I architect autonomous AI systems that ship to production
Fractional CTO and Enterprise AI Architect with 20+ years building production systems. Ex-CTO at Spericorn, founder of B24 e Solutions. I design and ship autonomous multi-agent platforms for companies that need AI leadership, not just AI advice.
Trusted by teams at
I don't just manage projects — I design the systems that make autonomous delivery possible. Here's how I think about building AI-native platforms.
Every complex system is decomposed into specialized agents with clear responsibilities. A planner agent, a builder agent, a reviewer agent, a deployer agent. They communicate through structured protocols, not chat. Each agent has a confidence threshold — if it's not sure, it escalates instead of guessing.
Before any agent acts, it evaluates two things: how confident is it in the action, and what's the blast radius if it's wrong? Low-risk actions execute autonomously. High-risk actions require explicit approval or a second-agent review. This is how you get AI that's bold on small changes and cautious on big ones.
Not every task needs GPT-4. I route tasks to the right model: fast/cheap models for classification, mid-tier for drafting, top-tier for complex reasoning. Token budgets are set per agent. The system optimizes for output quality per dollar, not raw capability.
My systems don't just execute — they learn. Every success and failure is logged in a structured memory. The next time a similar task appears, the system already knows what worked, what didn't, and what to try differently. Not fine-tuning — runtime learning through RAG-backed experience retrieval.
Across healthcare, fintech, SaaS, and enterprise AI since 2004
Consistent 5-star ratings across 50+ engagements with repeat enterprise clients
Built and led engineering teams as CTO at Spericorn Technology
From founding B24 e Solutions in 2004 to leading AI transformation programs in 2026
Fidelity, CBRE, PNB MetLife, Godrej, Meesho, SkinIQ, and more
Remote delivery across India, United States, United Kingdom, and Australia
Reduced patient data processing time by 40%
Problem: Manual healthcare tracking across multiple facilities with fragmented data and slow reporting cycles.
Solution: Led a team of 9 to build an AI-powered SaaS platform with automated data pipelines, real-time dashboards, and predictive analytics.
Result: 40% faster patient data processing. Platform deployed across 3 regions on schedule and within budget.
Concept to MVP in 6 months with 40% velocity improvement
Problem: AI skincare startup needed to go from research prototype to production MVP under aggressive investor timelines.
Solution: Set up cross-functional teams across US and India, implemented Scrum from scratch, coordinated dermatologists, ML engineers, and designers.
Result: MVP launched on schedule. Team velocity improved 40% through process optimization. Secured next round of funding.
35% increase in delivery velocity, release cycles cut from 4 weeks to 2
Problem: 35+ engineer team with no standardized processes, slow release cycles, and unpredictable delivery across 12+ concurrent projects.
Solution: As CTO, introduced Agile across 4 product teams, set up CI/CD pipelines, code review processes, and engineering career ladders.
Result: 35% faster delivery. Release cycles halved. Engineering retention improved through clear growth paths.
8+ concurrent projects delivered with zero critical-path delays
Problem: Fortune 500 clients needed reliable technical delivery across multiple mobile and web platforms simultaneously.
Solution: Managed 8+ concurrent delivery streams using Scrum and Kanban, with cross-functional teams spanning design, development, and QA.
Result: Zero critical-path delays across all enterprise engagements. Client relationships led to multi-year retained work.
I'm a Fractional CTO and Enterprise AI Architect who has spent 20+ years building, scaling, and leading technology organizations. I co-founded a software company, served as CTO at a 35-engineer firm, and have delivered 150+ projects across healthcare, fintech, and enterprise AI. Today I embed with companies as their CTO — owning architecture, hiring, and AI strategy.
As CTO at Spericorn Technology, I built the engineering org from the ground up — hiring, architecture decisions, release processes, and technology roadmap. Before that, I co-founded B24 e Solutions and ran it for 11 years, shipping for government agencies, banks, and healthcare systems. I've led enterprise delivery for Fidelity, CBRE, and PNB MetLife.
Now I build autonomous AI systems with LangGraph, CrewAI, RAG, and MCP — production platforms that replace manual workflows and drive measurable business outcomes. I bring the rare combination of hands-on AI building and executive-level technical leadership.
