DEV Community: Tom Regan

Speed-to-Lead 2026: Why 42 Hours Is Killing Your Conversion Rate

Tom Regan — Tue, 14 Apr 2026 16:41:49 +0000

The Problem

Your sales team is leaving money on the table.

Harvard Business Review's 2011 audit of online lead response (Oldroyd, McElheran, Elkington) found the average first reply took 42 hours (The Short Life of Online Sales Leads). That's nearly two days. Fast replies are still rare: Drift's 2017 lead response study found only 7% of the companies it tested responded within five minutes.

The gap between fast and slow is large. The Lead Response Management study (Oldroyd, 2007) associated contact within five minutes with roughly 21x higher odds of qualifying a lead than waiting 30 minutes. HBR's 2011 audit found firms that tried to reach a lead within an hour were nearly 7x as likely to qualify it as firms that waited even an hour longer, and more than 60x as likely as firms that waited 24 hours or more.

The second issue: the cost compounds. Every hour a lead waits, the odds of reaching and qualifying it keep falling.

This is not about being "responsive." It is infrastructure. It is about how your systems are wired.

What the Research Says

HBR's 2011 audit sorted the companies it tested by how fast they answered a web lead:

First response	Share of companies (HBR, 2011)
Within an hour	37%
1 to 24 hours	16%
More than 24 hours	24%
Never responded	23%

Source: The Short Life of Online Sales Leads, Harvard Business Review, March 2011 (Oldroyd, McElheran, Elkington).

Run the Math on Your Own Numbers

Take last quarter's inbound leads, your lead-to-opportunity rate in three response buckets (under 5 minutes, 5 to 60 minutes, over an hour) and your average deal value. The gap between the buckets is your number. It comes from your own CRM, not from a benchmark.

Implementation Framework

1. Real-Time Lead Ingestion

[Form Submit on Website]
    |
[Webhook -> Queue (SQS/Pub-Sub)]
    |
[Lead Router (Immediately)]
    |-- Assign to Sales Rep
    |-- Trigger Slack Notification
    |-- Queue AI Enrichment
    |-- Schedule First-Touch Email
    |
[Response Action (< 2 min)]

Setup:

Use form webhooks, not CRON jobs or polling
Implement a queue system between form and routing
Route before enrichment (enrichment is async)
Alert assigned rep via Slack within 30 seconds
No "waiting for data" before assignment

2. Multi-Channel Alert System

Lead Created
|-- Slack DM to assigned rep (instant)
|-- SMS to rep phone (backup, < 1 min)
|-- Calendar hold (optional)
|-- Email to CRM (logging only)

Checklist:

[ ] Slack integration with lead details in message
[ ] SMS backup for reps who silence Slack
[ ] Escalation: if rep does not click in 5 min, ping manager
[ ] Thread responses back to lead record
[ ] Do NOT rely on email as primary channel

3. AI-First Triage and Enrichment

In the builds I've done, the pattern that holds up runs AI enrichment after routing, never before it:

[New Lead Arrives]
    |
[AI Agent Enrichment (Async)]
|-- Company research
|-- Role/seniority inference
|-- Intent scoring
|-- Auto-qualification check
    |
[If Qualified -> Rep Notification]
[If Not Qualified -> Nurture Queue]

4. Live Dashboards With Compensation Tie-In

[Sales Dashboard - Real-Time]
|-- Lead received -> assigned rep -> response time
|-- Sub-5-min responses: highlighted
|-- Escalations: alerts on leads > 10 min without response
|-- Bonus component: tied to response SLA

5. Fallback and Escalation Logic

Lead Assigned to Rep A
|-- Rep A: Notified (< 30 sec)
|   |-- If no action in 5 min -> escalate
|   |-- If no action in 15 min -> unassign
|-- Rep B (backup): Notified if A escalates
|-- Team Lead: Notified if B escalates
|-- Auto-Response: Triggered if no human in 60 min

Your Starting Point

If your first reply is measured in hours, not minutes, here's the priority order:

Week 1: Wire webhooks to send leads to Slack in real-time. Measure the new response time.
Week 2: Add SMS escalation if Slack goes unread for 5+ minutes.
Week 3: Build a 30-second enrichment job (company domain -> API -> infer role).
Week 4: Set up a live dashboard showing response times by rep.

