Netra Systems, Inc.
Netra Systems optimizes AI agents to reduce cost and latency while improving reliability and security through automated log analysis and a managed agent kernel.
At a Glance
- Individual Developers
- Early-stage Startups
- Production AI Teams
- Enterprise FinOps
AI Tools by Netra Systems, Inc.
(1)fak — the Fused Agent Kernel
Open Source AI Agent Kernel
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Products & Services
Free developer tool for local log analysis and Claude/Gemini CLI usage optimization.
Commercial backend optimization engine for business-scale AI agent deployments.
Open-source (Apache 2.0) in-process kernel for managed agents, featuring addressable KV cache and security adjudication.
Trust substrate for verifying AI agent claims against git ancestry and real artifacts.
Market Position
Positions as a 'kernel layer' for agents, offering deeper control (addressable KV cache) and better performance (9.7x prefill elimination) than standard orchestration frameworks or model-native caching.
Leadership
Founders
Anthony Chaudhary
Technical Founder, previously at Diffgram (Lead) and healthcare systems. Lead developer of fak and dos-kernel.
Executive Team
Anthony Chaudhary
Technical Founder
Lead engineer for fak and DOS; former technical lead at Diffgram.
Rindhuja Treesa Johnson
AI & Data Scientist
Engineered Netra Zen and Netra Apex optimization engines.
Founding Story
Started as a micro-startup effort to resolve the frustration of slow and expensive AI agent runs. The founders built Zen to optimize Claude usage and expanded into a full managed agent infrastructure with Apex and FAK.
Business Model
Revenue Model
Subscription-based (SaaS) and usage-based fee (2.9% of managed spend for Premium/Enterprise).
Pricing Tiers
Basic Log Analysis, 1 User, Community Support.
Deeper Analysis, Up to 5 Users, Email & Chat Support.
Deepest Analysis, Unlimited Users, API/SDK Access, Basic Forecasting.
Advanced FinOps, SAML/SSO, SOC 2 Support, Dedicated Account Manager.
Target Markets
- Individual Developers
- Early-stage Startups
- Production AI Teams
- Enterprise FinOps
- Reducing LLM token costs
- Minimizing agent latency
- Enforcing safety policies on tool-using agents
- Debugging agent failure points