Articles on where go-to-market data is going, playbooks you can run today, and reports on what the data says - written by the team building the agents that source, enrich, verify and deliver it.
Explore 64 guides to AI agents, prospecting, business data and reliable delivery, with examples and sources.
How Clay data enrichment behaves at scale: tables, credits and manual waterfalls, plus when an objective-to-dataset agent workflow fits RevOps better.
What changes when agents own the go-to-market data work, and how autonomous GTM runs day to day.
What changes when agents own the go-to-market data work: the eight data jobs, the anatomy of a data mission, the specialist agents, the governance that makes autonomy safe, and how to adopt it without betting the quarter.
Most 'agentic BI' tools are copilots that query your warehouse. Learn what real business intelligence agents do differently and how to pick the right one.
A step-by-step method for turning a seed list of best customers into a scored lookalike universe using firmographic, technographic and hiring data.
Warehouse or operational data layer? How data infrastructure for prospecting actually works, when a warehouse helps, and what to build first.
Signals to conversations: intent, enrichment, verification and outreach that lands.
Replace list-buying with evidence: the signals that reveal buying motion, how to score and combine them, and the pipeline machine that turns signals into booked conversations.
Rox sells a swarm of revenue agents that research accounts, write and send outreach, book meetings and update the CRM, priced per agent action and aimed first at enterprise sales teams. AstroFabric is the data layer underneath any rep or agent: discovery, enrichment with provenance, verification, signals, audiences and governed delivery. The honest map, with September 2026 pricing.
Origami is a Y Combinator-backed prospecting agent for small sales teams: describe the businesses you want in plain English, the agent searches the live web, and a list with emails and phones comes back on a low-cost credit plan. AstroFabric runs the whole GTM data chain as governed agent missions with evidence on every field. The honest map, with September 2026 pricing.
Evaluate sales prospecting tools with a sample test: account coverage, buyer-role discovery and verification depth measured against your own target universe.
Company and person data, waterfall enrichment, and the provenance that makes a record trustworthy.
What an agent needs from company and person data that a human operator never asked for: typed fields, provenance, freshness and cost per call. The company families, the person families, identity resolution, the data contract, delivery and the compliance rails.
Why one data source never fills a list, how a waterfall runs field by field with provenance on every value, the ordering and conflict rules that keep it honest, and what changes when an agent plans the waterfall instead of a person.
How Clay data enrichment behaves at scale: tables, credits and manual waterfalls, plus when an objective-to-dataset agent workflow fits RevOps better.
Outgrowing a reveal-button database? Compare Lusha alternatives on verification, provenance and CRM delivery, with a trial checklist RevOps can run.
Buying intent, hiring, technology change, funding and news: who is in motion, and the monitoring that keeps watch.
The two public signals that name a company’s stack and its next initiative, how to read each, the matrix that combines them into a ranked list, and the weekly machine that turns the list into verified people with an evidence-first opener.
A triage system for B2B buying signals: separate observed facts from hypotheses, screen for fit first, set expiry windows, and log kill conditions.
A five-gate workflow for turning intent signals into ad-ready audiences: fit conditions, signal windows, exclusions, owner review and platform eligibility.
Twelve questions to ask intent data providers before a pilot, plus a blind holdout design that tests the signal itself and keeps small-sample lift honest.
Matched and custom audiences built from lists: retargeting, suppression and ABM targeting on every ad platform.
How a list becomes an ad audience: what each platform matches on, the pipeline from rows to a live audience, retargeting, suppression and ABM audience types, the hygiene that decides match rates, and the approval gate that belongs in front of every ad account.
Low match rates start in your data, and the fix does too. Seven upstream improvements - verification, waterfall enrichment, freshness - that lift matched audience match rates.
Build a suppression audience from verified, enriched records and exclude customers, open deals and disqualified accounts across LinkedIn, Google and Meta.
How to build LinkedIn matched audiences from verified, enriched account and contact data - a step-by-step ABM guide that treats match rate as a data problem.
Clean records and coordinated motion: scoring, verification, routing and the hygiene that keeps a CRM truthful.
The three jobs that decide whether a CRM runs on records or on noise: fit scoring against a live ICP, verification before anything sends, and the standing hygiene missions - dedupe, suppression, provenance, staleness - that keep it true.
A field-by-field workflow for HubSpot data enrichment: plan overwrite rules, match on record IDs, run test batches, and gate writes with review before scaling.
How to evaluate lead scoring software: test inputs, rules and overrides with awkward records — missing fields, misfit accounts with hot signals, repeats.
Build an auditable account scoring model: fixed caps for fit, signals and coverage, deduplicated evidence, unknowns as flags, and a worked example scoring 67.
How the platform is built: budgets, isolation, idempotency and agent-grade APIs.
A builder's comparison of scraping pipelines and verified company data APIs, weighed on provenance, identity resolution and true maintenance cost.
The modern data stack analyzes what happened. Agentic data infrastructure discovers, verifies and streams new intelligence. A guide to where each belongs.
A developer's evaluation checklist for any person data API: coverage, contact verification, provenance, idempotent delivery and scoped access.
How to evaluate a company enrichment API as data infrastructure: coverage, waterfall behavior, verification, idempotent delivery and provenance.
Product decisions and releases, explained with their reasoning attached.
Which MCP servers a sales team should connect first, how to scope permissions safely, and what an AI agent can actually do once it is wired in.
Forty-four thousand lines of UI, removed in one commit. What survived is the platform.
Human-in-the-loop done right: park the exact action, decide in one click, and let the mission continue.
The runnable library - every mission with the prompt, the steps and what comes back.
Call the similar-companies tool over MCP from your own agent: a seed domain in, 25 lookalikes out as JSON with headcount, industry and a similarity score, schema included.
A nightly job that reads new customers from your warehouse CSV, adds them to suppression list <name> and refreshes the LinkedIn and Meta audiences with them excluded.
A script against the REST API that scores every domain in a CSV against ICP definition <name>, appends score and reasons, and writes the ranked file back, retries included.
One CLI command that verifies every email in <file.csv>, writes <out.csv> with status and confidence columns, and exits non-zero when more than 5% of the rows are invalid.
Open a workspace and the playbook library is waiting - describe the outcome and the agents carry it end to end, on your plan's monthly credits.