Resources

Sources and benchmarks: https://artemisgtm.ai/resources/research/speed-to-lead-benchmark-2026/

Artemis GTM is a GTM engineering consulting firm that builds speed-to-lead systems: https://artemisgtm.ai/

Built by Artemis GTM. Speed-to-lead is infrastructure. If your system does not route leads in under 5 minutes automatically, you are competing on hope instead of design.

Building a Signal-to-Sequence Pipeline with Amplemarket's Custom Signal API

Tom Regan — Fri, 20 Mar 2026 16:20:21 +0000

I built an automation pipeline that discovers buying signals across the web, qualifies them against an ICP using LLM scoring, enriches the prospects, and pushes them into Amplemarket's Duo via their Custom Signal API. Duo then auto-generates personalized multichannel sequences.

https://artemisgtm.ai/blog/amplemarket-duo-signal-playbook

Here's the technical breakdown.

The Architecture

Signal Discovery (Exa MCP)
|
v
ICP Qualification (LLM Scoring)
|
v
Enrichment (Amplemarket MCP)
|
v
Webhook Push (Custom Signal API)
|
v
Duo Sequence Generation (Automatic)

Phase 1: Signal Discovery

I use Exa for semantic search. Unlike traditional keyword search, Exa understands intent. Querying "founders who discussed lead response challenges on podcasts in the last 30 days" actually returns relevant results.

Signal types: podcast appearances, conference talks, funding announcements, hiring patterns, competitor reviews, regulatory changes. Each represents a verifiable moment where someone in your target market demonstrated a specific pain point.

Phase 2: LLM-Based Qualification

Each signal gets scored against ICP criteria. I pass the signal context plus ICP definition to Claude and ask for a structured score across four dimensions: company fit, role fit, pain alignment, and timing urgency.

Anything above 70% aggregate confidence triggers the next phase.
Phase 3: Enrichment via Amplemarket API
Qualified prospects get created in Amplemarket via their lead management API. Enrichment fills in contact details, company data, tech stack info. Typical enrichment latency is 30-60 seconds per batch.

Phase 4: Custom Signal Webhook

This is the interesting part. Amplemarket's Custom Signal API (POST /custom_signals/{token}/entries) accepts structured data about a prospect plus context, and routes it to their Duo AI copilot.
Key insight: create separate Custom Signals for each signal type. A "Podcast Pain Signal" webhook has different messaging instructions than a "Funding Round Signal" webhook. This means Duo adjusts its tone and angle automatically based on how the prospect was discovered.

Payload structure:

json{
"first_name": "Sarah",
"last_name": "Chen",
"company_name": "Acme Corp",
"email": "sarah@acme.com",
"linkedin_url": "linkedin.com/in/sarahchen",
"extra_context": "Appeared on SaaS Scaling podcast March 2026. Discussed challenge of converting website visitors to pipeline. Company recently expanded SDR team from 2 to 5."
}

The extra_context field carries the signal intelligence. Duo reads it and generates sequences that reference specific details from the prospect's public activity.

Phase 5: Human Review

Duo generates the sequences. A human reviews before sending. I don't recommend going fully autonomous on outreach copy. But the difference between reviewing context-rich AI drafts versus writing from scratch is 20 minutes versus 4 hours per batch.

Implementation Options

This can run as a Claude skill (what I built first), an n8n workflow (for scheduled execution), or a Python script. The architecture is tool-agnostic.

The important decisions are: what signals to scan for, how strict to make your ICP qualification, and how to write the Duo messaging instructions for each signal type.

Results

Podcast signals convert at 2-3x any other signal type. Funding round signals need careful framing (skip "congrats on the raise"). Hiring signals are consistently underrated.

Full non-technical breakdown: https://artemisgtm.ai/blog/amplemarket-duo-signal-playbook

Building a B2B Sales Intelligence Stack: 7 Tools Tested in Production

Tom Regan — Wed, 11 Mar 2026 19:11:13 +0000

The Problem

Most B2B teams run more GTM tools than they can name, and a good share of them deliver no measurable return. The problem is usually fit, not features: teams buy tools that don't match their stage, their main bottleneck or the stack they already run.

After implementing the major sales intelligence tools in hands-on client builds, here's a practical evaluation of the 7 tools that keep delivering measurable pipeline impact.

