Brainforge Knowledge

GTM_AGENT_SPRINT

8 min read

GTM Projects & Roadmap

Purpose: Track all GTM projects, priorities, and agent development
Last Updated: 2026-01-26
Status: Active Development

🎯 Current Priorities

🔥 P0 - In Progress

1. Message Sequence Agent System ✅ COMPLETE

Status: Complete - Ready for implementation
Time: 4 hours (completed Jan 26, 2026)
Location: gtm/agents/

Built a complete Vercel-style message sequence generation system with AI-powered personalization and Slack approval.

What We Built:

  • ✅ 7 documentation files (architecture, guides, testing)
  • ✅ 4 campaign playbooks (cold outbound, mutual intro, event follow-up, LinkedIn)
  • ✅ Python testing framework
  • ✅ TypeScript schemas for all campaign types
  • ✅ Slack integration specification
  • ✅ Clean file organization

Next Steps:

  • Infrastructure setup (Next.js, Workflow DevKit, Slack app)
  • Test with real prospects
  • Deploy to production

2. Dynamic Rate Card ✅ COMPLETE

Status: ✅ Complete
Priority: P0 - High
Completed: Jan 26, 2026
Time: 2 hours

Goal: Create dynamic pricing calculator with all service models and hourly rates

What We Built:

  • ✅ Hourly rates by role ($150-250/hr)
  • ✅ Fixed service pricing ($5K-200K+)
  • ✅ Retainer models ($10K-40K/month)
  • ✅ Service packages and bundles
  • ✅ Volume/commitment discounts (5-15%)
  • ✅ Dynamic pricing formulas and calculator

Deliverables:

  1. RATE_CARD.md - Master rate card with all pricing
  2. SERVICE_CATALOG.md - 9 defined services with full details
  3. PRICING_CALCULATOR.md - Quick quote tool with formulas
  4. DYNAMIC_PRICING_GUIDE.md - Adapt pricing to new contexts
  5. README.md - Guide to using the pricing system

Location: gtm/pricing/

Next: Review and fill in any pricing gaps, then work on services mapping

3. Services Mapping Exercise ✅ COMPLETE

Status: ✅ Complete
Priority: P0 - High
Completed: Jan 26, 2026
Time: 2 hours

Goal: Map out all services, packages, and delivery models clearly

What We Built:

  • ✅ Complete service ecosystem map with visual flows
  • ✅ 4 client journey paths (Auditor→Builder, Tool Adopter, AI Innovator, Bridge)
  • ✅ Service bundles with pricing ($45K-90K packages)
  • ✅ Upsell/cross-sell opportunities by service
  • ✅ Revenue optimization and LTV analysis
  • ✅ Training gap identified ($30-50K annual opportunity)
  • ✅ Partner-friendly rate card for subcontract partners

Deliverables:

  1. SERVICES_MAP.md - Complete service ecosystem, journeys, bundles, gaps
  2. PARTNER_RATE_CARD.md - Clean, shareable rate card for partners

Location: gtm/pricing/

Key Insights:

  • 70% of clients starting with Data Audit convert to bigger projects
  • Platform builds have 80% retainer conversion rate
  • Client LTV ranges from $300K-965K depending on entry point
  • Training/enablement represents $30-50K annual revenue opportunity

4. SOW Framework System ✅ COMPLETE

Status: ✅ Complete
Priority: P0 - High
Completed: Jan 27, 2026
Time: 5 hours

Goal: Build comprehensive, semantic SOW framework for all engagement types (150K+)

What We Built:

  • ✅ 7 SOW templates (Tier 1-4) covering all engagement types
  • ✅ Decision framework with decision tree and selection matrix
  • ✅ 6-component reusable library (executive summaries, pricing, risks, metrics, communication, team)
  • ✅ Quality system (pre-flight checklist, anti-patterns, review rubric)
  • ✅ Pricing integration (model selector, calculators, rate card mapping)
  • ✅ Evolution system (learning, win/loss tracking, continuous improvement)
  • ✅ Quick start guide for new users

Deliverables (20 files total):

  1. README.md - Main navigation and system overview
  2. DECISION_FRAMEWORK.md - Which SOW to use when
  3. QUICK_START.md - Get started in 10 minutes
  4. 7 Templates: tier1-simple, tier2-tactical, tier3-interim/strategic/discovery, tier4-platform/execution
  5. 6 Components: executive-summaries, pricing-tables, risk-matrices, success-metrics, communication-plans, team-structures
  6. 3 Quality files: PRE_FLIGHT_CHECKLIST, ANTI_PATTERNS, REVIEW_RUBRIC
  7. 3 Pricing files: PRICING_INTEGRATION, MODEL_SELECTOR, CALCULATORS
  8. 3 Evolution files: LEARNING_SYSTEM, WIN_LOSS_TRACKING, CONTINUOUS_IMPROVEMENT

