PAVAN RAJPUT / GTM strategist & system builder
From identifying billion-dollar opportunities and redesigning business models to building markets, GTM motions and AI-native commercial systems.
$1.5M
Singapore business built from zero. No playbook, no inherited pipeline
$1.9B
New market opportunity identified for a conversational AI company & built the market-entry and GTM strategy to pursue it.
IP-LED GROWTH
Designed an asset-based consulting model for NTT DATA, transforming a traditional services proposition toward scalable, reusable IP-led offerings.
5+
Live AI-native GTM systems, built solo. Multi-agent tools for market prioritisation, signal intelligence, enterprise pursuit strategy and GTM execution.
01 / WHAT I DO
The best commercial systems connect insight, execution, and revenue.
Find the Market
Market insight
Marketing entry strategy
ICP & segmentation
Opportunity sizing
Competitive intelligence
Buyer & customer insight
Build the Motion
Positioning & value proposition
GTM strategy
Commercial model design
Signal intelligence
AI-powered workflows
Sales enablement
Win the Revenue
Enterprise hunting
Pursuit strategy
Business cases
C-level engagement
Multi-stakeholder selling
02 / SELECTED GTM BUILDS
AI-native commercial tools for the moments that matter. All built solo with no-code tools.
03 / OPERATING PRINCIPLE
Human-led. AI-accelerated.
More signals do not create better GTM. Better judgement does.
Modern GTM stacks can detect almost anything: intent, job changes, funding, hiring, product launches, web activity, technology shifts and behavioural signals.
The problem is not finding more signals. It is knowing which signals actually mean something in the context of the customer problem you solve.
I believe strong GTM starts by separating noise from evidence: identifying the few signals that genuinely change the probability of need, timing or fit. Then, using them to answer three questions:
Who should we target? Why now? What should we say?
That is the logic behind the systems I build. Not bigger lists, not more automation, and not “personalisation” for its own sake. The goal is to turn fragmented market evidence into a smaller number of higher-conviction commercial decisions.
AI should help compress that reasoning, not replace it.
04 / HOW I BUILD AI-NATIVE GTM SYSTEMS
From commercial problem to repeatable decision system.
01. Commercial problem
Start with the commercial decision that matters, and the cost of getting it wrong.
In practice: Which accounts actually deserve a seller’s time?
02. Hypothesis
State what you believe, and what evidence would change your mind.
In practice: Payout volume signals may predict buying intent better than firmographics.
03. Break down the workflow
Map how the best seller actually works, one decision at a time.
In practice: Prioritise → Pursue → Pitch → Prove ROI → Compete → Close.
04. Encode judgement
Turn experience & instinct into rules a system can apply consistently.
In practice: No flow-of-funds fit, no place on the list. However big the account.
05. Build specialist agents
One agent, one job. Narrow beats clever.
In practice: Four agents in Pursuit Command, each owning a distinct part of the deal.
06. Orchestrate
Connect the agents so context and judgement compound across the workflow.
In practice: Six agents in Gigs, working as one connected GTM motion.
07. Human accountability
The system recommends. A person decides and owns the outcome.
In practice: Every tool ends in a choice, not an action.
05 / BUILDING GROWTH IN DIFFERENT WAYS
06 / PRODUCT & GTM THINKING
Product teardowns for the commercial questions behind great products
My background spans market intelligence, strategy consulting, GTM and enterprise sales, so I tend to look at products through a commercial lens rather than a purely product or marketing one.
I use these teardowns to study how companies identify customer problems, shape differentiated value, position products, create emotional connection and choose the right growth motion - whether product-led, sales-assisted, ecosystem-led or something in between.
The goal is not to critique products for the sake of it. It is to sharpen pattern recognition across categories and understand what makes a product resonate, scale and create durable commercial value.
07 / WHAT I AM THINKING ABOUT NOW
AI-native GTM
How AI can become part of the commercial operating system itself, improving how teams identify opportunity, prioritise accounts, prepare decisions and execute complex GTM motions.
New Models of Market Creation
How emerging categories are built when the buyer, use case and commercial model are still forming, and how teams can turn weak market signals into focused bets, early lighthouse wins and repeatable demand.
Human Judgement in an AI World
Where AI should accelerate commercial decisions, and where experience, customer context, relationships and accountability still matter more than automation.
08 / ABOUT
My career has moved steadily closer to the revenue.
I started in market research, learning how customers, categories and markets actually behave. I moved into strategy consulting to help companies decide where to play, how to compete and where to place their bets. Then I moved into enterprise sales because I wanted to own the commercial outcome, not stop at the recommendation.
Along the way, I have built an APAC business from zero, shaped market-entry and GTM strategies, redesigned business models, identified billion-dollar growth opportunities, and carried an enterprise revenue number.
What ties all of that together is a simple pattern: I am most useful when the problem is still ambiguous, the market is moving, and the path to growth isn’t obvious.
More recently, I have been exploring how that judgement can be encoded into AI-assisted commercial systems - not to automate thinking, but to make good decisions faster, more repeatable and easier to scale.
2007–2013
Learning how markets behave
Markelytics: led the India research business, from customer segmentation to product launches.
KnowledgeFaber: first market-entry and GTM consulting, building a new ecommerce and payments practice.
2014–2017
Building from zero
Cross Marketing: built the APAC office from nothing to $1.5M in three years, with no clients, pipeline or playbook.
2017–2024
Strategy from both sides
April International: ran market intelligence inside a multi-billion-dollar business.
Forrester: advised tech, FSI and services firms on market entry, positioning, GTM and customer strategy.
2024–NOW
Carrying the number
Forrester: enterprise sales across ESEA. 104% and 106% of plan, 80% from new business I sourced myself.
NOW
Building AI-native GTM tools
Turning 15+ years of commercial judgement into working systems.
Interested in building something difficult?