints, and human handoff.
The Solution
I designed and delivered two coordinated AI roles. The AI Receptionist handles inbound phone, SMS, and web chat, captures customer and vehicle information, answers routine questions, books standard appointments, sends confirmations, and creates the structured record in the shop's workflow system. The AI Service Advisor handles deeper intake, explains DVI findings in plain language, presents estimates, follows up on open and declined work, and notifies customers when work is approved or complete.
The platform was deliberately designed as a first responder, not a gatekeeper. Standard bookings below the defined threshold can complete automatically; safety-critical issues, complaints, high-value repairs, and low-confidence interactions require human approval. Every workflow has an escalation path, every estimate has a follow-up state, and customers can reach a person when they need one.
How It Works
Capture
Every call, SMS, and web chat is answered, transcribed, and converted into a structured customer and vehicle record.
Assist
AI explains service findings, presents estimates, books standard work, and communicates in Bradham's plain-language voice.
Recover
Open estimates and declined work receive systematic, time-bounded follow-up until converted, closed, or intentionally stopped.
Architecture & Governance
The platform separates intake, reasoning, workflow execution, and governance so the shop can automate routine work without allowing the AI to make unsafe or commercially inappropriate commitments.
System Architecture — Contact-to-Revenue Flow
Five stages · governed human escalation
Phone, SMS, web chat, after-hours calls, service questions, and appointment requests
Intent classification, customer identity, vehicle details, appointment availability, and structured capture
History, open estimates, DVI findings, vehicle profile, shop schedule, and workflow-system records
Safety-critical, high-value, complaint, and low-confidence workflows require designated staff review
Appointments, confirmations, estimate sequences, completion notices, and workflow-system activation
Privacy, audit & voice-of-brand rail runs beneath every stage — Bradham owns its customer data, every action is logged, and every response follows the shop's approved tone and escalation policy.
- AI Receptionist layer for phone, SMS, and web-chat intake, with structured customer, vehicle, and intent capture.
- AI Service Advisor layer for DVI explanation, estimate presentation, approvals, completion notifications, and declined-work recovery.
- Tekmetric/workflow-system integration for RO creation, appointment booking, customer history, and status synchronization.
- Governance controls: safety-critical escalation, high-value repair approval, complaint routing, low-confidence handoff, data isolation, and immutable activity logging.
Business Impact & ROI
- Handled 47 inbound contacts in the pilot week, escalated only 3, and recorded 0 missed calls — creating an always-on front-counter experience.
- Recovered or converted $2,340 in open and declined work during the pilot week, including a $680 transmission flush and a $1,200 timing-belt job.
- Created an explicit labor-economics business case: a loaded human service advisor costs approximately $94,250–$152,250 annually, compared with a bundled AI package cost of $79,000 in year one and $54,000 thereafter.
- Designed the model to produce additional upside beyond labor substitution through missed-call recovery, faster response, estimate follow-up, higher approval rates, and customer retention.
- Established Bradham as a founding design partner for a repeatable platform that can extend across approximately 160,000 independent repair shops and dealership networks.
- Created a five-phase rollout model from discovery through production expansion, ensuring real shop data validates the ROI case before broader commercialization.
My Role
I owned the engagement as the AI/ML product and platform leader operating at CTO scope: I defined the product vision, mapped the front-counter operating model, built the labor-economics and upside case, designed the multi-agent architecture, and established the guardrails that determine when the AI can act autonomously and when a human must approve.
I also shaped Bradham's role as the founding design partner, aligning real shop workflows with a reusable product blueprint rather than building a one-off automation. The result was a governed AI front-counter platform that connects customer experience, service operations, revenue recovery, and future market expansion into one coherent operating model.