I'm Suhas Bhairav. I design reliable agentic systems, knowledge infrastructure, and production AI workflows for complex products.
AI starter templates
A growing collection of Next.js AI templates for chatbots, RAG, agents, voice, image generation, copilots, analysis, and developer tools, shaped around production guardrails, prompt-injection hardening, setup notes, environment guidance, comparison paths, and a one-click deploy route from GitHub.
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Open-source starters
12
Template families
1-click
Deploy path
Realtime speech
A full-stack JavaScript starter for building realtime browser voice agents with Next.js App Router, OpenAI Agents SDK, Realtime WebRTC sessions, ephemeral client secrets, live audio transcripts, multi-agent handoffs, and server-side API key handling.
Agent handoff
A full-stack JavaScript starter for building a multi-agent ChatGPT-style application with Next.js App Router, OpenRouter SDK streaming, nvidia/nemotron-3-super-120b-a12b:free, reasoning-enabled responses, mobile responsive chat UI, and server-side API key handling.
Streaming chat
A full-stack JavaScript starter for building a ChatGPT-style application with Next.js App Router, OpenAI Responses API streaming, a model selector, mobile responsive chat UI, and server-side API key handling.
The Bhairav Show
Julian Schwarzkopf
IT GRC · DORA
Tim Kreling
AI Recruiting · AI Voice Agents
Suhas Bhairav
AI Agent Safety · Enterprise AI Governance
Selected systems
Practical prototypes for real revenue, operations, and knowledge workflows.
0110 guided workflowsA guided AI workspace that turns scattered sales knowledge into account briefs, proposals, meeting prep, and CRM-ready actions.
Explore system
02Hours → minutesA source-grounded copilot that helps teams find answers across RFPs, proposals, and sales collateral in minutes.
Explore system
03Human-in-the-loopA multi-agent workflow for triage, root-cause summaries, SLA risk, and governed customer-response drafting.
Explore systemFree resources
Download practical AI PDFs, workflow scorecards, guardrail guides, and department playbooks.
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PDF downloads
114
Resource categories
A practical, beginner-friendly guide for marketing teams to implement AI-driven workflows. It covers content ops, campaign planning, research, reporting, approvals, and safe adoption.
A practical scorecard for leaders who want to know if their process, data, team, tools, risk controls, and ROI case are ready for AI workflows. Use it before investing in automation or agents.
A practical guide for setting allowed actions, restricted actions, approval steps, escalation rules, tool access, and audit expectations. Use it before giving AI agents real business authority.
What I build
I work where AI meets real organizational complexity—legacy systems, sensitive data, human decisions, and the need to prove that something actually works.
Designing observable workflows where models, tools, data, and human judgment work together safely.
Building retrieval and graph layers that make enterprise knowledge useful, traceable, and maintainable.
Taking AI beyond the demo with evaluation, failure-mode thinking, security, and production discipline.
Completely open source
An independent MIT-licensed project to upload department metrics, generate current-data JSON, and turn finance, sales, product, marketing, HR, and operations into CEO-ready recommendations.
Operating principles
The most valuable systems are understandable, governable, and quietly useful in ordinary work.
Start with a real decision or workflow. Earn autonomy through evidence.
Expose sources, reasoning boundaries, approvals, and failure paths.
The model is one component. Data, interfaces, evaluation, and operations make it work.
Research foundation
My research in IoT security, static analysis, fuzzing, and graph complexity still shapes how I build AI systems today.
ACM SAC
IEEE TrustCom
IEEE Transactions on Reliability
IARIA