Location: gtm/sow-framework/

Based on: 8 real SOW examples analyzed (Client A/B, Insomnia, Inteleos, Honey Stinger, LMNT, CTA, EdenOS)

Key Features:

  • Tiered system scaling from 150K+ comprehensive SOWs
  • Integrated with pricing system (gtm/pricing/)
  • Semantic/evolving design (learns from wins and losses)
  • Anti-patterns guide based on Insomnia failure analysis
  • Reusable components for faster SOW creation
  • Quality control system to prevent bad SOWs

Impact:

  • Reduce SOW creation time by 60-70% (templates + components)
  • Increase win rate with proven patterns
  • Ensure consistency across team
  • Learn and improve from every outcome
  • Never send an “Insomnia” again

📋 P1 - Backlog (Next Up)

4. Additional Campaign Playbooks

Status: 📝 To Do
Priority: P1 - Medium
Time Estimate: 60 min per playbook

Campaigns to Add:

  • Partnership Outreach (partner rep engagement, co-sell)
  • Product Launch (new feature announcements)
  • Re-engagement (inactive prospects, stalled deals)
  • Referral Request (customer referrals)

Process: Follow gtm/agents/HOW_TO_ADD_CAMPAIGNS.md

5. Message Sequence Agent - Infrastructure

Status: 📝 To Do
Priority: P1 - Medium
Time Estimate: 2-3 weeks

Implementation Phases:

Week 1: Setup

  • Next.js app with Workflow DevKit
  • Vercel AI SDK configuration
  • Slack app setup
  • HubSpot CRM integration

Week 2: Agent Implementation

  • Research agent with AI SDK Agent class
  • Campaign classifier with generateObject
  • Sequence generator with generateObject
  • Slack approval flow with Block Kit

Week 3: Testing & Launch

  • Test with 10-20 real prospects
  • Iterate on playbooks based on feedback
  • Train team on Slack approval
  • Launch to production

📊 P2 - Future (Lower Priority)

6. Partner Tracking System Improvements

Status: Backlog
Priority: P2 - Low
Location: knowledge/sales/partners/

Based on existing partner playbooks and tracking system.

📈 Success Metrics

Message Sequence Agent

  • Approval Rate: Target 80%+ (sequences approved without edits)
  • Response Rate: Target 20-30% higher than manual
  • Time Savings: 40 min → 6 min per sequence (85% reduction)
  • Volume: 10-20/week → 50-100/week (3-5x increase)

Dynamic Rate Card

  • Sales Cycle: Faster proposal generation
  • Pricing Consistency: No ad-hoc pricing
  • Conversion: Clear pricing increases close rate

Services Mapping

  • Sales Clarity: Reps know exactly what to sell
  • Marketing Alignment: Clear messaging per service
  • Client Journey: Smooth progression through tiers

✅ Completed Projects

Message Sequence Agent System (Jan 26, 2026)

Time: 4 hours
Status: ✅ Complete

What Was Built:

Documentation (7 files)

  1. MESSAGE_SEQUENCE_AGENT.md - Vercel-style architecture
  2. MESSAGE_SEQUENCE_SYSTEM_README.md - Master guide
  3. CAMPAIGN_TYPES_SCHEMA.md - TypeScript schemas
  4. SLACK_INTEGRATION_SPEC.md - Slack integration details
  5. HOW_TO_ADD_CAMPAIGNS.md - Guide to add campaigns
  6. TESTING_GUIDE.md - Testing without infrastructure
  7. IMPLEMENTATION_SUMMARY.md - What was built

Campaign Playbooks (4 complete)

  1. playbooks/cold-outbound-playbook.md - Cold prospecting
  2. playbooks/mutual-intro-playbook.md - Warm introductions
  3. playbooks/event-follow-up-playbook.md - Post-event outreach
  4. playbooks/linkedin-connection-playbook.md - LinkedIn sequences

Testing Framework

  • test_playbook.py - Python testing script
  • 6 built-in test scenarios
  • Claude API integration

