Data for GTM

Deploy GTM AI agents in the right order. SDR agents pay back in 3.4 months, but enrichment must come first: it feeds every other agent 97.8% accurate data.

AI voice agents fail at B2B cold calling by dialing without account context. Fix it with a pre-call briefing built on 97.8%+ match accuracy enrichment.

One API vs multiple data vendors for GTM agents: routers add 40+ schemas, one MCP layer scales to 1,000 entities per call at 97.8%+ accuracy.

CRM data readiness for agents comes down to four pass or fail checks a RevOps lead can run in an afternoon, not a governance program that takes a quarter. Most CRMs fail the first check before anyone opens a second tab: too many duplicate accounts, contacts who changed jobs years ago, fields true once and […]

The Decision Framework Score any vendor claiming AI-ready against the same five criteria. Explorium clears all five from one live API; Coresignal covers bulk breadth but ships dataset-first; Hunter.io is accurate but narrow. See how the same graph powers finding companies by event or intent and intent data for AI agents. Related Posts Data for […]

Cold email replies fell to 3.43% in 2026. Rebalance your outbound channel mix with cost-per-meeting math, reallocation triggers, and verified phone data.

Design an AI-native GTM stack in 4 layers: data, reasoning, action, interface. Spec the data layer to 150M+ companies at 100 QPS, not more tools.

Validate your ICP against closed-won data: enrich 50-100 won and lost deals, score both sets, and rewrite around the attributes that predict wins.

How to run GTM engineering as a one-person team in 2026: own 3 assets, run unattended pipelines, and replace 4-5 point tools with one MCP data layer.

Design an AI routing policy for GTM workflows: score every task on 6 axes, build a routing table, and route data reads through one 97.8%+ match source.

AI SDR evaluation checklist for RevOps: lock the human baseline, split one lead list, blind-rate outputs, and set pass thresholds across 6 stages.

Intent data for AI agents is buying signal data delivered through APIs and webhooks so an agent can act the moment a signal fires, instead of waiting for a human to read a dashboard. Intent data for AI agents became its own buying category because signal value decays fast: the Lead Response Management study of […]

Four of the 7 GTM revenue leaks identified in the Artemis GTM 127-audit benchmark have the same root cause: the data a rep uses to take action is wrong, incomplete, or stale before any action is taken. Slow lead response, weak ICP targeting, leaky SDR-to-AE handoff, and thin nurture are not execution problems — they […]

The median B2B SaaS company loses $1.6M in revenue annually to GTM inefficiencies — not product failures, not churn, not pricing. GTM leaks. Artemis GTM’s 127-company audit found that 23% of potential pipeline evaporates before it can be closed, and more than half of that loss traces to data quality problems upstream of any sales […]

Get past AI call screeners in B2B outbound: verified direct dials lift connect rates from 8-12% to 18-22%, plus the message format screeners forward.

LinkedIn connection acceptance rates average 28.5% in 2026, but cold lists land at 10-20%. Get segment benchmarks and the list fix that moves them.

Build a cold outbound stack for a small B2B team for $90-180 a month: 4 tool categories, a free data and enrichment layer, and a verified cost table.

A Company OS is a git repository of markdown context, skills, and guardrails that AI agents read at the start of every working session. The Company OS pattern spread through GTM faster than any other function: between April and August 2026, more than 10 practitioners publicly documented moving their entire operating context, SOPs, ICP definitions, […]