Build AI Agents That Scale Delivery While Your Team Focuses on Strategy

Phase 1: AI GTM Strategy Assessment and Reality Check

Objective: Map your entire GTM motion. Not the idealized version, but how work actually flows through your organization. We audit current workflows, data quality, tool integrations, and team capabilities to identify where AI agents create real leverage vs. where human judgment remains critical.

Result: No more guessing where AI fits. You'll have a detailed map showing exactly which processes are ready for AI automation, which need human oversight, and which should remain fully manual. Plus baseline metrics to prove the improvement later.

What You Get

Current state audit. Your GTM motion, marketing and sales processes, data flows, tools, handoffs, reporting.

AI workflow map. Showing exactly which processes to automate first for maximum scale

AI readiness scan. Data quality, access, governance, team skills, risks.

Opportunity map. Workflow scenarios ranked by value vs. effort, quick wins vs. platform bets.

Success metrics. Throughput, time to publish, ecycle times, and team capacity allocation.

Key Deliverables At This Stage

AI GTM strategy blueprint with a prioritized 90-day roadmap

Shared understanding of where AI creates leverage now, with measurable targets.

Reality Check Questions We Answer In This Phase

Roles & Responsibilities (optional)

Sponsor: approves scope, removes blockers.

Fractional CMO/Lead: owns blueprint, KPIs, and roadmap

Data/MarTech: maps events, access, and integrations.

Team leads: validate use cases and acceptance criteria.

Phase 2: Setting Up Right Architecture That Won't Break

Objective: Design an AI agentic system that integrates with your existing stack instead of replacing it. This isn't about the adding more complexity and technology, it's about what your team can actually maintain and drive GTM forward.

What You Get

Agent architecture. Map out exactly what each AI agent does, when it triggers, and where your team stay in the loop.

Data and toolstack integrations. Connect and synchronize your marketing, sales, support and ops toolstack and make sure we have a clear data readiness. We ensure data quality, establish single sources of truth, and create the feedback loops that let AI agents actually learn from outcomes, not just process tasks blindly.

Prompt engineering specifications. This is where most AI implementations go wrong, generic prompts produce generic outputs. We go deep into prompt architecture, building a comprehensive library.

→ Every prompt tied to specific workflows with clear inputs, outputs, and success criteria

→ Flexible frameworks that adapt based on context

→ Quality control rules. Built-in citation requirements, fact-checking protocols, and compliance guidelines.

→ Red flag detection. Automatic triggers for human review when AI encounters edge cases, sensitive topics, or confidence drops below threshold.

→ Iteration protocols. Version control for prompts with A/B testing frameworks to continuously improve output quality

Security matrix. Access scopes, retention, audit logs, rollback plans, etc.

Key Deliverables At This Stage

Implementation blueprint with specific APIs, data governance, webhooks, and integration points

Prompt library customized for your use cases

QA framework including test scenarios, rollback procedures, and performance benchmarks

Security matrix defining access controls, data retention, and audit requirements

Success checkpoint. Technical architecture approved by your engineering and devops teams.

Roles & Responsibilities (optional)

Engineering/MarTech: integrations, schema, logging.

Marketing Ops: tone, acceptance criteria, QA.

Fractional CMO/Lead: signs off on architecture, metrics, and guardrails.

Phase 3: Pilot Implementation

Objective: Launch fist AI agents in production with real workflows, real data, and real outputs on the line.

Week 1-4: Soft Launch

Deploy first agent to 10% of workflow volume with daily monitoring of outputs and quality

Immediate fixes for edge cases

Team training on how to collaborate with AI agents

Week 5-6: Scale Test

Expand to 50% of workflow volume

Measure actual time savings and outputs

Document what's working and what should be fixed (by priority)

Collect team feedback

Week 7-10: Performance Review

Compare results to baseline metrics

Calculate real efficiency improvements

Identify next workflows to start with

Build rollout plan for full implementation

Pilot Success Metrics

Phase 4: (Ongoing): Full-Scale Execution

Objective: Take the proven AI workflows across your entire GTM operation while continuously optimizing based on real performance data.

Monthly Optimization Cycle

Week 1: Performance review against output KPIs

Week 2: Prompt and model improvements based on edge cases

Week 3: New agentic workflow testing in sandbox

Week 4: Rollout of improvements to production

Scaling Checklist

Expand successful AI agents to 90% of applicable workflows

Add new agentic workflows based on pilot learnings

Retire or redesign underperforming agents

Document everything so your team owns the knowledge

Continuous Improvement Framework

Monthly metrics review with clear go/no-go criteria

Quarterly model retraining based on new data

Annual strategy reset to align with business evolution

Documented playbooks your team can execute without us

✅ Success Checkpoint: Self sufficient team running AI agentic workflows independently

Ready to Stop Talking About AI and Start Scaling With It? You've got three options.

1. Keep debating whether AI agents are "ready" while competitors scale past you
2. Try to figure it out yourself and learn expensive lessons
3. Get it right the first time with a proven implementation process