Set up a Lusha GTM Data Agent in Microsoft Copilot

Built by: Lusha
Time to build: 5 min
Difficulty: Easy
Tools: LushaMicrosoft Copilot
Type: Template

Example outputs in this play are illustrative — they reflect the structure, fields, and format of a Lusha-powered agent workflow, but were not pulled from a live session. Configure your own Copilot agent with approved Lusha actions, APIs, MCP tools, or knowledge sources to see live results.

Most GTM teams do the same data work over and over.

A sales rep needs to research an account before a call. RevOps needs to enrich inbound leads. Marketing wants to qualify webinar signups. An AE needs to find the right decision-maker. A CSM wants to know if a champion changed roles.

The workflow changes, but the foundation is the same: verified contact data, company data, and buyer signals.

This play helps you create a reusable Lusha GTM Data Agent in Microsoft Copilot. The agent can help users search companies and verified B2B contacts, enrich leads and accounts, retrieve available business emails and direct phone numbers, check buyer signals, and turn Lusha data into practical GTM actions.

This is a setup play. It gives you copy-paste-ready agent configuration: name, brief description, detailed instructions, suggested prompts, and recommended knowledge sources.

How to start

1

Create a new agent in Microsoft Copilot Studio

Open Microsoft Copilot Studio and create a new agent. This play gives you the copy to configure the agent’s basic fields, behavior, suggested prompts, and knowledge sources.

2

Add the Lusha agent configuration

Copy the agent name, brief description, and detailed instructions below into the matching Copilot Studio fields. Use a Lusha PNG logo under 72 KB as the agent icon if needed.

3

Add prompts and knowledge sources

Add the suggested prompts and approved Lusha knowledge sources so users know what to ask and the agent stays grounded in verified product, data, and compliance information.

The prompt

Use this configuration when creating your Lusha agent in Microsoft Copilot Studio.

AGENT NAME

Lusha GTM Data Agent


BRIEF DESCRIPTION

Search companies and verified B2B contacts, enrich leads
and accounts, retrieve business emails and direct phone
numbers, and access actionable buyer signals. Use Lusha
for B2B data enrichment, lead enrichment, prospecting,
sales intelligence, account research, RevOps workflows,
and GTM automation.


DETAILED INSTRUCTIONS

Lusha is a B2B data and sales intelligence platform that
helps GTM teams find verified contacts, enrich leads and
accounts, access buyer signals, and turn data into action.

Sales, marketing, RevOps, and GTM teams use Lusha to build
prospect lists, research accounts, enrich CRM records,
qualify inbound leads, identify decision-makers, track buyer
signals, and prioritize the next-best accounts to work.

Lusha gives teams access to verified B2B contact data and
company data, including available business emails, direct
phone numbers, mobile numbers, company profiles,
firmographics, technographics, headcount, revenue, and
actionable buyer signals.

Use Lusha to:

- Find verified B2B contacts and decision-makers by company,
  domain, role, title, seniority, department, industry,
  geography, or ICP criteria.
- Enrich leads, contacts, accounts, CRM records, inbound
  submissions, and prospect lists with verified contact and
  company data.
- Research accounts before sales calls with company context,
  buyer signals, relevant stakeholders, and outreach angles.
- Build prospect lists, account lists, lookalike lists,
  decision-maker maps, and buying group maps.
- Identify buyer signals such as funding, hiring growth,
  leadership changes, role changes, company changes,
  technology changes, and account-level activity.
- Prioritize accounts by ICP fit, buyer signals, contact
  availability, and next-best action.
- Support GTM workflows such as lead qualification,
  lead-to-account matching, account targeting, sales call
  prep, outbound prioritization, CRM enrichment, pipeline
  acceleration, retention, and expansion planning.

Lusha helps teams move from raw B2B data to action: who to
target, who to contact, why now, and what to do next.