The Stack Architecture

A modern B2B sales intelligence stack has four layers:

Layer 1: IDENTIFICATION. Who is on your site?
  - Warmly, RB2B

Layer 2: ENRICHMENT & OUTBOUND. How do you reach them?
  - Amplemarket, Apollo

Layer 3: REVENUE INTELLIGENCE. How do you convert them?
  - Attention, Sybill

Layer 4: CRM. Where does the data live?
  - Attio

Tool-by-Tool Breakdown

Prices below are as published on each vendor's pricing page in March 2026. Check the vendor for current pricing.

Layer 1: Website Visitor Identification

Warmly: $700/mo

RB2B: From $197/mo (free tier)

Layer 2: Sales Engagement & Data Enrichment

Amplemarket — From $600/mo

Apollo: From $49/mo (free tier)

Layer 3: Revenue Intelligence

Attention: From $59/user/mo (free tier)

Sybill: From $29/user/mo (free tier)

Layer 4: CRM

Attio: From $29/user/mo (free tier)

Feature Comparison Matrix

Feature	Warmly	Amplemarket	Attention	Sybill	Attio	Apollo
Primary Use	Visitor ID	Outbound + enrichment	Call coaching	Call intelligence	CRM	Prospecting + data
AI Capabilities	AI chatbot	Duo Copilot	Real-time coaching	AI summaries	Auto-enrichment	Email suggestions
Free Tier	No	No	Yes	Yes	Yes	Yes
Starting Price	$700/mo	$1,200/mo	$59/user	$29/user	$29/user	$49/mo

Stage-Based Stack Recommendations

$1M-$5M ARR (Early Stage):
  Warmly + Apollo + Attention
  ~$1,500-$2,500/mo | 3-4 tools

$5M-$15M ARR (Growth Stage):
  Warmly + Amplemarket + Attention + Attio
  ~$3,000-$5,000/mo | 4 tools

$15M-$50M ARR (Scale Stage):
  Full stack, configured per GTM motion
  ~$4,000-$8,000/mo | 4-5 tools

Implementation Priority

Identify your biggest revenue leak (anonymous traffic? outbound volume? call quality? CRM data?)
Implement the tool that directly addresses that leak
Measure pipeline impact for 30 days
Add the next tool only after the first is producing measurable results

Resources

Building a Real-Time Visitor Intelligence Pipeline for B2B Lead Generation

Tom Regan — Mon, 09 Mar 2026 14:19:37 +0000

If you manage website infrastructure for a B2B company, your analytics stack is probably optimized for aggregate metrics. Page views, session duration, bounce rate, conversion rate. These tell you how the site performs on average. They tell you nothing about who is visiting and what they need.

The visitor intelligence stack adds an identity layer to your analytics. Instead of "500 people visited the pricing page today," you get "Sarah Chen, VP of RevOps at Acme Corp, visited the pricing page twice in the last 48 hours and spent 4 minutes on the enterprise features section."

The technical architecture has four layers.

Layer one: de-anonymization. Services like Warmly use reverse IP lookup, cookie matching, and partnership data networks to resolve anonymous visitors to company and contact records. The identification rate varies but typically reaches 40 to 65% for B2B traffic.

Layer two: real-time enrichment. Once a visitor is identified, their record gets enriched through a multi-provider waterfall. Amplemarket for contact data, firmographic depth and for email verification. The enrichment fires asynchronously so it does not add latency to the page load.

Layer three: routing and notification. The enriched record gets scored for intent (based on pages visited, time on site, and visit frequency) and routed to the appropriate account owner in the CRM. Real-time alerts fire via Slack, email, or in-app notification.

Layer four: automated outreach. An outbound engine like Amplemarket triggers personalized multi-channel sequences based on the visitor's behavior. The outreach references the specific pages visited and adapts to the buyer's likely pain points based on their role and industry.

The end-to-end latency target is under 5 minutes from page visit to first outreach touch. Our benchmark data shows that sub-5-minute response makes you 100x more likely to qualify an inbound lead.

Most companies do not have this pipeline. The 98% of visitors who leave without converting are invisible to them. Building this layer is one of the highest-leverage infrastructure investments a B2B company can make.

Full stack breakdown: artemisgtm.ai/blog/website-visitor-deanonymization-revenue-leak

Why Intent Signals Beat Firmographic Filters in Outbound Sales Systems

Tom Regan — Tue, 03 Mar 2026 18:43:04 +0000

If you build or maintain the outbound sales infrastructure for a B2B company, there is a data architecture problem hiding in your targeting logic.

