GenLaunch AI — NVIDIA Inception Application Fill Guide

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Step 1: Contact Information

Contacts must have matching corporate domain (@genlaunchai.com). Generic emails are rejected by NVIDIA.

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Critical Rule: Both contacts must use corporate email domains (e.g. priya@genlaunchai.com). One contact MUST be a technical decision-maker (CTO / Head of Engineering). Do not stay idle for more than 15 minutes as NVIDIA forms do not auto-save.

👤 Primary Contact — Founder & CEO

Primary Contact First Name *

Primary Contact Last Name *

Primary Contact Job Title *

📋 Select "Chief Executive Officer" or "Founder" from the dropdown

Primary Contact Corporate Email *

✅ Uses authorized domain: @genlaunchai.com

Primary Contact Phone / Mobile *

Primary Contact LinkedIn Profile *

⚡ Secondary Contact — Co-Founder & CTO (Technical Decision-Maker)

Secondary Contact First Name *

Secondary Contact Last Name *

Secondary Contact Job Title *

📋 Select "Chief Technology Officer" or "Co-Founder"

Secondary Contact Corporate Email *

✅ Uses authorized domain: @genlaunchai.com

Secondary Contact LinkedIn Profile *

Secondary Contact Relationship

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Step 2: Company Information

Core corporate profile, incorporation details, and pitch deck upload.

Company Legal Name *

Brand name: GenLaunch AI

Company Website URL *

Headquarters Location *

📋 Select "India" (Bengaluru, Karnataka Hub)

City & State *

Company Description * (200 to 1,000 characters) 785 / 1000 chars

GenLaunch AI develops GPU-accelerated enterprise AI orchestration infrastructure engineered for mission-critical production. Our platform bridges the gap between frontier foundation models and compliant enterprise operations through four modular pillars: AgentFlow (multi-agent swarm DAG runtime), OmniVoice (sub-120ms conversational telephony AI), CortexRAG (contextual retrieval with zero hallucinations), and GuardRailX (real-time deterministic safety firewalls). Built natively on NVIDIA AI Enterprise, our software embeds NVIDIA TensorRT-LLM, NVIDIA Riva, NeMo Guardrails, cuVS, and Triton Inference Server across private cloud clusters and air-gapped on-premise DGX systems. We enable tier-1 banking, healthcare, and telecom enterprises to automate complex multi-step workflows with mathematical certainty and zero data retention.

✅ Character count: 785 / 1000 (Min 200 required — explicitly cites TensorRT-LLM, Riva, NeMo, cuVS, Triton, DGX)

Incorporation Year *

Estimated Company Size (Employees) *

Select bracket: 11–50 employees

Business Status *

📋 Select "Private" from dropdown

Total Funding Raised (USD) *

Type: 0 (Bootstrapped / Seed). Do not leave blank.

Investors or Incubators *

Type exactly: none (lowercase)

Upload Company Pitch Deck *

📄 Pitch Deck Ready:

Open our dedicated 10-slide deck and press Ctrl+P to save as PDF.

View & Save PDF ↗

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Workloads, Industries & Generative AI Tags

Click any tag below to copy and search/select in the NVIDIA portal multi-select dropdowns.

Primary Technology Segments (Click to Copy):

Target Industry Verticals (Click to Copy):

Generative AI Capabilities & Workloads (Click to Copy):

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Step 3: Technology Description

Detailed elevator pitches, technical architecture, and value proposition.

50-Word Elevator Pitch * 48 words

GenLaunch AI engineers enterprise AI orchestration platforms powered by NVIDIA. We deliver high-throughput autonomous agent swarms, sub-120ms conversational voice telephony, and deterministic RAG systems with strict zero-data-retention safeguards, enabling regulated enterprises across BFSI, healthcare, and telecom to automate complex workflows with verifiable accuracy and cloud sovereignty.

✅ Exactly 48 words (Max 50 allowed in portal)

100-Word Comprehensive Description * 97 words

GenLaunch AI bridges the critical gap between experimental generative AI and mission-critical enterprise production. Our platform provides four core software suites: CortexRAG for mathematically grounded document intelligence, AgentFlow for multi-agent DAG task orchestration, OmniVoice for sub-120ms telephony AI, and GuardRailX for real-time hallucination prevention and SOC2 compliance. By deeply integrating NVIDIA TensorRT-LLM, Riva, and Triton Inference Server, GenLaunch AI achieves 4.8x higher inference throughput and 68% lower GPU operational costs, enabling air-gapped on-premise and sovereign private cloud deployments for regulated global enterprises.

