By playing, you agree to uListen's Terms and Privacy Policy and YouTube's Terms of Service.

Best Place To Build

Suyash Singh, GalaxEye | "If I can't build a deep tech startup at IITM, I can never do it." | Ep. 10

Dive into an extraordinary journey of innovation, passion, and cutting-edge space technology!

Unknown HosthostSuyash Singhguest

Jan 24, 20251h 17mWatch on YouTube ↗

Summaries, chapters, transcripts, and FAQs on this page are made by uListen, not YouTube.

CHAPTERS

Jump to publisher timestamps

Chapter summaries

Deep-tech ambition at IITM: building satellites and India’s space “run” era
The episode opens with Suyash’s core belief: IIT Madras is the place where a deep-tech project must be possible—and if it isn’t, it won’t be anywhere. He also tees up GalaxEye’s long-term plan of scaling from the first satellite to a multi-satellite network and frames India’s inflection point in space innovation.
- •“If I can’t build deep tech at IITM, I can never do it” as a personal trigger
- •Early mention of scaling to 6–8 satellites over the next 4–5 years
- •Positioning India’s space ecosystem as moving from walking to running
- •Setting the stakes: deep-tech execution, not just ideas
The story behind the constant smile: leadership feedback that changed behavior
Suyash explains the unexpected origin of his trademark smile—direct feedback from early Hyperloop recruits who found him “depressing.” The moment becomes a lesson in self-awareness and leadership communication.
- •First Hyperloop orientation session feedback: “You are super depressing”
- •Conscious practice of smiling as a leadership tool
- •How founder demeanor impacts team motivation and culture
From mechanical engineer to IITM aerospace: corporate years, UPSC detour, and GATE reality
Suyash walks through his pre-IIT path: mechanical engineering graduation, a corporate stint, exploring UPSC, and realizing he wanted stronger engineering fundamentals. He explains how GATE works and why he chose aerospace with an application-driven mindset.
- •Graduated mechanical engineering (2013) → corporate experimentation → UPSC prep
- •Motivation to rebuild engineering fundamentals led to pursuing a master’s
- •GATE structure: discipline-specific papers; fairness vs breadth debate
- •Choosing aerospace partially by interest, partially by application potential
Breaking barriers at IITM: starting Avishkar Hyperloop and bridging BTech–Master’s culture
Suyash recounts the pivotal moment hearing about the SpaceX Hyperloop competition and deciding IITM must be able to build something world-class. He details the cultural gap Master’s students faced in joining build teams, and how he secured institutional trust to start anyway.
- •SpaceX Hyperloop competition as the catalyst; “You can’t do this in India” challenge
- •Difficulty for Master’s students to access CFI/build culture vs BTech students
- •Finding allies and earning a leap of faith from institute leadership (no initial funding)
- •Hyperloop team as a ‘bridge’ combining research mindset + innovation mindset
From Hyperloop to GalaxEye: an unplanned shift into space driven by a real problem
Suyash explains that space wasn’t his initial fascination—Hyperloop was. After graduation and a corporate role, a compelling problem statement pulled him into founding GalaxEye.
- •Hyperloop and Agnikul started around the same time, but space wasn’t his focus then
- •Post-2019 corporate offers and choosing one role that led to GalaxEye’s start
- •Founding driven by problem discovery rather than “love of space”
What GalaxEye does: consistent Earth imagery despite clouds and night
GalaxEye is framed as a data company that acquires images from space. The key market pain is reliability: clouds cover ~70% of Earth at any time, and optical imagery fails at night—breaking consistent delivery needed for software applications.
- •Space applications: communication, navigation, imaging—imaging is under-unlocked
- •Cloud cover and lack of light at night disrupt optical image availability
- •Need for consistent ‘service-level’ imagery delivery into customer workflows
- •Origin story: wildfire damage assessment blocked by smoke/clouds
The four pillars of satellite imaging value: mapping, commodities, defense, and asset monitoring
Suyash breaks down where Earth observation creates real-world value, clustering it into four major buckets. The discussion highlights how imagery supports planning, supply intelligence, security monitoring, and infrastructure maintenance at scale.
- •Mapping & surveying for infrastructure planning and layout decisions
- •Commodities monitoring: crop classification, crop health, supply intelligence
- •Defense & intelligence as a major dual-use driver
- •Asset monitoring: transmission lines, encroachment, maintenance prioritization
Deep dive into the tech: SAR vs multispectral and why fusion matters
The conversation shifts from applications to the physics and instrumentation behind consistent imaging. Suyash explains SAR (microwave radar imaging), frequency bands and penetration, and how multispectral/near-IR optical data complements radar for interpretability.
- •SAR = Synthetic Aperture Radar; microwave imaging with L/C/X bands, etc.
