DemandLab
Assess Your GTM System

AI-Powered GTM Systems

Deploy a GTM System in 90 Days

We design and deploy agentic AI systems that identify opportunities, make decisions, and execute across your GTM stack.

These systems don't automate tasks. They execute your GTM motion.

2–4 weeks to first impact. 90 days to full system.

GTM Agent System
Live

Data Layer

CRM · Clay · Signals

Connected

Intelligence

Context · Scoring · Output

Processing

Execution

Email · LinkedIn · CRM

Active
Agent Activity
00:04Signal: Series B — Acme Corp
00:09Scored 91/100 · ICP match
00:13Outreach generated + queued

2,400

Accounts

18

Sequences

+145

Appts / wk

Built for B2B SaaS GTM teams
Replaces manual workflows
Execution-first, not strategy decks
Live across marketing, sales, and RevOps

Live Agent Workflow

See how an agent operates inside your GTM stack

A single trigger sets off a chain of agent actions — no human input required at any step.

View full example
agent.run()live

The Problem

Most GTM teams don't have a strategy problem.

They have a systems problem.

We fix that by building the systems.

Most teams see impact from their first system within 2–4 weeks.

Under the Hood

What an agent actually does

A single agent can detect a trigger, score an account, generate outreach, execute it, and log the result — in minutes, without human input.

Each agent monitors live signals, evaluates context, makes decisions, and executes — without waiting for a human to trigger the next step.

01

Signal detection

Agent monitors funding events, hiring signals, and intent data in real time.

02

Decision layer

Evaluates ICP fit, scores the account, and determines whether to proceed.

03

Execution layer

Generates outreach, selects channel, deploys sequence, logs to CRM.

pipeline_agent.run(account="acme_corp")running
Input
trigger:"funding_event_detected"
type:"Series B · $24M"
account:"Acme Corp (enriched via Clay)"
icp_match:"B2B SaaS · 50–200 employees"
Agent Actions
1evaluate_icp_fit()
// score: 91 / threshold: 75 → PASS
2score_account_signals()
// intent: high · hiring: active · funding: recent
3generate_outreach(model='gpt-4o')
// subject + body personalized to ICP + trigger
4select_channel()
// email selected · LinkedIn queued as fallback
5execute_sequence(via='Instantly')
// sequence launched · 3-touch cadence active
6log_activity(crm='HubSpot')
// contact created · deal stage: Outreach · task set
Output
status:"meeting_booked"
crm_updated:"true"
time_to_execute:"4m 12s"
human_steps_required:"0"

System of Agents

Multiple agents. One connected system.

Each agent handles a specific function. Together they run your GTM end-to-end.

TriggerScoredExecutedFeedbackSignal AgentDetects eventsDecision AgentScores & prioritizesExecution AgentRuns outreach · CRMFeedback AgentLearns & improvescontinuous loop · self-improving

Each agent operates independently and shares context across the system in real time.

This is a system of agents that replaces manual GTM execution.

How We Work

A Structured 90-Day Build Process

01
Week 1–2

System Design & Data Mapping

Map existing stack, define data flows, set success metrics.

02
Week 3–6

Build & Integration

Clay, CRM, outbound platforms, and AI layer wired together.

03
Week 7–10

Launch & Optimization

System goes live. Monitor, tune, and accelerate.

04
Week 11–12

Handoff & Scaling

Documentation, team onboarding, and scale playbook delivered.

Every engagement follows a defined system. No guesswork. No delays.

Full System Architecture

End-to-End GTM System

How data flows from signal to execution and back

Live
01

Data Sources

CRM

Salesforce · HubSpot · Attio

Clay

Enrichment · Waterfall Logic

Signals

Intent · Job Changes · Site Visits

02

AI Layer

Analysis

ICP Fit · Prioritization · Context Mapping

Message Generation

Personalized · On-brand · Sequenced

03

Execution

Email

Multi-step Sequences · Auto Follow-ups

LinkedIn

Connection Requests · DMs · Comments

CRM Updates

Auto-sync · Stage Changes · Activity Log

04

Feedback Loop

Performance Tracking

Opens · Replies · Meetings Booked · Conversions

Optimization

Messaging · Targeting · Timing · Sequence Logic

Feedback loop continuously improves targeting, messaging, and timing — the system gets smarter with every cycle.

gtm-system.py
1# End-to-End GTM System
2data_sources [crm, clay, intent_signals, product_usage]
3 ai_layer.analyze(icp_fit, context, timing_score)
4 ai_layer.generate(outreach_message, content, copy)
5 execution [email, linkedin, crm_sync, content_publish]
6 feedback_loop.capture(opens, replies, meetings, revenue)
7 optimize(targeting, messaging, timing, sequence_logic)
8 system.improve() # gets smarter with every cycle

Systems We Build

GTM Systems We Build

Four core systems deployed across marketing, sales, and RevOps.

Marketing

Build a fully automated marketing operations system

We implement

  • Lead enrichment and scoring via Clay
  • AI-driven segmentation and routing
  • CRM lifecycle automation in HubSpot

Outcome

A system that automatically prepares, prioritizes, and routes pipeline without manual work.

Agent detects a new MQL, scores it against ICP, routes to the right sequence, and logs to HubSpot — no human touch.

Sales / BDR

Deploy a scalable outbound system for pipeline generation

We implement

  • Signal-based targeting (funding, hiring, intent)
  • AI-generated personalization at scale
  • Multi-channel execution via Instantly and LinkedIn

Outcome

Consistent pipeline generation without increasing headcount.