I design systems where specialized AI agents collaborate autonomously — planning, executing, and self-correcting without human-in-the-loop. Each agent owns a domain: code generation, testing, deployment, compliance. They negotiate, delegate, and resolve conflicts through structured protocols.
Full software lifecycle automation: AI agents that plan features, write code, run tests, handle deployments, and manage rollbacks. Not CI/CD tooling — actual autonomous systems that understand intent, manage risk, and ship production-grade software across regulated environments.
AI systems that treat regulatory compliance as a first-class constraint, not an afterthought. Built-in guardrails for healthcare (HIPAA), finance, and multi-jurisdictional requirements. The system knows what it can and can't do before it acts.
Every AI call has a cost. I architect systems with token budgets, model routing (use GPT-4 when accuracy matters, Haiku when speed does), and intelligent caching. The goal: enterprise-grade AI output at startup-grade costs.
Conceptual representation of autonomous platform architecture
Designed and built a multi-agent platform with 40+ modules handling the full software lifecycle: planning, building, testing, shipping, and regulatory compliance across 18+ countries. Proof that one architect with modern AI tools can build what traditionally requires a team of 20.
Reduced HR onboarding time by 80% across 4 departments
Multi-agent enterprise workflows automating HR, finance, support, and sales. Autonomous AI agents collaborate to complete complex business tasks that previously required manual coordination.
Cut reasoning pipeline latency from 45s to 8s with conditional routing
Multi-agent orchestration using LangGraph for complex reasoning. Graph-based workflow with state management, conditional routing, and confidence-gated decision making.
Enabled AI agents to interact with 6+ external services autonomously
Model Context Protocol server collection providing search, code execution, browser automation, and database tools — the infrastructure layer that makes AI agents production-ready.
3x lead conversion rate with AI-driven scoring and follow-ups
AI-powered CRM automation using n8n workflows. Intelligent lead scoring, automated follow-ups, customer segmentation, and sales pipeline management — replacing manual processes with autonomous agents.
Launch AI SaaS products in weeks instead of months
Production-ready starter with Next.js 15, Vercel AI SDK, Supabase, and Stripe. Auth, billing, AI integration, and deployment — the architecture I use to ship fast for clients.
Virendra made my offshore experience run smoothly, adding to his very good technical and business knowledge, he is a great communicator, reliable and trustworthy.
VIRENDRA IS SMART. I CANNOT OVERSTATE THAT. THIS IS MY SECOND TIME WORKING WITH HIM. PLAN TO RETAIN FOR ONGOING WORK.
Virendra has provided valuable and reliable technical services to my company for multiple projects. He is always accessible and responsive with the solutions needed, both in the moment and for the long term. I highly recommend him.
Virendra did a wonderful job for us. He always solved problems with a clear mind and quick. He never had an issue working longer if needed.
Technology strategy, architecture reviews, engineering leadership, and investor due diligence. I embed with your team 10-20 hours a week as your CTO — at a fraction of a full-time hire.
Agentic AI systems with LangGraph, CrewAI, RAG, and MCP. Multi-agent workflows that run autonomously in production — not prototypes, not demos, production systems with guardrails.
Move from AI experiments to production systems that deliver measurable ROI. Readiness assessment, strategy, implementation, and change management — from pilot to scale in 90 days.
A 30-minute call to understand your goals, current architecture, and where you're stuck. No pitch, no pressure — you'll leave with at least one actionable recommendation either way.
Within one week, you get a written assessment: what to build, what to fix, what to skip, and a clear engagement proposal with scope, timeline, and deliverables.
I embed with your team 10-20 hours a week — owning architecture, unblocking engineers, and shipping production systems. Most engagements show measurable results within the first 30 days.
After managing 50+ remote projects, I've found that async standups, overlapping work windows, and documented sprint goals matter more than synchronous ceremonies. The key is building team rituals that respect time zones while maintaining accountability.
Most enterprises should start with RAG, not fine-tuning. RAG gives you control over data freshness, costs less to iterate, and avoids the risks of model drift. Fine-tuning makes sense only when you need consistent behavioral patterns, not just knowledge retrieval.
