AI strategy and roadmap
Which problems are worth a language model, which are not, and what the winners cost to run. A scoped plan with evals before any code.
Generative AI consultant / ex-IBM / ex-Director of Engineering, YC W20
Latest essay No. 85 / 10 Nov 2026Production AI that survives contact with real users.
I help founders and engineering leaders take LLM features from demo to dependable: agents, retrieval, integrations, and the evals and guardrails that keep them honest in front of customers.
30 minutes, booked and paid through Razorpay. Not ready for a call? Email hire@viralruparel.com, reply usually within 24 hours.
8+
years shipping software
20+
engineers led as Director of Engineering
85
essays on agent engineering
24h
typical reply to a new enquiry
Built with teams backed by
- Y Combinator
- Andreessen Horowitz
- Bain Capital Ventures
- FirstMark
- BoxGroup
- Hawke Ventures
- Abstract Ventures
- Shopify Ventures
- Blockchain Capital
01
What I do
Generative AI consulting for production LLM and agent systems. Six things, done end to end, and every engagement starts with an eval set so we both know what better means.
How an engagement startsLLM integration
Model calls wired into your product with structured outputs, fallbacks across providers, caching, and cost controls that hold at scale.
RAG systems
Retrieval that answers from your own documents: chunking, hybrid search, citations, and a regression set that catches drift.
Agents and automation
Tool-using agents with authorization gates, idempotent side effects, clean retries, and an audit trail you can defend.
Full-stack product engineering
TypeScript, React, Node and Postgres. The product around the model is what makes an AI feature usable.
Architecture and technical leadership
Fractional leadership: system design, hiring, and the engineering habits that let a small team ship every week.
02
Selected work
What it was, what was broken, what changed. Ask for the long version of any of these on the call.
LLM file-to-JSON standardiser
GPT-4ClaudeLangChainOCRTypeScript
HR teams received payroll and employee data as CSV, Excel and PDF from dozens of sources and spent hours standardising it by hand. The platform accepts any format and returns structured JSON.
Outcome
85%
less processing time, and manual formatting errors gone across several HR systems.
Workflow automation platform
MERNAWSMicroservicesTeam lead
Businesses needed deep integrations between their SaaS tools and had nobody to build them. Joined as a founding engineer, left as Director of Engineering.
Outcome
99.9%
uptime serving thousands of customers, with the team grown from 2 to more than 20 engineers.
Watson-powered SAP support chatbot
IBM WatsonSAPRPALive chat
SAP support queries were burying the IT team and responses were slow. An enterprise chatbot wired into SAP, RPA and live chat took the first pass.
Outcome
70%
fewer support tickets, with 85% of queries resolved correctly by the bot.
Token compensation engine
Smart contractsNode.jsReactPostgreSQL
Blockchain companies were still paying people through opaque, manual, error-prone spreadsheets. Built automated token grants and payroll on smart contracts with AI-driven performance metrics.
Outcome
Transparent token distribution for fintech clients, auditable by the people being paid.
Solar PV and battery control platform
IoTMQTTRabbitMQInfluxDBTimescaleDB
Distributed solar and battery storage installations were monitored by hand and could not optimise energy use in real time. One platform now manages them all.
Outcome
Automated energy management across multiple installations in Europe.
03
Writing
Your Coding Agent Installs MCP Servers From Its Memory
You ask your agent to set up an MCP server, it picks a package name from its training data, and the name points at a reference server that was archived over a year ago with an open remote-code-execution hole. No dependency scanner sees it, because the server never lands in a manifest, and there is no CVE to match.Your Agent Remembers a Lie an Attacker Planted Last Week
A support agent started quoting a refund rule nobody wrote. The model was fine, the prompt was fine, but weeks earlier it had read an email, decided the rule was worth remembering, and wrote it to long-term memory with no record of where it came from.Your Agent Stalls When One MCP Tool Server Slows Down
A single flaky MCP tool server can hang your whole agent. The model is fine, the provider is fine, but one third-party tool starts answering in forty seconds instead of one, and every turn that touches it stalls for the full client timeout, three times over once retries kick in.Your MCP Tool List Reshuffles and Kills the Prompt Cache
An agent backed by MCP servers resends its whole tool list on every step, and that list sits at the very front of the cacheable prefix. If the server returns the tools in a different order, or the client refetches and gets a slightly different set, the prefix stops matching and you pay full price and full prefill latency for the rest of the request.Guides
All guides04
About
Enterprise discipline from IBM. Startup speed from a YC founding team. Eight years of putting both into systems that ship.
I am a Generative AI consultant and senior software engineer. I have spent more than eight years delivering full-stack products, designing the architecture under them, and lately wiring language models into platforms that have to work at scale.
At a YC W20 startup I joined as a founding engineer and left as Director of Engineering, leading a 20-person team through platform architecture, product delivery, and the unglamorous engineering practices that keep a product up.
Earlier, at IBM, I built Watson-based support automation that resolved 85% of queries and cut ticket volume by 70%. Since then I have led token-based compensation infrastructure at a blockchain fintech and energy control platforms for IoT fleets in Europe.
Today I help companies implement production-grade AI that creates business value, and I write about what breaks when agents meet reality.
- Now
- Independent consultant
- Before
- IBM, YC W20 startup, Web3 fintech
Stack
- Generative AI and LLMs
- OpenAI GPT-4, Anthropic Claude, LangChain, AI agents, prompt engineering, RAG systems
- AI and ML platforms
- IBM Watson, Hugging Face, TensorFlow, PyTorch, MLflow, vector databases
- Frontend
- React, Next.js, Tailwind CSS, Angular, Vue.js, Chart.js, Ant Design, shadcn/ui, MUI
- Backend and full-stack
- Node.js, Express, Prisma, T3 stack, REST APIs, Swift, MERN
- Databases and storage
- PostgreSQL, MongoDB, TimescaleDB, InfluxDB, vector DBs, database design
- Cloud and DevOps
- AWS, DigitalOcean, Kubernetes, Docker, Git, CI/CD, serverless
- IoT and real-time
- MQTT, RabbitMQ, IoT system design, real-time data, edge computing, edge AI
- Mobile and Web3
- React Native, iOS, Web3, smart contracts, blockchain, dApps
05
How it starts
A call, a scoped plan with an eval set attached, then a build measured against that set.
A 30-minute call
Tell me what you are building and where it is stuck. I will say plainly whether I can help, and what it would take. If I am not the right person, I will say that too.
A scoped plan with an eval set
Which problems are worth a language model, which are not, and what the winners cost to run. The eval set is written before any code, so we both know what better means.
Built against that eval set
Shipped in slices and measured against the same set, so better is a number we agreed on, not an opinion.
- Project
- Scoped delivery, from an MVP to an enterprise system. Scoped on the call.
- Retainer
- Ongoing architecture and technical leadership for a team that already ships. Scoped on the call.
- Reply
- Usually within 24 hours, from hire@viralruparel.com.
06
Start with a 30-minute call.
Tell me what you are building and where it is stuck. I will say plainly whether I can help, and what it would take.
- Response time
- Usually within 24 hours
- Engagements
- Projects and retainers
Ready to talkBook a 30-minute call
30 minutes, booked and paid through Razorpay. A receipt and a reply within 24 hours follow by email.
Not sure yet? Write first
Free, no commitment. Tell me what you are building and I will reply with whether I can help and what the next step would be.