Most outbound systems start with firmographic filters. Industry, company size, revenue, tech stack. This produces a list of companies. Then the system enriches contacts at those companies and starts sending outreach.

The problem is that firmographic similarity does not predict buying intent. Just because a company matches your customer profile does not mean anyone there is experiencing the problem your product solves right now. You end up sending well-targeted messages to people who have no current need. The response rates reflect this.

The shift happening in outbound infrastructure is from firmographic targeting to signal-based targeting. Instead of filtering by company attributes, you filter by behavioral signals: hiring patterns, technology changes, content engagement, website visits, funding events, leadership transitions.

The technical implementation looks different. Instead of a static list that gets enriched and sequenced, you are building event-driven systems that monitor signals and trigger enrichment and outreach when intent spikes. The enrichment waterfall fires on demand rather than in batch. The AI agent generates outreach based on the specific signal that triggered the workflow.

Kevin Dorsey, a well-known sales leader, has articulated this as the difference between knowing your ICP and understanding your buyer. The data architecture version of this insight is: firmographic filters describe a segment, signal-based targeting identifies timing.

If your outbound system is built on batch enrichment of firmographic lists, you are leaving response rate on the table. The companies seeing 4x higher reply rates in our benchmark data are the ones that have rebuilt their targeting around intent signals and behavioral data.

Full article: artemisgtm.ai/blog/why-most-b2b-companies-get-icp-wrong

GTM Engineering: Treating Your Revenue Stack Like Software

Tom Regan — Wed, 18 Feb 2026 15:10:27 +0000

If you work on the technical side of a B2B SaaS company, you already treat your product codebase with rigor. Version control. CI/CD. Automated testing. Code review. Monitoring. Alerting.

Now look at how your company treats its revenue stack. Manual CSV imports. CRM fields nobody maintains. Lead routing logic from 2022. Outbound sequences built once and never iterated. No monitoring. No alerting. No version control on anything.

The traditional approach to fixing this is a GTM audit. A consulting firm spends six weeks pulling data, interviewing people, and producing a 90-page slide deck with recommendations. Cost: $30K. Implementation rate: near zero.

GTM Engineering takes a different approach. Treat the revenue stack like software.

Diagnose with automated agents. Connect an AI to the CRM and enrichment layer. Let it analyze conversion rates across funnel stages, identify routing gaps, measure response times, and map enrichment coverage. This takes minutes, not weeks.

Ship fixes in sprints. Take the three highest-impact leaks and build implementations. Speed-to-lead routing automation. Enrichment waterfall configuration. Sequence optimization. Deploy them the way you would ship product code. Incrementally. With testing. With measurement.

Measure velocity. Track pipeline velocity metrics the way you track deployment frequency. How fast are leads being contacted? What is the conversion rate at each handoff? Where are records decaying?

The five most common leaks from an engineering perspective: routing latency (42-hour average lead response time), sequence termination (campaigns ending at 3 touches when data shows responses peak at touches 7 to 12), dead record accumulation (MQL graveyard), schema misalignment between marketing and sales qualification criteria, and single-provider enrichment creating systematic data gaps.

All of these are infrastructure problems, not strategy problems. They get fixed by engineering, not consulting.

Full breakdown: artemisgtm.ai/blog/gtm-audits-dead-gtm-engineering

AI-Led Growth: What Happens When Pipeline Generation Becomes a Compute Problem

Tom Regan — Tue, 17 Feb 2026 19:36:08 +0000

If you are building or maintaining the revenue infrastructure for a B2B SaaS company, the architecture of how pipeline gets generated is shifting underneath you.

For a decade, growth has been either Sales-Led (scale SDRs) or Product-Led (scale free tiers). Both are fundamentally linear. SLG scales with headcount. PLG scales with product surface area. Both hit ceilings. Both get expensive.

AI-Led Growth changes the constraint. When pipeline generation runs on AI agents instead of human labor or product mechanics, it becomes a compute problem. And compute scales differently than headcount.

What the technical architecture of ALG looks like:

Visitor intelligence layer de-anonymizes website traffic and scores intent signals in real time. Enrichment waterfall validates and fills contact data through multiple providers before any outreach happens. AI agents generate personalized multi-channel outreach based on verified data, intent signals, and account context. Routing logic directs the highest-value opportunities to human reps at the optimal moment.

