Garima Kalra

Strategist. Builder. Multiplier.

Currently building Work Intelligence for Fortune 500 clients @ Lightcast.

See My Work →LinkedIn

The blueprint

The deadly combo most teams need but rarely find.

I strategize.

Multi-year platform integration strategy post-acquisition
New market vertical entry (SaaS)
Central Admin layer — Multiple products
Sunset of a new Product

I build.

6x 0→1 Product lifecycle
CRM & Billing platforms
One-click migration tool → $500K OPEX cut
Working prototypes for customer validation

I multiply.

Global IAM Platform across 100+ countries
Zero-touch onboarding for F-500 clients
Mentored PMs across platform & growth
Established product culture and roadmap rituals

The principles

My Product Philosophy.

01
Talk to users. Then look at the data. In that order.
Quant tells you the what. Qual tells you the why. I've never made a good product decision from a spreadsheet alone.
02
Outcomes over output
Roadmaps aren't to-do lists. Every item on mine is tied to a measurable customer or business outcome. I measure success in impact, not tickets closed.
03
Speed is a strategy
A high-fidelity prototype has killed more bad ideas — and unlocked more good ones — than any meeting ever will. I build them not to look polished, but to make the hard conversations happen faster.
04
Kill your darlings
I recommended deprioritizing a product I spent months shaping — because customer signals told me to. The courage to say no separates good PMs from great ones.

How I work with AI

I work on AI. I work with AI. Both, every day.

Layer 01

Building the intelligence layer for the future of work.

At Lightcast, I'm building Tasks Intelligence — a product that sits on top of one of the world's most complex labor market knowledge graphs and asks a genuinely hard question: what happens to your workforce when AI shows up?

Not in the abstract. Specifically. Task by task, role by role. What gets automated. What gets augmented. Where you need a human in the loop and why. Fortune 500 companies use this to make real workforce decisions — hiring, reskilling, restructuring. The stakes are real. So is the ambiguity. That's the interesting part.

automate → augment → keep human in loop
// this is the framework we're operationalizing at scale

Layer 02

I don't wait for engineers to validate ideas.

I use AI coding tools — Claude, Lovable — to turn a product idea into a clickable prototype in hours, not weeks. The hypothesis goes in, a working thing comes out. I can put it in front of users the same day. Iterate before anyone's written a spec.

This isn't about replacing engineers. It's about collapsing the gap between idea and evidence.

Real example →

Built an ROI Calculator from scratch using AI tools. No engineer, no sprint, no ticket. The consulting team picked it up and started using it with clients. It lives in the wild now.

Layer 03