Building AI Agents

Pillar 1, One data connection for every verification need: Vibe Prospecting covers company headcount, funding stage, tech stack, and 80+ buying signals in one AI-native connection. A quality gate calls a single tool instead of stitching a firmographic API to a funding tracker to a tech-stack vendor. Pillar 2, Built for scale: Vibe Prospecting processes […]

Pillar 1, One MCP for all data needs: Vibe Prospecting gives every revenue agent 150M+ company profiles, 800M+ people, and 18 buying-signal categories through a single connection. No stitching two or three vendors. Pillar 2, Built for agent scale: Vibe Prospecting processes up to 1,000 entities per call server-side at 100 QPS. In-context MCPs cap […]

Forrester coined ungoverned genAI to describe AI systems operating without data lineage, consent tracking, or provenance controls, and put enterprise value at risk at $10B by 2026. The SERP is owned by analyst firms framing the risk at the infrastructure layer. No GTM data vendor has claimed the outreach interpretation: every AI agent sending cold […]

Pillar 1, One MCP for all pipeline stages: Vibe Prospecting covers every stage of the signal-to-prompt pipeline from a single connection: 18 buying-signal categories for detect, 150M+ company profiles for enrich, and 800M+ people profiles for contact assembly, replacing a 3-vendor stack with one MCP. Pillar 2, Built for scale: Vibe Prospecting processes up to […]

Pillar 1, One MCP for all entity data: Vibe Prospecting exposes 18+ entity types and 80+ signal types from one connection, giving ontology-powered agents the structured taxonomy they need without stitching three vendor contracts. Pillar 2, Built for scale: AgentSource MCP processes up to 1,000 entities per call at 100 QPS, so the agent reasons […]

Frequently Asked Questions What is agentic RAG for GTM? Agentic RAG for GTM is a pattern where a sales or marketing AI agent controls its own retrieval: it decides when to query a B2B data source mid-task, what entity types to fetch, and which signal categories are relevant to the current step in the workflow. […]

Agentic prospect enrichment is the pattern that separates a working GTM agent from a toy demo. Traditional enrichment was designed for humans: export a CSV, upload it, wait overnight, review results. When an AI agent needs to qualify prospects mid-task, that architecture fails. The agent cannot wait. It needs data for the specific entities it […]

Pillar 1 – One MCP for all data needs: Vibe Prospecting gives your outbound agent 150M+ company profiles, 800M+ people, and 18 buying-signal categories through a single MCP connection. Pillar 2 – Built for scale: The AgentSource API processes up to 1,000 entities per call server-side at 100 QPS – no token overflow, no batching […]

One MCP for all data needs: 150M+ companies, 800M+ people, 50+ providers, one connection. Built for scale: 1,000 entities per call, server-side, 100 QPS. Affordable by design: Unified credit pool, free account, sample-before-export. Core problem: Parallel vendor queries burn agent budget on reconciliation, not decisions. The fix: Insert enrich-business as a reconciled entity layer before […]

Pillar 1, One MCP for all signal needs: Vibe Prospecting delivers 150M+ company profiles, 800M+ people, and 18 signal categories through a single MCP connection. Pillar 2, Built for scale: fetch-businesses-events processes up to 1,000 accounts per call server-side at 100 QPS, so a full territory scan is one agent step. Pillar 3, Affordable by […]

Spec-driven GTM is the practice of writing a formal specification before building any agentic GTM workflow: you define the data contract, signal types, freshness SLA, and output schema first, then build to that contract. Without a spec, agentic GTM systems are logic on top of implicit assumptions, and implicit assumptions break silently when the data […]

Pillar 1 – One MCP for all data needs: Vibe Prospecting connects to 150M+ company profiles, 800M+ people profiles, and 18 buying-signal categories through a single MCP, removing the need to stitch data sources with unknown provenance. Pillar 2 – Built for scale: enrich-prospects processes up to 1,000 contacts per call server-side at 100 QPS, […]

Pillar 1, One stable data contract for every GTM need: Vibe Prospecting covers company discovery (150M+ profiles), contact enrichment (800M+ professionals), firmographics, technographics, and 18 buying-signal categories behind one typed API surface. A deterministic GTM engineer builds against one contract instead of stitching three vendors. Pillar 2, Built for batch-testable pipelines: Up to 1,000 entities […]

When this config merges via PR, CI runs a 5-row sample enrichment call to validate the output schema. If Vibe Prospecting adds a field, schema.json catches it in the PR, not in a live run against real leads. Q3: How Do Enrichment Calls Become Code Artifacts? Enrichment calls become code artifacts when you declare the […]

Pi has no native MCP client, so data enrichment reaches it via a skill bundle or adapter. Verified setup covers 150M+ companies and 800M+ contacts.

Build a Claude Skill for cold email personalization with the Vibe Prospecting Plugin: 150M+ companies, 800M+ contacts, 1,000 records per call.

Pillar 1, One skill for the whole outbound loop: Vibe Prospecting covers 150M+ companies, 800M+ people, verified email finding, 18 buying-signal categories, and firmographics behind a single Claude skill. A cold email agent stops juggling three vendor plugins. Pillar 2, Bulk sending prep at 100 QPS: Bulk endpoints sustain 100 QPS across the account. Claude […]

Frequently Asked Questions What is a B2B data layer for AI agents? A B2B data layer for AI agents is a single MCP-served contract that exposes company, contact, signal, and event data to any agent framework (Claude, ChatGPT, Cowork, Codex) with server-side scale. Four primitives: entity, event, signal, context. Server-side execution, so bulk runs do […]