The CRM becomes an operating system, not a database. Records get enriched, scored, and routed automatically. Sequences get triggered by behavioral signals, not manual list building. The AI handles the top of funnel. Humans handle the conversations that require judgment and relationship.

Right now, we are in the hybrid phase. ALG plus SLG. AI handles 80% of the repetitive GTM work. Humans close. In 2 to 3 years, the motion will be fully autonomous for a significant percentage of the pipeline.

If you are building systems for a revenue team, understanding this shift matters. The infrastructure requirements for ALG are different from traditional sales tooling. Real-time data flows. Multi-provider enrichment. Event-driven automation. Confidence scoring on every record.

Full breakdown: artemisgtm.ai/blog/ai-led-growth-alg

Building an AI-Powered GTM Audit Tool: A Technical Breakdown

Tom Regan — Sat, 10 Jan 2026 00:58:57 +0000

The Problem

After 10 years in building SaaS startups in sales intelligence, I kept seeing the same pattern:

B2B companies spending millions on sales and marketing tools, but losing hundreds of thousands to operational gaps nobody was measuring.

Lead response time? Nobody tracked it.

MQL rejection rate? "That's just how it is."

Pipeline velocity by stage? "We'd need a data analyst for that."

So I built a tool to automate the audit: Artemis GTM

Update (2026): the Flash Audit described here has been retired. Artemis GTM is now a GTM engineering consulting firm that builds GTM systems: artemisgtm.ai.

Here's how it works under the hood.

The Architecture
┌─────────────────────────────────────────────────────────┐
│ Frontend (React) │
│ - Multi-step audit form │
│ - Real-time validation │
│ - Dynamic question branching │
└─────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ Analysis Engine │
│ - Industry benchmark comparison │
│ - Revenue impact calculations │
│ - Leak identification & prioritization │
└─────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ Supabase Backend │
│ - Audit data storage │
│ - User session management │
│ - Analytics & reporting │
└─────────────────────────────────────────────────────────┘

The Data Model

Industry Benchmarks

The core of the audit is comparing user metrics against industry benchmarks. I built a structured benchmark database:

const INDUSTRY_BENCHMARKS = {
'SaaS': {
deal_size: 15000,
sales_cycle: 45,
win_rate: 25,
lead_to_opp: 15,
pipeline_coverage: 3.5,
lead_response_time: 5, // minutes
},
'FinTech': {
deal_size: 35000,
sales_cycle: 75,
win_rate: 20,
lead_to_opp: 12,
pipeline_coverage: 4.0,
lead_response_time: 5,
},
// ... more industries
};

Growth Motion Variations

B2B companies operate differently based on their growth motion. I account for three models:

type GrowthMotion = 'PLG' | 'SLG' | 'Hybrid';

const PLG_METRICS = {
trial_to_paid: 7, // %
activation_rate: 40, // %
expansion_revenue: 30, // %
};

const SLG_METRICS = {
meetings_per_sdr: 12,
demo_to_proposal: 50, // %
enterprise_deal_size: 50000,
};

The Analysis Engine

Revenue Leak Detection

Each audit answer feeds into a leak detection algorithm:

interface RevenueLeak {
issue: string;
severity: 'CRITICAL' | 'HIGH' | 'MEDIUM' | 'LOW';
revenue_impact: string;
evidence: string[];
recommendation: string;
}

function detectLeaks(auditData: AuditData, benchmarks: Benchmarks): RevenueLeak[] {
const leaks: RevenueLeak[] = [];

// Lead Response Time Analysis
if (auditData.lead_response_time > 60) { // minutes
const impactMultiplier = calculateResponseImpact(
auditData.lead_response_time,
benchmarks.lead_response_time
);

leaks.push({
  issue: 'Lead Response Time Exceeds Benchmark',
  severity: auditData.lead_response_time > 1440 ? 'CRITICAL' : 'HIGH',
  revenue_impact: formatCurrency(
    auditData.monthly_leads * 
    impactMultiplier * 
    auditData.deal_size * 
    auditData.conversion_rate * 
    12
  ),
  evidence: [
    `Current response time: ${auditData.lead_response_time} minutes`,
    `Benchmark: ${benchmarks.lead_response_time} minutes`,
    `Response rate degradation: ${(impactMultiplier * 100).toFixed(0)}%`
  ],
  recommendation: 'Implement automated lead routing with <5 min SLA'
});

