Execution
The workflow moves from GTM signals to human-approved execution, helping revenue teams prioritize opportunities, maintain CRM quality, and improve future decisions.
Turning GTM signals into approved revenue decisions and the right communication strategy.
Project Overview
GTM teams receive more signals than ever before. The challenge is no longer collecting data, but deciding which signals deserve action, why they matter, and how to engage safely. That gap is where opportunities are missed and time is lost.
My Role
Collaboration
Product StrategyCustomer DiscoveryProduct-Market FitUX/UI DesignAI Interaction DesignDecision Workflow DesignContinuous customer validationProduct iteration
Designed and implemented in parallel.
Research ↔ Design ↔ Engineering evolved simultaneously throughout the project.
The product was continuously refined through parallel exploration, shared implementation, customer validation, and iteration.
I designed a Decision Quality Agent that acts as the reasoning layer between GTM signals and GTM execution through explainable AI, human approval, and continuous learning.
Maryna Lievshyna - Product Designer
- Evolving UX continuously around technical constraints
- Worked in a shared development environment alongside engineering
Théo Boutron - Full-Stack Engineer
- Technical architecture
- Integrations
- Full-stack development
Workflow
Customer Discovery
I interviewed GTM professionals to understand their daily pain points, why they occur, how teams currently solve them, and which parts of existing workflows they already value and wanted to keep.
Product Journey
The Problem
GTM teams already receive enough signals, but still spend too much time manually deciding which opportunities deserve action while maintaining fragmented CRM and account data.
Today
- Signals
- Manual research
- Uncertain prioritization
- Manual execution
- Lost time
With Decision Quality Agent
- Signals
- Explainable reasoning
- Human approval
- Confident execution
- Time saved
The Solution
The Decision Quality Agent turns fragmented GTM signals into explainable, human-approved actions, helping revenue teams prioritize opportunities, maintain CRM quality, and continuously improve future decisions.
Why?
Explainable AI
Every recommendation is supported by transparent reasoning before any action is taken.
Should we?
Human Approval
Review the proposed action, add business context, and decide whether it should move forward.
Now what?
Confident Execution
Turn approved decisions into faster execution while keeping CRM data accurate and teams focused on high-value opportunities.
Intended Outcomes
Companies invest in helping revenue teams make faster, better-informed decisions on the opportunities that matter most.
More productive SDR time
Better-informed outreach
Less time deciding what to do next
Cleaner CRM with fewer manual updates
Confident prioritization
Focus on high-value opportunities
Product Walkthrough
This walkthrough follows one real GTM signal from detection to human-approved execution.
Key Product Decisions
Executive Summary
Decision-makers receive an explanation before reviewing the supporting analysis.
Explainable AI
Every recommendation is backed by evidence and explains how the final score is calculated.
Decision Quality
The agent separates verified evidence from unknowns, explains the risk, and recommends the safest next action.
Message-Market-Fit
The agent proposes the best communication strategy before generating any customer-facing message.
Execution Strategy
Approved recommendations become a structured plan defining what to do, when to do it, which channel to use, and what will be created.
Execution Preview
Emails, LinkedIn messages, reminders, and CRM tasks are generated only from verified evidence. Nothing customer-facing is executed without human approval.
Continuous Learning
Approved human feedback becomes reusable business knowledge through Company Operating Memory.
Product Architecture
Execution
Agent creates drafts, reminders, CRM tasks, and follow-ups while keeping every action traceable.
CRMGmailSlackCalendarLinkedInTasksDraftsRemindersContact discovery
Company Operating Memory
The feedback is stored as reusable business knowledge to improve future recommendations.
ICP:B2B SaaSPersonas:VP GrowthProof assets
Human Learning Loop
Every reviewed decision helps improve future decision quality through reusable human feedback.
Wrong channelBad angleNeeds proofToo genericSave as a reusable rule
Reflection
This project started with a simple question:
- Explain recommendations before execution.
- Keep every customer-facing action under human approval.
- Turn approved feedback into reusable business knowledge.
Rather than automating outreach, I focused on creating transparency, explainability, and confidence before execution begins.
