Writing
GTM systems writing and field notes.
A current series on the operating layer underneath AI-enabled GTM: what agents can read, what they can suggest, what needs review, and where human judgment still owns the call.
Current articles
Where to start reading.
The series moves from safe revenue-data access into field ownership, judgment, attention, review queues, provenance, and tool boundaries.
A CRM stage should not be treated as progress until the buyer evidence underneath it can survive manager review.
AI can expand GTM motion. The operating question is whether the system protects the human attention needed to qualify, persuade, and convert the right accounts.
AI expands GTM activity. GTM architecture decides what converts through validation, data liquidity, human judgment, and operating guardrails.
China may not need to export a Western super app if DeepSeek, Qwen, Kimi, and GLM become the model layer underneath the software companies already use.
AI usage becomes meaningful only when a workflow shows lower cost, better quality, lighter review burden, or a business motion that improved.
A startup can avoid hiring and still become expensive when AI usage, GTM tooling, and review burden become the new burn layer.
A run ledger makes agentic GTM work accountable by preserving context, judgment, policy, review, and outcome.
Frames reverse ETL as the context and provenance layer that tells agents where data came from and how much to trust it.
Shows how useful agent signals become owned review work instead of another alert stream.
Explains how agents should decide which changes deserve attention before they create work for a human.
Use this when the question is who owns a field, which system is trusted, and what needs approval before CRM data changes.
A decision model for separating answers, suggestions, escalations, allowed actions, and hard stops.
Explains why CRM needs an agent-facing interface with permissions, context, auditability, and safe action boundaries.
Clarifies when to use MCPs, CLIs, and workflow automation so agents and operators do not blur execution boundaries.
Start here for the baseline: access, truth, boundaries, judgment, and review before agents act near revenue data.
Defines the missing coordination layer between GTM tools, agent access, human approval, and accountability.
The opening thesis for the series: GTM AI is becoming infrastructure, not just content, prompts, or automation.
Repos
Public build samples.
These samples show the shape of the operating systems behind the writing: signal intake, review surfaces, CRM memory, and handoff workflows.
A GTM operating layer for source intake, signal review, CRM handoff, and execution routing.
Shows the architecture and workflow pattern without exposing private client data, credentials, or internal operating material. View repository Repository sample Opportunity Discovery SystemA sample pipeline for turning market, company, and account signals into qualified opportunities for review.
Demonstrates how fragmented public signals become structured inputs for outbound, strategy, and partner prioritization. View repository Repository sample AI Operating SkillsA usable skill-pack library for signal intake, source review, evidence contracts, review queues, handoffs, and decision support.
Includes 25 public SKILL.md files with inputs, workflow steps, output contracts, and guardrails. View repository Repository sample Client Communications to CRMA workflow for converting calls, emails, and loose client context into CRM-ready account memory.
Shows how communication context can become structured follow-ups, account updates, and safer CRM handoffs. View repository Repository sample Company Chat Founder BriefA briefing workflow that converts scattered company chat into founder-facing decisions, risks, and next steps.
Useful for compressing internal discussion into a cleaner review surface without exposing private workspace history. View repositorySkills
Reusable AI operating skills.
A featured subset of the usable SKILL.md files. The full library includes 25 skill packs for intake, review, handoff, logging, sync boundaries, and control surfaces.
Open the full skills libraryCollects source changes, market movement, account activity, and operating signals into a reviewable queue.
Creates the first filter: what changed, why it may matter, and whether it deserves action. View skill file Reusable skill Source reviewTurns source documents, links, and notes into grounded briefs with clear evidence boundaries.
Separates facts, interpretations, unsupported claims, open questions, and the source material behind them. View skill file Reusable skill Evidence contractCarries decision, evidence, confidence, risks, and source URLs forward between workflow stages.
Prevents later AI steps from re-inventing facts from a thin summary or losing provenance. View skill file Reusable skill Review queue routingMoves records through explicit states such as needs review, rejected, confirmed, draft ready, and approved.
Keeps ambiguous or risky transitions human-owned while repeatable work moves forward. View skill file Reusable skill CRM handoffConverts unstructured client communication into structured CRM updates, follow-ups, and account memory.
Designed around field ownership, next steps, source context, and review before write. View skill file Reusable skill Founder briefCondenses company chat, updates, and operating context into founder-ready briefs.
Pulls decisions, risks, priorities, and unresolved questions out of high-volume collaboration surfaces. View skill file Reusable skill Decision reviewCompares evidence, constraints, owners, and timing before recommending the next operating move.
Keeps the output practical: what matters, what is uncertain, who owns it, and what happens next. View skill file