VizopsAI, Inc
Empower enterprises to turn any AI agent into a production-grade, ROI-focused system by applying research-backed, proprietary reinforcement learning optimizers.
Founding Story
Vizops was started to solve the 'prototype ceiling' where 90% of AI agent prototypes never reach production. The founders leverage their backgrounds at DeepMind and AWS to bring industrial-grade RL optimization to enterprise workflows.
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Leadership
Founders
Pushpak Pujari
CEO. Former Director of Product at Verkada, AWS IoT, and Sony. Education: The Wharton School (MBA), IIT Delhi.
Pushpendre Rastogi
CTO. Former Senior Scientist at Google DeepMind, Amazon Alexa, and Lab126. Education: PhD from Johns Hopkins University (NLP and Deep Learning), IIT Delhi.
Executive Team
Pushpak Pujari
Chief Executive Officer
Experienced AI product leader, formerly at Verkada, AWS IoT, and Sony.
Pushpendre Rastogi
Chief Technology Officer
NLP and RL expert, formerly at DeepMind and Amazon Alexa. PhD from Johns Hopkins.
Business Model
Revenue Model
Enterprise platform (SaaS/B2B) providing an optimization layer for AI agents. Model-agnostic pricing likely based on usage or enterprise license agreements.
Pricing Tiers
Includes Tune (optimizing existing agents) and Create (building new agents) paths, VPC deployment, and proprietary optimizers (MORL, GRPO).
Target Markets
- Enterprise (Retail, Financial Services, Insurance)
- Mid-market Debt Agencies
- Legal Tech
- Logistics & CPG
- E-commerce Cart Recovery
- Voice-AI Call Center Capacity
- Enterprise Tier-1 Support Automation
- Legacy Codebase Migration
- Secure Legal Discovery
- Logistics and Customs Workflow Automation
- National Specialty Retailer
- Mid-market Debt Agency
- Top-10 US Insurer
- F500 CPG Giant
History & Milestones
Achieved 18% lift in recovered cart revenue for a national specialty retailer through SMS/WhatsApp retargeting.
Increased call capacity by 300% for a mid-market debt agency using Voice AI.
Generated $2.1M annualized support savings for a high-growth CRM platform.
Completed a 4-month legacy code migration (estimated at 18 months) for a top-10 US insurer, saving $45M operationally.
Released a 14B-parameter RL-fine-tuned model achieving 96.2% retrieval accuracy, outperforming larger models like GPT-4 and Claude Opus.
