I architect agentic
systems & the infra
that survives production.

AI writing tools generate fluent but off-voice content, and lean on models that don't reliably return valid structured output — so the backend either bloats with brittle glue or breaks the moment you swap the LLM. The harder problems are staying on-voice, keeping JSON trustworthy, and staying portable across providers.

A 6-agent Mastra pipeline (Persona Analyst → Onboarding → Trend Research → Content Generator, plus a live Persona Chat and an AI co-writer) turns a user's own posts into 5–7 personalized briefs. A deterministic Writing-DNA engine extracts 15+ voice metrics at zero LLM cost. The whole app is provider-agnostic — every agent resolves its model through one factory, switchable between Google Gemini and Ollama Cloud by an env var — and structured output is enforced in four cheap-first layers (native JSON mode → prompt rule → local repair/normalize → at most one repair call) so no call depends on schema-constrained decoding. Trend intelligence fans out across Tavily, Hacker News, 60+ domain RSS feeds, and Google News with cross-source dedup and a two-tier cache. Feedback — explicit ratings plus implicit behavior — feeds a recency-decayed learning loop, weighting reported post performance highest. Turborepo monorepo: 3 packages, 11 REST modules with Swagger, 13 MongoDB collections; a Next.js 14 app with real-time chat, an AI co-writing editor, and a 7-signal AI detector plus humanizer.

• Provider-agnostic LLM layer — swap Gemini ↔ Ollama Cloud with one env var, no code change
• Never hand the SDK a response schema — enforce JSON in four cheap-first layers so unreliable models still return valid output
• Deterministic Writing-DNA fingerprint — 15+ voice metrics at zero LLM cost, 100% reproducible
• Docker image trimmed 2.66 GB → 338 MB (87%) via multi-stage builds; SHA-tagged, health-check-gated CI/CD

A live content-intelligence platform that generates on-voice briefs from real-time trends, learns from every rating and reported result, and runs on either a hosted or self-hosted LLM — behind production CI/CD with an 87%-smaller image.

Legal contracting on the web is mostly PDFs and DocuSign. Lit Law needed real-time concurrent editing, AI-drafted clauses backed by retrieval, hard uptime guarantees, and reliable payments — all at the throughput a production legal platform actually faces.

Node.js + Express microservices over gRPC. PostgreSQL with Drizzle ORM as the system of record, Redis for caching. 40+ JWT-authed REST APIs. RAG pipelines use custom embeddings on Pinecone. Real-time collaboration (presence, concurrent editing) is delivered over SSE. The Next.js (SSR) frontend uses React, Redux, Tailwind, and Tiptap. Razorpay and Cashfree handle payments.

• Custom embeddings over off-the-shelf — better domain fit on Indian legal corpus
• SSE for collaboration instead of WebSocket — simpler ops at the scale we needed
• 95%+ test coverage on the backend; nothing merges without a green eval path
• Two payment providers with idempotency keys — Razorpay + Cashfree at 100% reliability

99.9% uptime, 2.3× throughput improvement, 48% latency reduction. Scales to 1,500+ concurrent users with 500+ monthly signings across two payment providers.