- •Lower frequency → higher penetration (e.g., L-band through foliage)
- •Optical/multispectral uses visible + near-IR; depends on sunlight
- •Fusion of SAR + multispectral aims to make imagery both available and understandable
From idea to first satellite: Mission Drishti systems engineering and risk management
Suyash outlines how a startup moves from concept to a launch-ready spacecraft using systems engineering playbooks. He details design reviews, supply chain realities, simulation/testing, and the structured path to reduce catastrophic failure risk.
- •Mission framing: Drishti started around Dec 2021
- •NASA-style process: idea → concept → PDR → CDR → AIT → launch readiness
- •Satellite specs: ~150 kg, ~1m cube, deployable ~3.5m antenna
- •Risk strategy: mix of space-proven components and selective innovation; heavy environmental testing
Testing before the big leap: ISRO’s POEM module and constellation plans
To reduce risk further, GalaxEye uses ISRO’s POEM as an in-orbit experimentation platform before the full satellite mission. The chapter also covers the roadmap toward a multi-satellite mesh for higher revisit rates and denser data collection.
- •POEM = PSLV Orbital Experimental Module for in-orbit subsystem validation
- •Learning end-to-end launch integration by ‘touching’ the launch vehicle process
- •Using POEM results to validate assumptions or course-correct
- •Post-Drishti scaling: plan for 6–8 satellites in 4–5 years
Earth imaging evolution and India’s space arc: from V-2 photos to Cartosat and policy reform
The host and Suyash zoom out to the broader history of Earth imagery, contrasting early grainy photos with today’s sub-30cm resolution. They connect India’s strong upstream capability (ISRO) with the recent policy shift enabling private downstream innovation.
- •Milestones: 1946 V-2 image → Apollo-era iconic imagery → modern high-res commercial images
- •What “30 cm resolution” means in practical terms
- •India’s imaging capability: Cartosat examples near ~28 cm resolution
- •2020-era reforms: space/geospatial policies enabling private companies; growth to dozens of startups
GalaxEye’s execution philosophy: prove on drones/aircraft first, then go to orbit
Suyash describes a frugal, de-risked approach: miniaturize the sensor stack, fly it on aerial platforms, collect large datasets, and mature algorithms before committing to space. This becomes their bridge from lab to orbit and builds confidence in performance.
- •Avoiding ‘buggy code on production’ mentality for space hardware
- •Aerial validation: drones/aircraft/HAPS; 400+ flights to refine sensors and algorithms
- •Timeline expectation: roughly 10–12 months from the conversation to orbit
- •Mindset: engineering validation loops before irreversible deployment
Why SpaceX (and Musk) matters: vertical integration, speed, and rethinking “space-grade”
Suyash explains why Musk is inspirational in space: challenging legacy assumptions, driving costs down, and accelerating iteration. They discuss electronics grades, the fear of failure in taxpayer-funded programs, and how private players reframe acceptable risk.
- •Space historically as ‘national importance’; SpaceX shifts the model
- •Vertical integration examples: drastic cost reduction by building in-house
- •Electronics hierarchy: hobby/industrial/military/space-grade; rad-hard tradeoffs
- •Innovation via controlled risk-taking and faster iteration cycles
Hyperloop explained: frictionless transport concept and building India’s largest test tube
Returning to Suyash’s origin story, the episode demystifies Hyperloop as a vehicle in a low-pressure tube with levitation to remove key frictions. Suyash emphasizes that the core hurdle is not the physics but infrastructure and administrative execution at scale.
- •Hyperloop as a ‘fifth mode’ of transport: vehicle inside a (soft) vacuum tube
- •Removing drag (vacuum) and rolling friction (levitation) to enable high speeds
- •IITM team’s ~410–422m tube as a major testing platform; largest by size claim
- •Scaling barriers: land, clearances, and large infrastructure delivery
Ecosystem, government navigation, team-building, and investors: how deep tech gets built in India
In the closing stretch, Suyash credits IIT Madras’ network effects for cofounder formation, investor access, and ongoing support—and argues founders must actively ask for help. He also covers how GalaxEye builds teams in a talent-scarce market using ex-ISRO/DRDO advisors, and what convinces investors to fund high-risk space ventures.
- •IITM as the center of cofounder formation and cascading support networks
- •Government engagement: leadership intent vs on-ground variability; importance of the right counterparts
- •Hiring playbook changes by stage: generalists early, specialists later; India’s talent scarcity reality
- •Advisory layer: retired ISRO/DRDO experts as hands-on problem solvers and capability multipliers
- •Investor rationale: macro trends + policy tailwinds + mission clarity; “Avengers of Deep Tech” cohort

Publisher timestamps

Get more out of YouTube videos.

Read along on your phone, search it beside the video, or ask your favorite AI app what was said.