We run the GTM engine that turns leads into pipeline.
mkdir is the RevOps + GTM engineering team B2B sales orgs hire to score leads, research accounts, write outbound, and wire it all into HubSpot — without adding headcount.
Wired into your GTM stack
“Your SDRs are working leads by feel.”
Five thousand raw leads in a spreadsheet. No consistent scoring, no prioritization, no idea which fifty actually fit ICP. Reps guess. Pipeline lags. Most B2B teams don't need another SDR — they need the GTM engine that decides which accounts are worth the SDR's next hour.
Every GTM step is a decision tree, not a script.
A real GTM engine has to decide: is this lead worth scoring? Is this account worth researching? Is this call worth a follow-up sequence? Linear if-this-then-that breaks the moment reality stops matching the happy path.
This is a real workflow we shipped: an AI pre-check that decides whether a sales call is worth a full transcript extraction — or whether to exit early and save the compute spend.
The full GTM engine — built and operated for you
GTM Engineering
Lead scoring, ICP modeling, account research and prioritization — your raw lists turned into a ranked pipeline.
Learn more →Outbound Systems
Personalized outreach at scale: signal-driven messaging, Clay enrichment, LemList cadences wired into HubSpot.
Learn more →Revenue Operations
CRM hygiene, pipeline reporting, compliance screening, and call-intel — the back office of your GTM engine.
Learn more →GTM engines and RevOps systems 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
Six-step Python pipeline that takes any raw lead CSV and outputs a campaign-ready file: 278 industry labels normalized to 50 canonical, 100-point ICP score (industry, title, size, revenue, tech, geo), hard filters, automated product research, 9-signal account prioritization, LemList export with merge fields populated.
5,000 raw
Tiered A→D
leads scored, ranked, ready to work
minutes
from raw CSV to campaign-ready
Account Research & Messaging Engine
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.
Minutes
to research and write outbound
this is the most beautiful thing
— Principal, Tax Investor Coverage
Built for B2B sales teams. By someone running the engine.
GTM engineers, not generalists
ICP scoring, account research, signal-driven outbound — built by someone who runs the GTM engine, not just connects apps.
Your stack, not ours
HubSpot, Clay, Apollo, LemList, n8n — we build inside what you already pay for, no migration tax.
Zero behavior change for SDRs
Reps get a tiered queue and pre-filled briefs. They keep working in HubSpot and LemList — the system runs underneath.
Project-based, paid on delivery
Defined scope, fixed timeline, shipped output. No retainers to figure things out, no junior hand-off.
The GTM engineer
running the engine for you
Varun Bagrodia
linkedin →I run the GTM engine for B2B sales orgs — the system that decides which accounts your SDRs work next, what to say to them, and how the whole thing gets back into HubSpot.
Currently building the GTM operationalization for a B2B AI sales SaaS — ICP scoring on 5,000+ leads, automated account research on 150+ Tier-1 targets, signal-driven outbound wired into LemList. Previously shipped four interconnected RevOps automations for a climate finance company: deal-deck generation, email activity capture, compliance screening, call-transcript extraction.
Before that: financial risk management at Credit Suisse, quantitative work in portfolio optimization. Master's in Engineering Management (Finance) from Duke (GPA 3.82), BE in Mechanical Engineering from BITS Pilani.
mkdir is the vehicle for that work — no large team, no overhead, no hand-off to a junior. You work directly with me.
"I don't hand off to a junior. I don't sell you a retainer to figure things out. I come in, find the leverage point, and ship something that works — then I disappear until you need me again."
— Varun Bagrodia, Founder
From the mkdir blog
How we cut deal presentation time from 30 minutes to under 60 seconds
Sales and deal teams routinely spend 15–30 minutes building presentations by hand for every deal. We built an event-driven pipeline that triggers on CRM deal stage changes, pulls the deal data, generates copy with GPT-4o, and populates a Google Slides template — in under a minute, with no human involvement.
What's covered
- —Triggering n8n from HubSpot deal stage changes
- —Structuring GPT-4o prompts for consistent marketing copy
- —Master file architecture for version-tracked presentations
Turning sales call transcripts into structured CRM data automatically
Valuable insights from sales calls get trapped in recordings — inaccessible and unsearchable. We built a workflow that ingests Fireflies and Sybill transcripts, extracts 21 structured deal fields using GPT-4o, and writes them back to HubSpot custom properties — no manual entry required.
Common questions
What is mkdir?+
mkdir is the GTM engineering and RevOps team B2B sales orgs hire to build the system that turns raw leads into pipeline — ICP scoring, account research, signal-driven outbound, and the CRM plumbing that ties it together.
How is this different from hiring a GTM engineer?+
A full-time GTM engineer is a $150k–$200k commitment with undefined scope and no shipped output for months. mkdir is project-based: defined scope, fixed timeline, and a working system in production. Prove the work first, then bake it into payroll if you want to.
What does a typical engagement look like?+
Three phases: a one-week GTM Audit that maps your funnel and identifies the highest-leverage builds; a Build & Deploy phase with a custom timeline (usually 2–6 weeks per project); and an optional monthly Retainer to maintain and extend the engine as your team grows.
How is this different from Zapier or Make?+
Zapier and Make connect apps with linear if-this-then-that triggers. mkdir builds branching, AI-decisioned workflows that score leads on a 100-point ICP model, research accounts at depth, extract structured data from transcripts and PDFs, and run end-to-end without human review on the steps that don't need it.
What tools does mkdir work with?+
HubSpot, Clay, Apollo, LemList, Vector, n8n, Python, OpenAI (GPT-4o), Slack, Fireflies, Sybill, Google Slides, Google Sheets, Gmail, and Serper API. We build inside whatever stack you already have rather than asking you to migrate.
Who runs mkdir?+
Varun Bagrodia — founder. Currently runs the full GTM operationalization for a B2B AI sales SaaS. Previously shipped four interconnected RevOps automations for a climate finance company. Background: financial risk management at Credit Suisse, Master's in Engineering Management (Finance) from Duke, BE in Mechanical Engineering from BITS Pilani. You work directly with Varun on every engagement; nothing is handed off to a junior.
Meet Home Relay — the AI voice agent for property maintenance.
Built by the same team behind mkdir. Home Relay's AI agent answers tenant maintenance calls 24/7, captures the issue, creates the ticket, and dispatches a technician — all before the property manager wakes up.
Same playbook as the GTM engine: take the manual, repetitive, after-hours work off the team's plate and let a system handle the long tail.
Ready to stop doing things manually?
Book a free 30-minute consultation. We'll audit one of your workflows on the call.