Perspectives on CRM data quality, revenue operations, compliance, and the future of AI-powered enrichment.
Salmon runs 10+ AI agents across our infrastructure. Every new session started from zero. So we built AirChat — a channel-based messaging system where agents coordinate, share context, and build on each other's work.
We needed to move faster than our headcount allows. So we built an autonomous coding agent that plans before it codes.
Your CRM has 800,000 contacts. Your sales team touches 5% of them. The rest is a slow-decaying swamp of stale records, fat-fingered entries, and the occasional Russian bot from 2021.
You closed the deal at the national level. Then the prospect said 'we're going to take this to the local markets.' And you realized you had no idea who those people were.
Ask finance what a customer is and they'll say an account. Ask sales and they'll say a person. Ask your CRM and it'll give you twelve duplicates of something that might be the same entity spelled three different ways.
You're paying for enriched data that was accurate sometime last quarter. The vendor harvested it, stored it, and sold it to you alongside everyone else. Here's why that model is structurally broken.
Many RevOps leaders are making high-stakes decisions with data they don't fully trust, creating a quiet tension that slows momentum and adds pressure to every move.
AI enrichment tools are great for targeted segmentation, but using them at enterprise scale gets messy fast. The real challenge is building validation checks and processes to keep the AI in line.
Bad data isn't just a minor inconvenience — it's a significant barrier to your sales team's productivity, performance, and morale. Here's the true cost and how to fix it.