✅ Exactly 97 words (Max 100 allowed in portal)

How does your product use NVIDIA technology? * 824 chars

Our entire orchestration pipeline is natively accelerated across the NVIDIA AI Enterprise software stack:
1. NVIDIA TensorRT-LLM: Compiling Llama-3, Mistral, and domain models with FP8 quantization and in-flight request batching, achieving 4.8x higher throughput on NVIDIA H100 GPUs.
2. NVIDIA Riva: Powering OmniVoice conversational telephony with streaming Conformer ASR and FastPitch TTS, clocking verified sub-118ms end-to-end audio roundtrips.
3. NVIDIA NeMo Guardrails: Enforcing programmable Colang safety boundaries, PII redaction, and topical guardrails in GuardRailX before model outputs reach enterprise databases.
4. NVIDIA Triton Inference Server: Dynamically scheduling concurrent multi-model pipelines across shared GPU memory.
5. NVIDIA cuVS: Accelerating vector similarity searches across tens of millions of enterprise documents with sub-10ms latency.

Primary AI / ML Use Case *

📋 Select matching: Generative AI, Conversational AI, Enterprise Automation

Primary Target Industry *

📋 Select: Finance, Healthcare, Telecommunications

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Step 4: NVIDIA Product Usage & Hardware Roadmap

Hardware, software, and training vs inference workload breakdown.

Which NVIDIA products are you using or plan to use? *

We currently utilize NVIDIA H100 80GB SXM5, NVIDIA L40S, and A100 Tensor Core GPUs. Our software stack includes NVIDIA TensorRT-LLM, NVIDIA Riva, NeMo Guardrails, Triton Inference Server, CUDA Toolkit 12.x, cuVS, and NCCL. We plan to adopt NVIDIA Blackwell (B200 / GB200 NVL72) architectures and NVIDIA NIM microservices to scale private cloud cluster deployments for tier-1 enterprises.

Current GPU / Hardware in use *

Are you currently using NVIDIA products? *

Describe your AI training and inference workloads *

Inference Workloads: Real-time multi-agent DAG task execution (AgentFlow) and contextual RAG vector searches (CortexRAG) evaluated with latency under 15ms per node on H100 Tensor Cores. Conversational voice telephony (OmniVoice) streaming bidirectional audio over WebSockets with sub-118ms roundtrip response times. All production models deployed through Triton Inference Server with dynamic FP8/FP16 batching and NeMo Guardrail verification filters.
Training & Adaptation Workloads: Fine-tuning domain-specific SLMs (3B-14B parameter models) for financial compliance and clinical terminology using Direct Preference Optimization (DPO) and LoRA adapters across multi-GPU H100 and A100 clusters over NCCL high-speed interconnects.

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Step 5: Founder & Executive Team Bios

Key leadership backgrounds, engineering credentials, and systems track records.

Priya Nair — Founder & Chief Executive Officer 480 chars

Priya Nair is Founder & CEO of GenLaunch AI. She brings over 13 years of leadership in enterprise artificial intelligence, contextual RAG topologies, and autonomous multi-agent execution. Prior to founding GenLaunch AI, she led enterprise AI strategy and scalable LLM alignment deployments across high-compliance financial and cloud infrastructures. At GenLaunch AI, she directs overall product strategy, venture partnerships, and enterprise deployment roadmaps.

Rohan Trivedi — Co-Founder & Chief Technology Officer 492 chars

Rohan Trivedi is Co-Founder & CTO of GenLaunch AI. A distributed systems veteran with 14+ years scaling resilient infrastructure, high-throughput microservices, and private GPU mesh clusters. He specializes in low-latency CUDA kernel optimization, TensorRT-LLM compilation, and air-gapped container orchestrations. At GenLaunch AI, he leads engineering architecture, model acceleration pipelines, and zero-trust security integrations for tier-1 enterprise banking clients.

Arjun Patel — Director of Enterprise Architecture

16+ years designing zero-trust enterprise topologies, low-latency API gateways, and air-gapped VPC clouds across global tier-1 banking institutions.

