Company reference
How GhostRep uses company signals.
Field conversations give coaching a starting point. Add company material, job context and manager priorities to help your team prepare a more useful response, review an appointment and build the next practice session.
6 input groups
Sources of company context
5 surfaces
Outputs shaped by your company
During the conversation
Live coaching
No
Raw transcripts exposed by default
The premise
A roofing rep explaining an inspection and an HVAC rep discussing a repair need different context. Useful coaching starts with the homeowner’s question and the information available to that team.
Start with a question your team hears often. Bring the relevant product information, pricing explanation and appointment context into the coaching workflow. Review the suggested response against your company’s process, then decide what the rep should try next.
Company signals
The context behind a useful response.
The conversations, material and priorities your team already works with.
01
Echo sessions and field conversations.
When reps run Echo on an in-home appointment or canvassing route, the platform captures the conversation and turns it into structured signal: which objections came up, where the deal stalled, how the rep handled price pressure, what the homeowner asked, and which moments mattered most.
02
Role play sessions.
Reps practice against AI homeowner scenarios across roofing, HVAC, solar, windows, doors, and other home improvement contexts. The system records what the rep tried, where they got stuck, and which patterns repeat across the team.
03
Canvassing and appointment capture.
Activity at the door, in the appointment funnel, and in follow-up shows where reps are spending time and where conversations actually turn into appointments and sat demos.
04
Coaching notes and manager priorities.
Managers tag what they want reps to work on. Those notes — combined with the priorities a sales leader sets inside AI Sales Coach — direct where coaching focuses next.
05
Job Intel and CRM context.
Job notes, customer questions, inspection findings, and the context a manager attaches to a job become part of the picture the rep sees before the appointment and what the system reasons about afterward.
06
AI coach settings and company playbooks.
Owners and managers set the coaching style, the priority objections, the products and pricing structure, and the close standards. Those settings shape what AI Sales Coach surfaces and how Training Studio builds material.
What it produces
Outputs shaped by your company.
Live support helps during the conversation. Summaries and training drafts help managers prepare the follow-up.
Coaching
AI Sales Coach summarizes what is happening across the team, flags trends, and recommends what each rep should work on next — based on actual sessions and notes, not assumptions.
Practice
Managers can use the objections and questions from field conversations to choose a focused practice scenario. Reps then rehearse the response before the next appointment.
Summaries
Echo and Job Intel turn raw conversation and job context into concise summaries managers can scan — so the manager spends time coaching, not transcribing.
Training material
Training Studio drafts company-specific training modules from the playbooks, objections, and priorities a company actually uses. Managers approve before anything ships to reps.
Manager-ready outputs
Coaching recommendations, recap notes, and rollup reports come pre-structured so managers can review, edit, approve, and act — without rebuilding from scratch.
Manager control
Owners and managers decide what gets used.
Coaching outputs, training modules, and recommendations are starting points. A human signs off before anything becomes the standard for the team.
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Managers and admins control what data is reviewed, who can see it, what gets approved for team-wide use, and what gets exported.
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Live coaching and AI conversations can reach reps directly. Managers review material before adopting it as company training or a team-wide standard.
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Raw transcripts are not broadly exposed by default. Access to underlying session content follows your organization's permission structure.
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AI outputs can be wrong. GhostRep treats them as starting points for a human reviewer, not as final answers.