Aurelien Aubert

Founder, Cargo

LinkedIn

34 articles.

• Let an Agent Propose Changes to Your Scoring Model

  A weekly job that reads pipeline data, finds the accounts your scoring model is wrongly suppressing, and opens a pull request with the fix and its blast radius. How Plain built it, and how to build your own.

• Answering Positive Replies in Minutes, Not Hours

  The most expensive leak in outbound is a yes nobody answers. How an agent can research, classify and draft the reply to every response across email and LinkedIn, and why the human approval step is the part to keep until the classifier earns trust.

• How Long Is a Buying Signal Worth? Time Decay, Weights, and Verification

  A signal that is three months too old sends a rep to the wrong account with the wrong reason. How to give every signal an expiry window and a weight, why the simplest version is a WHERE clause, and why verification comes before any of it.

• Data Architecture for GTM Engineers: What to Learn First

  Agents write the code now. What decides whether a GTM system lasts is the data underneath it: durable tables, keys that mean one thing, identity resolution, and one writer per fact. The four ideas to learn first, and the mistakes each one prevents.

• Do Early-Stage Startups Still Need a CRM?

  More startups now run go-to-market from a git repository and a database, and add a CRM later. When that works, what a CRM actually gives you that a repo does not, and the signs it is time to buy one.

• Your ICP Filter Is Hiding Your Hottest Accounts

  A hard ICP cutoff, set once and revisited quarterly, silently suppresses accounts that are showing real buying intent. How it happens, the weekly query that finds them, and how to change the rule without throwing the ICP away.

• The Missing Layer Between Your CRM and Your Reps

  Signals live in six tools, the account list is a three-week-old spreadsheet, and reps prospect on gut. The fix is not another tab in the CRM: it is an activity layer with its rules in git, its data in one place, and the reason for every account on screen.

• Why GTM Teams Need to Migrate Their Stack to Git

  Agents now write most changes to a revenue engine, and a GTM stack configured by clicking has no place to review them. Why the migration to git is due now, what moves first, what stays in the UI, and how teams have done it in under a day.

• Building and Contextualizing Autonomous GTM Agents

  An agent with a good model and no company context writes copy any competitor could have sent. The four things context actually means (knowledge, state, tools, memory), how to give an agent each one as code, and how to keep them true once they are deployed.

• Cross-Platform Revenue Logic: Where GTM Rules Should Live

  The same qualification rule ends up written in the CRM, the marketing tool, a warehouse model and a rep's saved view, and the four disagree. Why logic scatters across platforms, what belongs in each tool, and how to define a rule once and let every system execute it.

• Revenue Data Middleware for GTM Operations

  The layer between the warehouse, the CRM and the tools reps work in. What middleware has to do that a sync does not (identity resolution, a schema contract, conflict rules, idempotency, back-pressure, a trace), and how to tell whether you need it or just need one more pipeline.

• Revenue Pipeline Engineering: Treating Pipeline as a System

  Pipeline targets tell you the number missed. They do not tell you which stage leaked. How to instrument pipeline generation as a system with inputs, yield and latency per stage, what to measure at each seam, and where agents belong once the instrument exists.

• Modern Alternatives to CRM-Native Lead Routing and Assignment

  Salesforce assignment rules and HubSpot workflows route on the fields the CRM holds. What to do when the rule needs rep capacity, an account match, product usage or a score computed elsewhere: the three classes of alternative, and what capacity-aware routing looks like declared as code.

• Building a Software Factory for Go-to-Market

  A software factory treats go-to-market as a development process: revenue logic built, versioned and deployed as code, operated by agents that read your company's knowledge at runtime. What the model requires, and why context is the constraint rather than the model.

• Build vs Buy: Scaling Your GTM Engineering Stack in 2026

  When a GTM engineering team should build revenue infrastructure in-house and when to buy a platform: what the second year of a custom stack costs, where the line sits layer by layer, and what one system can honestly replace.

