Nasscom Generative AI Foundry | Cohort - 3

Name

First NameLast Name
Designation
Typically, a founder/co-founder tends to fill this form
Email (Work)
Provide your business email address. Note: This will become the registered email address for all future communications.
Phone Number
Please enter a valid phone number.Format: (000) 000-0000.

Start-up's Legal Name
Start-up's Brand/Product Name
Date of Start-up Incorporation

DayMonthYear
2 digit day, 2 digit month, 4 digit year
Registered Address

Street Address

Street Address Line 2

CityState

Zip CodeCountry
Company Website
Company's LinkedIn URL
Company's Twitter Handle
Co-Founder Names
Type N.A. if you're the solo founder
LinkedIn URL (All founders)
About the Founding Team
Format: Name, Qualification, Experience, Domain, etc
Number of Full-Time Employees (other than the founding team)
Please ignore part-time/contract staff and interns
Team Composition: Tech vs Non-Tech
Number of Domain Experts, PhDs, etc

Your start-up's GenAI solution
Nature of your GenAI solution
Describe the GenAI Techniques being used
Do you have a plan to open source your model? If so, how and by when?
Your answer can also be No
Regarding your generative AI model, what is its approximate parameter count, and what are the typical monthly GPU-hours required for its operation? Can you elaborate on the average inference cost per 1,000 outputs?
What is the single most significant breakthrough or differentiating innovation that defines your generative AI model's unique capabilities (in 100 characters or less)? What are the main current constraints or limitations you are actively working to address?
How are safety and guardrails implemented in your generative AI model, specifically addressing the alignment techniques used, the structure of your content-filter stack, and the typical cadence and methodology of your red-teaming efforts?
Nature of data that your start-up is using/has used to train the model
Help us understand your data sources better

Please explain the Primary Application & Environment – core function, operating setting (industrial, outdoor, healthcare, etc.) and constraints (hazardous, underwater, space).
Explain the Autonomy & Physical Tasks – supervision level (full / supervised / tele-op / human-in-loop) and key tasks (grasping, navigation, manipulation, assembly, sensing).
Please provide the Testing Maturity & Compliance – real-world test-hours, current MRL (1-10), target safety certs (ISO 10218, IEC 61508, other)
Please suggest the Core Autonomy Stack – principal AI algorithms for perception / planning / control.

Elaborate on the specific challenges your AI solution addresses within the defense sector, and what is the intended operational environment – be it the battlefield, command and control, cybersecurity, or logistics – where it's designed to make an impact?
Elaborate on your solution's autonomy and deployment profile, specifically detailing the level of decision authority it possesses, its typical deployment settings, and any significant Size, Weight, and Power (SWaP) constraints that influence its design or application?
Could you elaborate on your solution's current Technology Readiness Level (TRL) and its implications for deployment, and what's your ITAR/export control status or any other relevant compliance considerations for expanding into different markets?
Given the mission-critical nature of your defense AI solutions, how do you approach AI assurance? Could you elaborate on your chosen methods—such as explainability, formal verification, or sandboxing—and the specific reliability provisions you've implemented to ensure consistent, trustworthy performance in high-stakes operational environments?

How do you envision the interplay between the space segment—encompassing areas like Earth Observation, satellite operations, and exploration—and the core AI functions, both on-board and on the ground, within your SpaceTech solutions?
What's your current technology readiness level (TRL), and how does that inform your projected development timeline, including any need for radiation hardening and your desired launch window?
Ground-Segment & Data Needs – estimated downlink/uplink bandwidth (Mbps) and ground-station partners.
Mission Impact – specific efficiency, safety, or capability gains delivered.

Beyond just detection, how do your AI-driven cybersecurity solutions specifically target emerging threat domains and attack vectors, and what unique combination of AI methods—like anomaly detection, behavioral analytics, threat intelligence correlation, or predictive modeling—do you leverage to proactively defend against and respond to these evolving threats?
Beyond just identifying types of threats like malware, phishing, or ransomware, how does your solution specifically integrate with and cover various stages of the cyber kill chain or MITRE ATT&CK framework to provide comprehensive protection?
Could you elaborate on the core performance metrics of your cybersecurity solution, specifically in terms of detection latency and false-positive rates? What key feeds or benchmarks have you integrated to validate and continually improve these performance claims?
Beyond standard security protocols, how do you continuously harden and update your AI models to proactively defend against evolving adversarial attacks? Could you elaborate on your retraining and update frequency, the specific adversarial-defense measures you've implemented, and the latest insights gained from your purple and red-team exercises?

Problem Domain & Use-Case – brief description of the industry problem solved and primary application scenario.
Core AI Approach & Data – principal algorithms / models used and key data sources (multi-select + short text).
Maturity & Performance – TRL (1-9) or live-user / revenue metric, plus headline performance benchmark.
Compliance & Impact Considerations – relevant regulatory / ethical requirements met and anticipated impact over the next 12 months.

Gross Annual Revenue / Annual Recurring Revenue