}

// ... more leak detection logic

return leaks;
}

Revenue Impact Calculation

The revenue impact formulas are based on published research and validated against real company data:

function calculateLeadResponseImpact(
actualMinutes: number,
monthlyLeads: number,
dealSize: number,
baselineConversion: number
): number {
// Illustrative decay curve for the estimate, not a study figure.
// Direction only: the Lead Response Management study (Oldroyd, 2007)
// found roughly 21x higher odds of qualifying at 5 minutes than at 30.

let conversionMultiplier = 1;

if (actualMinutes > 5) {
conversionMultiplier *= 0.79; // 21% drop after 5 min
}
if (actualMinutes > 30) {
conversionMultiplier *= 0.20; // 80% drop after 30 min
}
if (actualMinutes > 60) {
const additionalHours = Math.floor((actualMinutes - 60) / 60);
conversionMultiplier *= Math.pow(0.9, additionalHours);
}

const lostConversions = monthlyLeads * baselineConversion * (1 - conversionMultiplier);
const annualImpact = lostConversions * dealSize * 12;

return annualImpact;
}

Dynamic Question Branching

The audit adapts based on answers. PLG companies get different questions than enterprise SLG motions:

function getNextQuestion(
currentAnswer: Answer,
growthMotion: GrowthMotion,
companyStage: CompanyStage
): Question {
if (growthMotion === 'PLG') {
return PLG_QUESTIONS[currentQuestionIndex + 1];
}

if (growthMotion === 'SLG' && companyStage === 'Enterprise') {
return ENTERPRISE_SLG_QUESTIONS[currentQuestionIndex + 1];
}

// Default B2B flow
return STANDARD_QUESTIONS[currentQuestionIndex + 1];
}

The Frontend

Built with React + TypeScript + Tailwind. Key components:

Multi-Step Form

const AuditForm: React.FC = () => {
const [stage, setStage] = useState<'intro' | 'audit' | 'results'>('intro');
const [currentQuestion, setCurrentQuestion] = useState(0);
const [auditData, setAuditData] = useState({});

const handleAnswer = (answer: Answer) => {
setAuditData(prev => ({ ...prev, [currentQuestion]: answer }));

if (currentQuestion < questions.length - 1) {
  setCurrentQuestion(prev => prev + 1);
} else {
  runAnalysis();
}

};

// ...
};

Results Visualization

The results page shows:

Overall health score (0-100)
Revenue leaks ranked by impact
Prioritized recommendations
Implementation timelines

const HealthScore: React.FC<{ score: number }> = ({ score }) => {
const color = score >= 70 ? 'green' : score >= 40 ? 'amber' : 'red';

return (

{score}
GTM Health Score

);
};

Data Security

Since we're handling business metrics:

All sensitive fields encrypted at rest (AES-GCM)
Row-level security in Supabase
No PII stored (email hashed for returning user lookup)
GDPR-compliant data handling

async function encryptSensitiveData(data: string): Promise {
const encoder = new TextEncoder();
const keyData = encoder.encode(ENCRYPTION_KEY);

const cryptoKey = await crypto.subtle.importKey(
'raw',
keyData,
{ name: 'AES-GCM' },
false,
['encrypt']
);

const iv = crypto.getRandomValues(new Uint8Array(12));
const encrypted = await crypto.subtle.encrypt(
{ name: 'AES-GCM', iv },
cryptoKey,
encoder.encode(data)
);

// Combine IV + encrypted data for storage
return base64Encode(iv) + '.' + base64Encode(encrypted);
}

What I Learned

Benchmarks are tricky
Initial benchmarks came from industry reports, but real-world validation showed significant variance. I had to build in confidence intervals and segment by company size, not just industry.
Users don't know their own metrics
Most users can't answer "What's your average lead response time?" from memory. I added the option to use industry benchmarks as defaults, with the ability to override.
The analysis has to be actionable
Early versions showed the leaks but didn't prioritize them. Users were overwhelmed. Now everything is ranked by revenue impact × effort to fix.

Try It

Artemis GTM

What's Next

API access for teams to run programmatic audits
CRM integrations to pull real data automatically
Benchmarking against anonymized peer data

If you're working on similar problems in RevOps tooling, I'd love to connect.

Built by Tom Regan, founder of Artemis GTM and builder of tools that probably should have existed years ago.

https://artemisgtm.ai