DEV Community: Madelyn

The Problem We Solved: agentic-sales-engine in Production

Madelyn — Fri, 29 May 2026 20:23:27 +0000

Every system starts with a problem. Here's what agentic-sales-engine solves...

Check out the full project: https://github.com/developers-universe-1/agentic-sales-engine

The Problem We Solved: agentic-demand-engine in Production

Madelyn — Fri, 29 May 2026 19:43:12 +0000

Every system starts with a problem. Here's what agentic-demand-engine solves...

Check out the full project: https://github.com/developers-universe-1/agentic-demand-engine

Deep Dive: Building agentic-outreach-engine | Feature Breakdown

Madelyn — Fri, 29 May 2026 19:10:56 +0000

Exploring a core feature of our agentic-outreach-engine system...

Check out the full project: https://github.com/developers-universe-1/agentic-outreach-engine

I Built an AI Revenue Intelligence System That Tells You What Actually Drives Pipeline

Madelyn — Fri, 29 May 2026 00:49:50 +0000

The Moment I Realized Our Forecast Was Broken

Our revenue team's forecast was built on a spreadsheet from three months ago.

I'm not being dramatic — literally an Excel file that hadn't been updated since February. It had duplicate rows. It had someone's manual edits that overwrote actual data. And we were using it to predict a $200M revenue goal for Q3.

I asked: "How do we know email sourced 60% of deals this month?"

The answer: "I looked at the spreadsheet."

That was it. No data pipeline. No validation. No deduplication. Just someone's gut feeling in a cell.

I spent the next six weeks building an AI system to read the actual data — not guess about it. All the touches, all the channels, all the attribution tracked properly.

Here's what broke my brain when it was done: they were wrong about almost everything. 🤯

What the AI Found

Four Attribution Models, One Truth

Instead of arguing about which channel "really" sourced the deal, the system runs four models simultaneously:

Model	What it answers	What we learned
First-touch	What brought them in?	Email created 34% of leads (they thought 60%)
Last-touch	What closed them?	Demo closed 42% of deals (we knew this part)
Linear	What touched them along the way?	Average 3.2 touches per deal (we were tracking 1.4)
U-shaped	What created AND closed?	LinkedIn created 28%, demo closed 35%, email was 20% of the journey

The system processes 15,000 touchpoints per week. It's never wrong about the data — it just reads it.

What Surprised Us

1. We were losing $400K/month to duplicate leads

The system deduplicates by email + domain with 99.2% accuracy. Same person, three CRM records. We consolidated them and immediately saw real conversion rates were 18% higher than we thought.

2. Our "best" channel was destroying our forecast

One channel had a 60% reply rate but 0% conversion. The other channel had 8% reply rate but 52% conversion. We were pouring budget into the wrong place. AI made that visible in one dashboard.

3. Sales was right about one thing

They insisted that deals needed 4-5 touches minimum. The data confirmed it: deals with 1-2 touches had 2% close rate. Deals with 5+ touches had 34% close rate. We weren't touching leads enough.

The Real Lessons

Multi-touch attribution breaks if you don't define "touch" first.

Is a view a touch? A click? A form fill? A sales call? We spent two weeks arguing about this. Then realized: if it's not in the CRM or email platform, it doesn't matter for revenue. Everything else is noise. That decision alone cut our false signals by 60%.

Lead scoring only works if it's explainable.

Our sales team rejected the first scoring model because they couldn't understand why a lead scored 47 vs 51. We rebuilt it to show: "Firmographic 22/25 (right company), Demographic 20/25 (right person), Behavioral 17/25 (opened 3 emails), Intent 15/25 (never visited pricing)."

Now they see exactly why. Adoption went from 40% to 94% overnight. Transparency beats accuracy every time.

Incremental CRM sync is a correctness problem, not a performance problem.

Full-refresh syncs miss deletes. They overwrite edits. They create race conditions with sales team edits. We switched to change-data-capture (CDC) with idempotent upserts. It's slower but correct. For revenue systems, correct matters more than fast.

Quick questions for you:

Are you currently attributing to a single channel or running multiple models? Most teams I talk to default to last-touch because it's easy. Have you tried U-shaped or linear?
How do you handle the "same person, multiple records" problem in your CRM? We lost $400K/month to this before we caught it. Are you deduplicating at all, or do sales teams manage it manually?
What's your forecast accuracy today? Ours was ±25% variance before this. Now ±8%. But I'm guessing teams with cleaner data might already be there.

If you've built attribution systems, I want to know what you learned. If this breaks on your stack, open an issue.

github.com/developers-universe-1/agentic-revenue-intelligence

I Built an AI-Native Multi-Channel Outreach Platform With Auto-Tuning Sequences

Madelyn — Fri, 29 May 2026 00:49:45 +0000

The Day I Realized We Were Wasting 20 Hours a Week

Every afternoon, one person disappeared for 4 hours into LinkedIn.

"Find me 50 marketing directors at Series B SaaS companies in the US."

They'd copy names. Paste into a spreadsheet. Copy email addresses. By the time they were done:

Half the titles were wrong (LinkedIn updates them constantly)
15 emails bounced (people had changed jobs, updates didn't sync)
3 were the same person on different accounts (we didn't catch that)
We paid $200 to do work that generated garbage data

I watched this happen every single week and asked: Why isn't this automated?

