GTM engines we've shipped
GTM Lead Processing Pipeline
The problem
A B2B AI sales SaaS had raw lead lists from every channel, website visitors, LinkedIn engagement, conference exhibitors, Clay enrichment. No consistent scoring, no prioritization, no way to know which 50 of 5,000 leads actually fit ICP. SDRs were working leads by feel.
The solution
A pipeline that takes any raw lead CSV and turns it into a ranked, campaign-ready list. It normalizes messy industry data, scores every contact against a 100-point ICP model, and researches each account automatically before handing off a file your sequencer can run immediately.
5,000 raw
Tiered A→D
leads scored, ranked, ready to work
minutes
from raw CSV to campaign-ready
Account Research & Messaging Engine
The problem
Even scored leads need personalized outreach. Manually researching each Tier-1 account's product catalog, CRM setup, and sales operations took hours per company. The bottleneck wasn't scoring, it was writing the pitch. SDRs stared at blank pages.
The solution
Automated research on every top-tier account, product lines, sales channels, and how ready they are to buy, then a messaging engine picks the right angle (a new hire, a product gap, a growth signal) and drafts an outreach message ready to send. No SDR ever starts from a blank page.
Hours/account
Minutes
to research and write outbound
150+
accounts profiled, never a blank page
From 20 hours of manual work to zero.
The client
A growing climate finance company with a lean operations team. Strong deal flow, ambitious pipeline, but their ops infrastructure hadn't kept pace. Every new deal created more manual work.
The challenge
- >Deal presentations created manually for every prospect
- >Email activity captured inconsistently, ~60% at best
- >Compliance screening done ad-hoc, no standard process
- >Call insights trapped in recordings, never actioned
Impact
| Metric | Before | After |
|---|---|---|
| Manual hours per week | ~15-20 hrs | 0 |
| Presentation creation time | 15-30 min | < 1 min |
| Email activity capture rate | ~60% | 100% |
| Compliance screening coverage | Ad-hoc | 100% |
| Cost per automated action | ~$25-50 (analyst time) | < $0.05 |
The ecosystem
AI-Powered Deal Presentation Generator
Problem
Creating a deal presentation took 15-30 minutes per deal. Slides were inconsistent, often outdated, and entirely dependent on one analyst.
Solution
n8n workflow triggered by HubSpot deal stage change. Pulls deal data from CRM, generates formatted slides via GPT-4o and Google Slides API, uploads to Drive, and posts to Slack.
< 1 min generation time. $0.02 per presentation.
Zero-Friction Email Activity Tracker
Problem
Only ~60% of email activity was making it into HubSpot. Reps weren't logging calls. Deal history was incomplete.
Solution
Automated Gmail → HubSpot sync via n8n. Every email sent or received is parsed and associated with the correct contact and deal automatically.
100% capture rate. Zero manual logging.
Automated Compliance Screening
Problem
Compliance checks were ad-hoc and inconsistent. New deals might go unscreened for days.
Solution
n8n workflow triggered on new HubSpot deal. Runs adverse media search via Serper API, summarizes findings with GPT-4o, and delivers a structured report to Slack.
100% of deals screened. Flags in < 2 minutes.
AI Seller Communication Extraction
Problem
Valuable insights from sales calls lived in recordings, inaccessible and unsearchable.
Solution
Ingests Fireflies and Sybill transcripts, extracts structured deal signals with GPT-4o, and writes back to HubSpot custom fields.
Currently in validation.
wired into the client's stack: hubspot / n8n / gpt-4o / google slides api / google drive api / apps script / serper api / slack / fireflies / sybill
See each workflow in detail
Every automation built for this client, what it does, how it works, and what changed.
AI-Powered Deal Presentation Generator
Every time a deal went live, someone had to manually copy-paste 15+ data fields into a slide template. We automated the whole thing, from CRM trigger to finished Google Slide, in under 60 seconds.
GPT-4o rewrites the raw seller description into polished buyer-facing copy. A master-file architecture means the presentation URL never changes across updates, and full version history is preserved in every file.
What it's like to work with mkdir
this is the most beautiful thing
> Principal, Tax Investor CoverageClient under NDA
This is one of those things that seems like it's small but it's huuuuuge
> Head of OperationsClient under NDA
I genuinely thought this was a problem that would require PDE to solve, but Varun proved me wrong. EXCEPTIONAL.
> Director, Revenue Strategy & OperationsClient under NDA
$ mkdir meetings
New customers, without hiring a salesperson.
Book a free 30 minute call. We will show you exactly which companies we would go after for you, and what it would take.