Archos LabsThe Translation Layer
Where AI programs break.
Researched by Metis — an AI research agent I built — and reviewed by me before anything ships, against 25 years of breaking these programs from the inside. Most AI newsletters are prompted. This one is engineered.
Data Foundations
Three Data Decisions That Make AI Work At Startup Scale
Your AI tool isn't broken — your data is. Fix the entity model, master your records, and automate collection. No data engineer required.
4 min read · Published
AI Readiness
Shadow AI Is Already On Your Team
Your team is already using AI you haven't approved. Here's how to find it, assess the real risk, and build lightweight governance that doesn't kill…
3 min read · Published
Data Foundations
Your CRM Data Is Lying To Your AI
Before you deploy AI for lead scoring or forecasting, your CRM data needs a 7-point audit. Here's what AI actually needs — and what it typically finds.
3 min read · Published
Getting to ROI
Write This Before Any AI Project Starts
Founders who skip a written success definition fund a story, not an investment. One page, four fields: metric, baseline, target, timeline. Here is how to write…
3 min read · Published
Build Without a Team
Six Questions To Ask Before Hiring A Data Consultant
Founders often pay for data reports nobody reads. These six questions help you scope a data consulting engagement around decisions, not deliverables.
3 min read · Published
Data Foundations
Fragmented Data Doesn't Just Slow You Down
Most SMB owners treat disconnected tools as a minor annoyance. The evidence shows fragmented data blocks AI entirely — and costs more than you're tracking.
3 min read · Published
AI Readiness
Why AI Projects Fail Before They Start
RAND's 65-interview study and a 2,000-article synthesis show AI projects fail upstream — not at the model. Here are the 5 causes and the audit that prevents…
3 min read · Published
Data Foundations
What 'Clean Data' Actually Means for a Business Like Yours
Founders are told to clean their data before using AI. This explains what data readiness actually means at SMB scale, without a data team or technical jargon.
3 min read · Published
Getting to ROI
Why Your First AI Project Should Be Boring
Founders pick AI projects for visibility. Between 80–95% fail before shipping. Here's why narrow, repetitive first projects succeed and how to pick one.
3 min read · Published
Build Without a Team
You Don't Need a Data Team. You Need a Data Decision
Most founders hire a data engineer before making the three decisions that determine whether the hire works. Here's what those decisions are and how to make…
3 min read · Published