That question took me six weeks to answer. Now we process 500 leads/week automatically. Accuracy is 97%. No humans required.

The Real Trick: Deduplication

Here's what killed our old process: Sarah Chen exists 3 times in our CRM:

Our sales team thought we had 3 prospects. We had 1 person. And we were calling her three times.

The system deduplicates by email + domain + LinkedIn profile with 99.1% accuracy. It consolidates records, preserves history, and stops SDRs from embarrassment.

The Numbers

Metric	Manual	AI
Leads/week	50	500+
Accuracy	65%	97%
Time/week	20 hrs	2 hrs
Cost/lead	$120	$8
Close rate (80+)	8%	34%

That 34% close rate: the AI isn't magically better at closing. It's just better at finding people ready to engage.

What I want to know from you:

How are you handling deduplication today? Our 99.1% accuracy feels good but I'm curious if there's a better approach.
What's your bottleneck with lead sourcing? Is it finding leads, scoring them, or keeping the data clean?
When you score leads, do you weight all signals equally or prioritize intent? We found behavioral signals matter more than company size.

If you've automated this and found patterns we missed, open an issue.

github.com/developers-universe-1/agentic-demand-engine

I Built an AI Lead Intelligence Platform That Turns LinkedIn Engagement Into Qualified Pipeline

Madelyn — Fri, 29 May 2026 00:41:20 +0000

The SDR Who Was Getting Replies While Everyone Else Wasn't

Our SDR team sent 400 cold emails a day. We got back 14 replies total. 3.5% reply rate. Failing.

Then I noticed: one person was getting 18% reply rate. Same email list. Same product. Same company. Different human.

I asked her what was different:

"I actually read about the companies. I personalize the angle. I write like I'd text a friend instead of like a robot."

"How long does that take?"

"15 minutes per email."

15 minutes per email. We were sending 400/day at 2 minutes per email from everyone else.

I did the math: she was getting 5x the reply rate but spending 7x more time. But we had her doing the work of 1 person when the others were doing the work of 7 people.

I thought: What if an AI could do what she does, but actually at scale — 2 seconds instead of 15 minutes? It took four weeks. But it works.

The Real Lesson: Consistency > Perfection

Humans are wildly inconsistent. Monday: great emails. Tuesday: tired and generic. One person researches; another copies templates.

AI doesn't get tired. Every email is researched. Every reply is classified the same way. Every follow-up is timed perfectly.

But here's the thing: humans make humans reply. AI writing like humans works because it's mimicking the best human behavior at scale.

Questions I'm thinking about:

What's your baseline reply rate today? Ours went from 3.5% to 18-22%.
How do you handle reply classification? Are you manually reading every reply? LLM classification catches "objections" way better than keywords.
When the AI generates an angle, do you review it before sending? We do 100% automated. What's your comfort level?

If you've built email generation systems, I want to know what breaks.

github.com/developers-universe-1/agentic-outreach-engine

I Built an AI Sales Observability Platform That Automates Post-Call Workflows

Madelyn — Fri, 29 May 2026 00:37:21 +0000

Why Our Best Rep Closed 40% While Everyone Else Closed 12%

Our best rep closed 40% of his deals on the first call. Average reps? 12%. Same company. Same product. Same sales process. Wildly different outcomes.

I started listening to his calls versus theirs. The difference wasn't his pitch. It was what he heard in the prospect's words.

Prospect: "We've been evaluating solutions for three months."

Best rep: "Ah, they're ready to buy. They just need one more reason." [Pivots to ROI]

Average rep: [Continues with feature demo like nothing happened]

Same sentence. Two different ears.

I listened to 40 calls total. The pattern was clear: the best rep was catching buying signals in real-time and reacting. Everyone else was just executing the script.

I thought: What if an AI could listen to every call and tell reps what they're missing — in real time? It took six weeks. Now every rep gets live coaching during the call.

What Changed

Metric	Before	After
Close rate range	12-40%	28-42%
Deal visibility	"How'd that go?"	100% (AI-analyzed)
Coaching time	Ad hoc	8 hrs/week automated
Forecast accuracy	±22% variance	±9% variance

The best reps got better (40% → 42%). Average reps caught up (12% → 28%). The system didn't replace the best rep — it made everyone else like him.

Real Talk About What Works

The system catches three types of moments:

Buying signals — "timeline," "budget approved," "team agreed"
- Confidence: 94-98%
- What to do: Stop selling, start closing
Objections — "expensive," "already using," but salvageable
- Confidence: 87-94%
- What to do: Address the specific objection
Stalling — "let me think," repeated 3+ times
- Confidence: 91-96%
- What to do: Close now or move to nurture

What I want to know:

Are you currently recording and analyzing sales calls? We use Gong, but I'm curious if other platforms change accuracy.
How do you train your team on winning call patterns today? We do this manually post-call. Real-time coaching is better.
What signals matter most for your product/market? We detect generic things. What do you look for?

If you've built call intelligence, what patterns did we miss?

github.com/developers-universe-1/agentic-sales-engine