How It Works

Precision outbound.
Built for sales.

From ICP definition to inbox-ready sequences in minutes. No RevOps team. No GTM engineer. No technical setup.

01Targeting
02Accounts
03Contacts
04Messaging
Step 1 · Targeting

Start with a precise definition of your company

Problems you solve. Personas who feel them. Attributes that predict fit. Operator builds the ICP your rep can actually sell.

Step 2 · Accounts

Score every account against the problems you solve

Each account in the universe is evaluated against your problem attributes. You see exactly why each one passes or fails — no black-box "fit score."

Step 3 · Contacts

Verify the right people at each account

For every matched account we run a parallel lookup across 20+ sources, verify email and phone, and match each contact to your personas.

Step 4 · Messaging

Weave research into a ready-to-send messaging strategy

Each verified contact gets a cited, multi-channel sequence — grounded in the account's specific situation, reviewed by your rep.

PROBLEMSPERSONASICPScaling outboundDisconnected stackLow inbound flowVP SalesHead of RevenueDir. Sales OpsPressure to scaleBroken outboundLimited RevOpsYour Company
Datadog
Scaling outbound0
Disconnected stack0
Low inbound flow0
Score
Linear
Scaling outbound0
Disconnected stack0
Low inbound flow0
Score
Figma
Scaling outbound0
Disconnected stack0
Low inbound flow0
Score
Airtable
Scaling outbound0
Disconnected stack0
Low inbound flow0
Score
Notion
Scaling outbound0
Disconnected stack0
Low inbound flow0
Score
Retool
Scaling outbound0
Disconnected stack0
Low inbound flow0
Score
SL
Sarah LiuVP Sales
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+ 14 more sources
Email VerifiedPhone Verified◍ VP Sales
JC
James ChenHead of Revenue
>Provider 1
>Provider 2
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+ 14 more sources
Email VerifiedPhone Verified◍ Head of Rev
MS
Maria SantosDir. Sales Ops
>Provider 1
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>Provider 5
>Provider 6
+ 14 more sources
Email VerifiedPhone Verified◍ Sales Ops
RESEARCHSarah Liu · Datadog
SL
Sarah LiuVP Sales · Datadog
PROBLEM47 open sales roles on LinkedIn[1]
ICPICP fit: 92%[2]
NEWSQ3 enterprise security push[3]
PERSONASarah leads outbound initiatives[4]
PROBLEMScaling outbound w/o adding RevOps[5]
DRAFTEmailLinkedInCall
ToSarah Liu
SubjScaling outbound without added headcount
Hi Sarah,
I noticed Datadog has 47 open sales roles and is pushing into enterprise security [1][3].
Growth like that usually means outbound needs to scale fast [5].
A few VPs like you[4] at similar stage companies [2] have used Operator to add precision outbound without adding RevOps headcount.
Would a 15-min walkthrough of how we can help you find accounts with monitoring troubles be useful?

A complete outreach package. Per account.

Account research, verified contacts, a messaging brief, and email, LinkedIn, and call drafts — all grounded in the research, all ready for review and send.

Account Research

Overview, headcount, revenue, funding, challenges, sources

Contact Card

Name, title, email, LinkedIn, phone — verified 20+ sources

Messaging Brief

Tailored positioning connecting the account's challenge to your product

Email Draft

Subject + body grounded in the value scenario, challenge-first

LinkedIn Message

Shorter version adapted for LinkedIn format and tone

Call Script

Discovery question framed around the challenge — an opening

Contact Alignment

Why this specific person is the right contact for this outreach

Citations

Links to research sources that informed the messaging

How Operator Compares

Built by sales people. For sales people.

Operator sits at the intersection of data, research, and messaging. Here's how we differ from other tools in the space.

Data Enrichment & Workflow

Operator vs Clay

Clay is a powerful platform for GTM engineers. Operator delivers the same data depth and targeting precision as a finished workflow any rep can run on day one.

See full comparisonSee full comparison

Autonomous AI Agents

Operator vs AI SDRs

Autonomous AI SDRs automate the wrong part of outbound. Operator automates the research and keeps your rep in control of what actually goes to the buyer.

See full comparisonSee full comparison

Signal Aggregation

Operator vs Common Room

Common Room excels with warm community signals. Operator is built for teams that need to find and reach buyers cold, with precision.

See full comparisonSee full comparison

Product-Led Sales

Operator vs Pocus

Pocus converts product usage signals. Operator generates cold pipeline from scratch for sales-led motions.

See full comparisonSee full comparison

Four steps to a better outcome.

See how to find the right accounts. And how to reach them.