AskAI Assistant
The problem: Reps spend time hunting through CRM, calls, and emails to answer basic questions about accounts and deals. The solution: A Slack-based Q&A agent that answers questions about any account, deal, or contact using the full context graph. What this looks like: Ask “what happened on the Acme deal last week?” and get an answer in seconds, not minutes of digging through tabs.Deal Risk Monitor
The problem: At-risk deals slip through the cracks. By the time you notice the warning signs, it’s too late. The solution: An agent that monitors engagement drops, champion changes, and other risk signals — and alerts you before deals go dark. What this looks like: Surface at-risk deals automatically instead of hunting through CRM. Get alerts when engagement drops or key contacts go silent.Post-Call Workflow
The problem: After calls, reps forget to update CRM, send follow-ups, or capture next steps. Important context gets lost. The solution: An agent that listens to call transcripts, generates summaries, and updates CRM automatically. What this looks like: Calls get summarized and logged without manual data entry. Follow-up tasks get created automatically.Pipeline Hygiene
The problem: CRM data quality degrades over time. Close dates slip, stages don’t match reality, and forecasts suffer. The solution: An agent that identifies data quality issues and either auto-fixes them or alerts the rep. What this looks like: Keep your pipeline clean without endless “update your deals” Slack messages. Auto-fix what can be fixed, flag what needs human review.Exec Briefing
The problem: Preparing for executive meetings means pulling data from multiple systems and manually assembling a brief. The solution: An agent that generates account briefs before meetings, pulling from CRM, calls, and emails. What this looks like: Get a comprehensive account brief in your inbox before your QBR, automatically.Deal Scoring
The problem: Qualification is inconsistent. Reps use different criteria, and it’s hard to compare deals objectively. The solution: An agent that scores deals against your criteria (MEDDPICC, BANT, or custom) using actual conversation and CRM data. What this looks like: Objective deal scores based on real signals, not gut feel. Identify gaps in qualification before they become pipeline problems.Call Coaching
The problem: Managers can’t review every call. Reps don’t get consistent feedback on their technique. The solution: An agent that analyzes calls against your coaching criteria and provides feedback. What this looks like: Scalable coaching feedback without requiring managers to listen to every call. Identify patterns across the team.Start with a template or build from scratch
Every template is customizable. Adjust the data sources it can access, modify the instructions, or combine multiple templates into something new. If your use case isn’t covered by a template, you can build custom agents from scratch using the MCP server or agent builder (coming soon).Next steps
- Quick start — Connect your data and deploy your first agent
- MCP server — Build custom agents with full API access