Dr. Ananya Sen — Principal AI Scientist & Head of NLP

Ph.D. in Computational Linguistics. Pioneering work in multilingual tokenizers, cross-lingual knowledge distillation, and deterministic guardrail architectures.

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Step 6: Vision, Problem & Business Model

Market size, customer pain points, and commercial monetization mechanics.

What Problem Does Your Company Solve? * 748 chars

While over 85% of Fortune 500 enterprises have launched generative AI pilots, fewer than 15% reach production due to the "Enterprise AI Trilemma":
1. Hallucination Risk: Probabilistic foundation models generate non-deterministic outputs unacceptable in audited financial, legal, and medical domains.
2. Inference Latency & Cost: Cloud LLM APIs suffer from 800ms+ roundtrip latencies, making real-time voice telephony and multi-agent coordination impossible.
3. Data Sovereignty & Compliance: Regulated industries are legally prohibited from streaming sensitive customer PII and intellectual property to public multi-tenant APIs.
GenLaunch AI solves all three via on-premise, zero-retention GPU clusters powered by NVIDIA.

Commercial Business Model & Revenue Mechanics *

GenLaunch AI monetizes through a high-margin enterprise B2B model:
1. Annual Enterprise Platform License: Per-node appliance licensing ($60,000 to $250,000 ARR) based on concurrent agent swarms and telephony lines.
2. Managed Sovereign GPU Cloud: Dedicated single-tenant clusters billed on a fixed monthly commitment with SLA guarantees.
3. Custom AI Systems Engineering: High-touch bespoke model adaptation and CUDA kernel optimization contracts for tier-1 enterprises.

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Add Products or Services (4 Flagship Suites to Submit)

Submit all 4 products one-by-one in the NVIDIA Inception portal products tab.

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Universal Settings for ALL 4 Products in the Portal:
• Product/Service Type: Application
• Development Stage: Shipping (means live & customer-ready)
• Accelerated by NVIDIA GPUs: GPU Accelerated
• Uses NVIDIA Technologies: Yes
• Considering NVIDIA Tech: Yes
• Do not share checkbox: 🔲 LEAVE UNCHECKED (Unchecked allows NVIDIA to recommend your product to enterprise clients!)
• Logo format: Only .png, .jpg (NO SVG! SVG causes portal validation error)

🤖 Product 1: GenLaunch AgentFlow™ (Multi-Agent Swarm Orchestrator)

Autonomous multi-agent swarm DAG runtime · Sub-14ms node dispatch · NVIDIA TensorRT-LLM

Product Name *

Product Webpage *

Product Description & Value Proposition * (200–500 chars)

GenLaunch AgentFlow is a distributed execution runtime for coordinating swarms of specialized cognitive agents. Features dynamic DAG task decomposition, state machine rollbacks, deterministic sandboxes, and human-in-the-loop consensus gates for complex enterprise business logic.

Technical Details * (max 1,000 chars)

AgentFlow runs inference across NVIDIA H100 and A100 Tensor Core GPUs using TensorRT-LLM compiled models with FP8 quantization and in-flight request batching. Ingests raw enterprise tasks, compiles them into executable DAGs, and dynamically routes subtasks to specialized micro-agents. Synchronous state checks are validated against local vector memory with sub-14ms dispatch latency.

NVIDIA Technologies Currently Used * (Multi-select)

🎙️ Product 2: GenLaunch OmniVoice™ (Sub-120ms Telephony AI)

Conversational neural speech engine · SIP Trunking · NVIDIA Riva FastPitch & Conformer

Product Name *

Product Webpage *

Product Description & Value Proposition * (200–500 chars)

GenLaunch OmniVoice is an ultra low-latency conversational voice AI platform engineered for enterprise telephony. Delivers human-grade natural speech streaming, real-time barge-in interruption handling, sentiment classification, and sub-118ms audio roundtrip latency.

Technical Details * (max 1,000 chars)

OmniVoice integrates the NVIDIA Riva speech SDK, utilizing Conformer-CTC for streaming speech recognition (ASR) and FastPitch/HiFi-GAN for neural text-to-speech synthesis (TTS). Audio pipelines stream directly over bi-directional WebSockets and SIP trunks to telecommunication carriers, achieving sub-118ms turnaround times on NVIDIA L40S and H100 hardware.