• Building a Self-Driving GTM Engine with Agentic Infrastructure

  Agentic GTM infrastructure is what a self-driving GTM engine runs on: AI agents defined and deployed as code, orchestration workflows callable from the UI, the CLI, the API and agents, and one unified data model behind lead routing and scoring.

• What is GTM infrastructure? The layer underneath your agents

  GTM infrastructure is the layer a revenue engine runs on: one data model, typed tools, plays that fire on data, agents, and context, defined in code instead of clicked into a UI. What it is, what it is not, and how to tell whether you have it.

• Your coding agent fixes your revenue engine before you notice it broke.

  Coding agents already open pull requests against your codebase. Point one at a revenue engine that is code and it does the same job there: reads the failing trace, patches the play, opens the PR. The four surfaces that make it possible, and 30 days of running our own go-to-market this way.

• Best GTM Orchestration Platforms in 2026

  Compare the best GTM and revenue orchestration platforms in 2026 across full-lifecycle data, decision logic, agents, human review, deployment, governance, observability, and total cost.

• AI loop engineering for GTM: how you build an autonomous revenue engine

  Loop engineering came out of coding agents, where the verifier is free. Revenue doesn't have one. This is how to build the missing verifier, in the order that turns a pile of agents into an autonomous GTM engine.

• The GTM Control Plane (2026): Why Orchestration Beats Point Automation

  In 2026, the winning GTM teams don’t just automate tasks, they orchestrate systems. Here’s the control-plane model: unify context, encode policy, and deploy actions across CRM, Slack, and outbound tools with near-zero revenue latency.

• LLM Evals for Revenue Agents (2026): How to Measure Quality, Not Activity

  If your AI agents are touching CRM and pipeline, you need evals. This guide explains practical evaluation frameworks for enrichment, routing, research, and outreach, plus how to deploy safely with canaries and segment-level monitoring.

• The CRM in 2026: Warehouse-Native, Event-Sourced, and Agent-Operated

  CRMs are shifting from being the system of record to being a system of engagement. In 2026, the winning architecture is warehouse-native data, event-sourced customer context, and agents that execute workflows with governance.

• Building an AI-Powered ICP Engine

  Build a dynamic ICP engine that learns from won/lost deals, surfaces emerging segments, and auto-updates targeting criteria. Includes architecture, data requirements, and operationalization workflows.

• Revenue Operations Guide for Growing Teams

  Build a RevOps function that aligns sales, marketing, and CS. Covers org structure, tech stack architecture, process design, metrics frameworks, and 90-day implementation roadmap for B2B SaaS teams.

• The Comprehensive Guide to TAM Prioritization

  According to Chet Holmes, only 3% of any market actively seeks solutions, while 40% might consider switching solutions.

• The right approach to B2B Lead scoring

  Lead & account scoring ranks those entities in order based on their likelihood of becoming customers

• The Rise of the GTM engineer

  How AI-Enabled GTM engineers Are Building the Next Gen of B2B Growth.

• Warehouse-First System of Engagement: Why the Future is Data-Native Operations

  Discover why warehouse-first architecture eliminates sync complexity, reduces costs by 44%, and creates a true single source of truth for revenue operations.

• What is a Prospect Relationship Management (PRM)?

  A prospect relationship management platform is a database made to manage your leads.

• What is a System of Engagement? Understanding the Foundation of Modern Revenue Operations

  Learn what systems of engagement are, how they differ from systems of record, and why traditional CRM-centric engagement is no longer enough for modern go-to-market teams.

• Cargo Is SOC 2 Type II Compliant, Building Trust for the AI Era

  Cargo achieves SOC 2 Type II compliance, demonstrating our commitment to protecting customer data and building trust in AI-powered revenue operations.

• The AI-Led GTM Maturity Curve: From Founder Hustle to Autonomous Growth

  AI is no longer a tool, it's a teammate. Every high-performing company starts in chaos and scales toward predictable revenue. This is the GTM maturity curve. The 5 stages every teams go through, the pain points at each, and how AI agents and humans work together to drive growth.

• Snowflake vs Salesforce - The system of record battle

  CRM was the first software to be a SaaS, it was the first software to own a marketplace, and the first software to provide a public API.