Organization

  • Cleaned up redundant files
  • Clear structure: playbooks/ for campaigns, memory/ for knowledge
  • Archived 6 old/duplicate files

Architecture:

Trigger → Research Agent → Campaign Classifier → Sequence Generator 
→ Slack Approval → Send

Event Follow-Up Agent (Jan 2025)

Status: ✅ Complete
Location: .claude/agents/event-follow-up-agent.md

  • Hyper-personalized event follow-up sequences
  • Segmentation by engagement type
  • Persona-matched messaging
  • Qualification scoring

LinkedIn Sequence Agent (Jan 2025)

Status: ✅ Complete
Location: .claude/agents/linkedin-sequence-agent.md

  • Multi-step LinkedIn sequences
  • Job posting → lead playbook
  • Mutual intro playbook
  • GSheets-friendly format

ICP Analysis Agent (Jan 2025)

Status: ✅ Complete
Location: prompts/icp-agent-prompt.md

  • Analyzes attendee lists for ICP matches
  • 5-dimension scoring
  • Ranked prioritization

📂 Project Structure

gtm/
├── agents/                           # Message sequence agent system
│   ├── README.md                     # Overview and quick start
│   ├── MESSAGE_SEQUENCE_AGENT.md     # Architecture (Vercel-style)
│   ├── MESSAGE_SEQUENCE_SYSTEM_README.md  # Master guide
│   ├── playbooks/                    # Campaign playbooks (4 complete)
│   │   ├── cold-outbound-playbook.md
│   │   ├── mutual-intro-playbook.md
│   │   ├── event-follow-up-playbook.md
│   │   └── linkedin-connection-playbook.md
│   ├── memory/                       # Supporting knowledge
│   │   ├── ideal-customer.md
│   │   ├── positioning.md
│   │   ├── qualification-criteria.md
│   │   └── vertical-testing.md
│   ├── test_playbook.py             # Testing script
│   └── [other documentation files]
│
├── pricing/                          # 🆕 TO CREATE
│   ├── rate-card.md
│   ├── pricing-calculator.xlsx
│   └── pricing-guide.md
│
├── services/                         # 🆕 TO CREATE
│   ├── service-catalog.md
│   ├── service-matrix.md
│   ├── client-journey.md
│   └── packages.md
│
├── partners/                         # Partner program + vendor folders
│   └── [partner playbooks and tracking]
│
└── sales/                           # Existing sales materials
    └── [SOWs and proposals]

🔄 Development Process

Adding New Projects

  1. Add to appropriate priority section (P0, P1, P2)
  2. Define status, owner, time estimate
  3. List deliverables and acceptance criteria
  4. Move to “Completed” when done

Priority Definitions

  • P0 (High): Critical for business, blocks other work
  • P1 (Medium): Important but not blocking
  • P2 (Low): Nice to have, future consideration

Status Labels

  • 📝 To Do - Not started
  • 🔄 In Progress - Actively working
  • ⏸️ Blocked - Waiting on something
  • ✅ Complete - Done and shipped

📅 Timeline

ProjectStartEndStatus
Message Sequence SystemJan 26Jan 26✅ Complete
Dynamic Rate CardTBDTBD📝 To Do
Services MappingTBDTBD📝 To Do
Additional PlaybooksTBDTBD📝 To Do
Infrastructure SetupTBDTBD📝 To Do
Agent ImplementationTBDTBD📝 To Do
Testing & LaunchTBDTBD📝 To Do

🔗 Reference Documents

Internal

External

📞 Questions & Decisions Needed

For Dynamic Rate Card

  • What are hourly rates by role/level?
  • What fixed retainer models exist?
  • How do volume discounts work?
  • What’s the minimum engagement size?

For Services Mapping

  • What are all the services we offer?
  • How do they relate to each other?
  • What’s the client journey?
  • What packages should we create?

For Message Sequence Agent

  • Which campaign to implement first?
  • What triggers should we automate?
  • Who approves in Slack?
  • What CRM fields to update?

🎓 Key Learnings

What’s Working

  • Vercel architecture as template - Clear reference point
  • Campaign-specific playbooks - Self-contained, easy to add
  • Test before building - Validate content quality first
  • Clean organization - Removed redundancies early

What’s Next

  • Dynamic pricing - Make pricing clear and consistent
  • Service clarity - Define what we actually sell
  • Full implementation - Build the agent infrastructure
  • Scale - Move from manual to automated sequences

Next Session: Work on Dynamic Rate Card and Services Mapping Exercise

Last Updated: January 26, 2026

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