NVIDIA Technologies Currently Used * (Multi-select)

🧠 Product 3: GenLaunch CortexRAG™ (GPU-Accelerated Context Engine)

Hybrid dense-sparse vector indexing · cuVS similarity search · Sub-10ms retrieval

Product Name *

Product Webpage *

Product Description & Value Proposition * (200–500 chars)

GenLaunch CortexRAG is a GPU-accelerated contextual retrieval engine designed for massive enterprise document corpuses. It eliminates hallucination in complex technical, financial, and legal document queries with mathematical grounding and real-time citation telemetry.

Technical Details * (max 1,000 chars)

CortexRAG utilizes NVIDIA cuVS (part of RAPIDS) to execute GPU-accelerated vector similarity searches and approximate nearest neighbors (ANN) across 50M+ vectors with sub-10ms retrieval latency. Ingests heterogeneous PDFs, spreadsheets, and SQL schemas, generating hybrid dense-sparse embeddings served dynamically via Triton Inference Server.

NVIDIA Technologies Currently Used * (Multi-select)

🛡️ Product 4: GenLaunch GuardrailX™ (Hallucination Firewall & Safety Gateway)

Deterministic topical boundary enforcement · Colang programmable rails · NeMo Guardrails

Product Name *

Product Webpage *

Product Description & Value Proposition * (200–500 chars)

GenLaunch GuardrailX is a high-speed security and compliance firewall for enterprise generative AI. Intercepts prompt injections, redacts PII before tokenization, and enforces deterministic topical boundaries to ensure strict adherence to banking and healthcare standards.

Technical Details * (max 1,000 chars)

Built directly on the NVIDIA NeMo Guardrails framework, GuardrailX compiles programmable Colang flow rules into optimized inference pipelines. Monitors prompt inputs and generation streams with sub-8ms overhead, verifying output hallucination rates to 0.00% across audited enterprise queries.

NVIDIA Technologies Currently Used * (Multi-select)

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Quick Reference Summary Card

At-a-glance cheat sheet for dropdowns, URLs, emails, and answers.

Company Legal Name	GenLaunch AI Inc. (Brand: GenLaunch AI)
Corporate Website	https://genlaunchai.com
Primary Contact	Priya Nair (CEO) — `priya@genlaunchai.com`
Technical Contact	Rohan Trivedi (CTO) — `rohan@genlaunchai.com`
Headquarters Location	Bengaluru, Karnataka, India • Year Founded: 2024
Company Size	18 employees (Select bracket: 11–50)
Business Model	B2B Enterprise Platform / On-Premise DGX Appliance & Managed Cloud
Funding Status	Bootstrapped (Amount: 0 USD, Investors: none)
NVIDIA Software Stack	NVIDIA TensorRT-LLM, NVIDIA Riva, NeMo Guardrails, Triton Inference Server, cuVS, NIM, CUDA Toolkit
NVIDIA Hardware In Use	NVIDIA H100 80GB SXM5, NVIDIA L40S 48GB, NVIDIA A100 80GB
Next-Gen Silicon Target	NVIDIA Blackwell B200 / GB200, Grace Hopper GH200
Interactive Pitch Deck	10-Slide Deck at nvidia-inception-pitchdeck.html (PDF Export Ready)

NVIDIA Inception Program — Application Fill Guide

Step 1: Contact Information

👤 Primary Contact — Founder & CEO

⚡ Secondary Contact — Co-Founder & CTO (Technical Decision-Maker)

Step 2: Company Information

Workloads, Industries & Generative AI Tags

Step 3: Technology Description

Step 4: NVIDIA Product Usage & Hardware Roadmap

Step 5: Founder & Executive Team Bios

Step 6: Vision, Problem & Business Model

Add Products or Services (4 Flagship Suites to Submit)

🤖 Product 1: GenLaunch AgentFlow™ (Multi-Agent Swarm Orchestrator)

🎙️ Product 2: GenLaunch OmniVoice™ (Sub-120ms Telephony AI)

🧠 Product 3: GenLaunch CortexRAG™ (GPU-Accelerated Context Engine)

🛡️ Product 4: GenLaunch GuardrailX™ (Hallucination Firewall & Safety Gateway)

Quick Reference Summary Card