Agent detects a hiring signal, enriches the account, generates outreach, and launches a sequence within minutes.

Customer Success

Automate retention and expansion workflows

We implement

  • Usage and engagement tracking
  • AI-triggered outreach and alerts
  • Expansion and renewal workflows

Outcome

Increased retention and expansion without manual follow-up.

Agent detects a drop in product usage, triggers a personalized check-in, and flags the account for renewal review.

RevOps

Build a unified GTM operating system

We implement

  • CRM architecture and lifecycle stages
  • Attribution and reporting frameworks
  • Automated workflows across marketing and sales

Outcome

A clean, scalable GTM infrastructure that supports growth.

Agent monitors CRM data quality, fills missing fields via enrichment, and syncs attribution across the stack daily.

How the System Works

Three systems. One connected workflow.

Each diagram represents a live system — built, deployed, and running.

01GTM Agent Workflow
active

Clay

Enrich + Score

AI Engine

Analyze + Generate

Outreach

Email + LinkedIn

CRM

Log + Sync

02Outbound Automation System
active

Signal

Intent detect

Score

ICP fit

Generate

AI message

Deploy

Sequence

Track

Replies + mtgs

03Content Generation Engine
active

Brief

Topic + ICP

Research

SERP + LLM

Generate

AI draft

Publish

CMS + SEO

Pipeline Generation System

From signal to booked meeting — fully automated.

Watch how the system moves from a trigger event to a personalized outreach sequence, without a single manual step.

Pipeline Generation Agent

How the system runs

Active

Trigger Events

Intent · Job Changes · Visits

Clay Enrichment

Contacts · Firmographics · Signals

AI Personalization

Messaging · Context · Tone

Email + LinkedIn

Sequences · Cadences · DMs

CRM Update

Sync · Track · Attribute

< 5 min

Trigger to outreach

100%

Personalized messages

Auto

CRM sync on reply

pipeline-agent.py
1# Pipeline Generation Agent
2trigger("funding_event | hiring_surge | site_visit")
3 filter(icp_score > 0.75, not_in_crm=true)
4 enrich(clay.waterfall, ["email","title","company","linkedin"])
5 generate_message(llm, personalize=true, tone="direct")
6 deploy_sequence(email=[day_1, day_3, day_7], linkedin=["connect","dm"])
7 sync_to_crm(stage="active_outreach", log_activity=true)

System Architecture

Built around how your GTM actually works.

01

Data Layer

CRM · Clay · Signals

Pull from CRM, Clay enrichment, intent signals, and product data to build full account context.

02

AI Layer

Analysis · Generation

AI scores fit, identifies timing, and generates personalized messaging and content.

03

Execution Layer

Email · LinkedIn · CRM

Actions deploy across email, LinkedIn, and CRM — with full activity logging on every touch.

04

Feedback Loop

Optimization

Captures opens, replies, and meetings. Feeds results back to improve targeting and timing over time.

execution_log.ts
running

// Real Execution Example

01

signal: "Series B funding detected — Acme Corp"

02

→ clay.buildContactList("VP Sales, CRO, CEO")// 14 contacts enriched

03

→ ai.generateMessage(contact, brandVoice)// personalized per contact

04

→ outreach.send(["email", "linkedin"])// deployed in 4 min

05

→ crm.logMeeting("Meeting booked: 3 days later")

✓ Pipeline created. Zero manual steps.

Why DemandLab

Built for Speed. Designed to Execute.

Most GTM projects take 6–12 months before producing results. We deploy fully operational systems in under 90 days.

Built for Speed

Most GTM projects take 6–12 months before producing results. We deploy fully operational systems in under 90 days.

Productized Approach

Every system is built using proven frameworks across Clay, HubSpot, outbound platforms, and AI tools — not built from scratch.

Operator-Led Execution

Our team consists of hands-on GTM operators who build and launch systems. Not strategists. Not consultants.

Our Model

This is not a traditional agency

What this is not

Not a traditional agency

We don't sell hours or retainers

No strategy decks without execution

No handoff to a junior team

What this is

Systems that run your pipeline

We build and deploy the full system

Operational in under 90 days

Continues generating results after handoff

Who This Is For

Built for GTM teams ready to operate at scale.

We work with B2B SaaS GTM teams that are ready to replace manual execution with systems. If your team is still doing by hand what a system could do at scale, this is built for you.

You already have a GTM motion in place

Your team is executing manually

You want to scale pipeline without scaling headcount

qualify.js
// Is this a fit?
if (team.isExecutingManually) {
return deploy('gtm-system');
}
if (pipeline.isStuck) {
return deploy('outbound-agent');
}

The Stack

Your Stack, Rewired Into an Agentic System

We don't replace your tools. We connect them into a system of agents that thinks, decides, and executes across your GTM stack.

feedback loopSignal LayerClayApolloZoomInfoAI DecisionOpenAIAnthropicExecution LayerInstantlySmartleadLemlistCRM LayerHubSpotSalesforceAttioAUTOMATION LAYERn8nMakeZapier
·

Clay identifies signals

AI agents make decisions

Outreach platforms execute

CRM systems update automatically

All working together as a single agentic system.

Ready to Deploy Your GTM System?

If you need a system that generates pipeline — not another strategy deck — let's talk.