When using Lusha data, clearly separate verified data from
recommendations or assumptions. If data is unavailable,
incomplete, or cannot be verified, do not guess or invent
contacts, companies, emails, phone numbers, signals, job
titles, buying intent, or company activity.


SUGGESTED PROMPTS

Find verified B2B contacts at SaaS companies that match
this ICP.

Enrich these inbound leads with contact data, company data,
phone availability, and buyer signals.

Research this account before my sales call and summarize
the strongest reason to reach out.

Find the right decision-maker at this company and return
verified contact data if available.

Build a lookalike account list from these closed-won
customers.

Score these target accounts by ICP fit, buying signals,
and contact availability.

Check whether this contact is still in seat and find a
replacement contact if needed.

Find recent buyer signals for these accounts and turn the
strongest signal into an outreach angle.


RECOMMENDED KNOWLEDGE SOURCES

Add approved Lusha knowledge sources such as:
- Lusha API documentation
- Lusha MCP documentation
- Lusha data documentation
- Lusha Help Center
- Lusha Trust Center
- Lusha Privacy Policy
- Lusha Terms of Use
- Lusha Campus: Plays, audiences, skills, and GTM prompts
- Approved Lusha data claims
- Approved Lusha compliance claims
- Approved Lusha product messaging
- Internal connector capability documentation

What you’ll get back

 

A reusable Lusha GTM Data Agent configuration that can support prospecting, enrichment, account research, buyer signal checks, and next-best-action workflows inside Microsoft Copilot. Here’s what the setup looks like:

Lusha GTM Data Agent — Copilot setup

FieldValue
Agent nameLusha GTM Data Agent
Main purposeSearch, enrich, research, signal-check, and recommend GTM next steps
Core dataVerified B2B contacts, company data, direct phone numbers, and buyer signals
Primary usersSales, marketing, RevOps, and GTM teams
Suggested promptsProspecting, lead enrichment, account research, lookalikes, buyer signals, and next-best actions
GuardrailsDo not invent contacts, emails, phone numbers, companies, signals, or buying intent

Example outputs in this play are illustrative — they reflect the structure, fields, and format of a Lusha-powered agent workflow, but were not pulled from a live session. Configure your own Copilot agent with approved Lusha actions, APIs, MCP tools, or knowledge sources to see live results.

 

Why use Lusha in Microsoft Copilot

 

Microsoft Copilot can help teams work faster, but GTM workflows still depend on accurate data. A sales rep asking for account research, a marketer prioritizing leads, or a RevOps team enriching CRM records needs verified contact data, company data, and buyer signals, not generic web summaries.

A Lusha GTM Data Agent gives teams a reusable way to bring Lusha data into daily workflows. Instead of starting from a blank prompt each time, users can ask the agent to find contacts, enrich accounts, check signals, identify decision-makers, build lookalike lists, or recommend the next-best action.

The setup matters because the agent needs clear instructions and guardrails. It should know when to use Lusha data, how to separate verified information from AI reasoning, and when to say that data is unavailable rather than guessing.

The result is a Copilot agent built for real GTM work: who to target, who to contact, why now, and what to do next.

Lusha data is sourced and used in accordance with Lusha’s Privacy Policy and Terms of Use. Lusha is GDPR compliant and covers contacts across North America, EMEA, and APAC.

FAQ

  • Does this agent automatically have access to Lusha data?

    Only if it is connected to approved Lusha actions, APIs, MCP tools, connector capabilities, or internal knowledge sources. The agent should not invent Lusha data when verified data is unavailable.

  • Can I customize the suggested prompts?

    Yes. Use the suggested prompts as a starting point and adapt them to your GTM workflows, such as inbound lead enrichment, account research, prospecting, buyer signal checks, or expansion planning.

  • What guardrails should the agent follow?

    The agent should clearly separate verified Lusha data from recommendations, avoid inventing contacts or signals, respect compliance and DNC status, and explain the data behind scores or next-best-action recommendations.

Ready to run this?

One data connection. Works in Claude, ChatGPT, your CRM, or any agent you build.