Healthcare software fails when teams build for compliance first and users second. I've delivered platforms like TrackMx and Swift-S by flipping that order: build for the clinician, then layer in compliance. Agile's iterative approach catches regulatory gaps early without sacrificing UX.
I write about AI architecture, technical leadership, and what I learn from building autonomous systems.
88% of agent pilots never reach production. The missing piece isn't better models — it's a real eval pipeline. Here's the one I build.
Read article →MCP hit 97M monthly downloads, but most enterprise deployments break in the same 3 places. Here's the production checklist I use.
Read article →After managing 50+ remote projects across US, UK, India, and Australia, here's what actually works for distributed Scrum teams.
Read article →Virendra Vaishnav is a Fractional CTO and Enterprise AI Architect with over 20 years of experience building production systems across healthcare, SaaS, AI, and enterprise platforms. He co-founded B24 e Solutions in 2004 where he delivered 150+ projects, served as CTO at Spericorn Technology leading 35+ engineers, and currently operates as a Fractional CTO with a 5.0/5.0 client rating across 50+ engagements.
Virendra specializes in Fractional CTO leadership and Enterprise AI Architecture. He provides technology strategy, engineering team setup, architecture reviews, and AI product development for startups and growth-stage companies. His technical focus is building autonomous multi-agent systems using LangGraph, CrewAI, RAG, and MCP. He also brings deep expertise in healthcare AI, compliance systems, and startup advisory.
Yes. Virendra has extensive experience managing remote distributed teams across the US, UK, Australia, and India. He has completed 50+ CTO and architecture engagements with a 5.0/5.0 client rating, leading cross-functional global teams of up to 35 engineers using Scrum, Kanban, and SAFe methodologies with tools like Jira, Confluence, Linear, and Notion.
Virendra has delivered multiple healthcare projects including TrackMx, an AI-powered Healthcare SaaS platform where he led agile delivery managing a team of 9, and Swift-S, a Healthcare Platform where he oversaw delivery with a team of 12. He also managed delivery of an AI-powered skincare diagnostics platform at SkinIQ Inc.
Yes. Virendra actively builds AI agent systems using CrewAI for multi-agent enterprise workflows and LangGraph for complex reasoning pipelines. His open-source projects include CrewAI Enterprise Agents for automated HR, finance, and sales processes, and a LangGraph Multi-Agent Pipeline for graph-based workflow orchestration with conditional routing.
Virendra builds AI products using LangChain, LangGraph, CrewAI, RAG, MCP, OpenAI, Anthropic Claude, Google Gemini, LLaMA, Mistral, Vercel AI SDK, TensorFlow, PyTorch, n8n, Vector Databases, and Embeddings. For full-stack development, he uses Next.js, React, Python, FastAPI, Node.js, and TypeScript.
Virendra offers three core services: (1) Fractional CTO — technology strategy, architecture reviews, engineering team setup, and investor due diligence; (2) AI Product Development — building production agentic AI systems with LangGraph, CrewAI, RAG, and MCP; and (3) AI Transformation — taking enterprises from AI experiments to production systems that deliver measurable ROI.
Virendra follows a proven 5-step delivery methodology: (1) Discovery & Requirements with stakeholder interviews and roadmap creation; (2) Architecture & Sprint Planning with technology selection and backlog creation; (3) Agile Execution using 2-week sprints with daily standups and CI/CD; (4) Quality & Risk Management with automated testing and security audits; (5) Delivery & Iteration with staged rollouts and retrospectives.
Virendra Vaishnav is based in Jaipur, Rajasthan, India. He works remotely with clients across the United States, United Kingdom, Australia, and India. His experience managing distributed teams across multiple time zones makes him effective for global remote engagements.
The fastest way is to book a free 30-minute strategy call at calendly.com/virendra-aixpertz/30min. You can also email virendra@aixpertz.ai or connect on LinkedIn. Virendra is available for Fractional CTO engagements, AI architecture consulting, and project-based work. He typically responds within 24 hours.
I embed with your team as a Fractional CTO — architecting AI systems, leading engineering, and shipping production code. Not slide decks. Available for remote engagements worldwide.