Engineers levelling up to senior/staff, tech leads de-risking a build, and founders sanity-checking an AI bet. If you're past tutorials and now facing real design trade-offs — orchestration, grounding, evals, cost, reliability — this is the room for that conversation.
What We Work On
Your system, your decisions — with a second set of experienced eyes
Agentic system design
Orchestration topology, state, tools/MCP, memory, and where reliability comes from. Choosing patterns that survive real load.
RAG platform design
Ingestion, retrieval quality, hybrid search, caching, and evals — designing for accuracy you can measure, not hope for.
Voice AI architecture
Realtime vs pipeline stacks, latency budgets, barge-in, and cost — the trade-offs behind a responsive voice agent.
AI backend & scale
Async FastAPI, multi-tenancy, streaming, data model, and the distributed-systems fundamentals that keep it up.
Evals & reliability
Making non-deterministic systems trustworthy: eval strategy, observability, guardrails, and safe failure.
Career & interviews
Growing into senior/staff, system-design interview prep, and the judgment that separates levels.
Formats
Ongoing 1:1 Mentoring
Regular sessions as you build and grow.
- Recurring live sessions
- Your codebase and decisions
- Async questions between sessions
Architecture Review
A focused deep-dive on your current design.
- Review of your system + docs
- Orchestration, evals, cost, reliability
- Prioritised recommendations
Founder Advisory
Sanity-check an AI bet before you commit.
- What's real vs hype for your case
- Build vs buy, stack, and staffing
- Fractional-CTO style guidance
10+ years shipping production systems — Amazon (Payments), Agoda, and CoinSwitch (scaled 600K → 10M users) — and now building Yuvan, a live voice-first AI tutor on LangGraph, RAG, and OpenAI Realtime. The advice is grounded in systems that run, not slideware.
Frequently Asked Questions
What is AI architecture mentoring?
1:1 sessions where we work through the design of a real AI system — agent architecture, RAG platform, voice pipeline, or AI backend. You bring the problem or codebase; you get concrete, senior-level guidance from someone who has shipped these systems.
Is this for individuals or teams?
Both — individuals levelling up to senior/staff and unblocking a build, teams running design reviews, and founders sanity-checking an AI bet.
Can you review our existing architecture?
Yes. A common format is a focused review of your current design — orchestration, retrieval, evals, cost, reliability — ending with prioritised recommendations.
How is this different from the training pages?
Topic training (LangGraph, RAG, MCP) teaches you to build a component. Architecture mentoring zooms out to whole-system design trade-offs and your growth as a senior engineer.
Related Training
Agentic AI Training →
Build the agent, then design the system around it.
LangGraph Training →
The orchestration backbone, in depth.
RAG Training →
Grounding and retrieval quality, in depth.
MCP Training →
Tooling your agents cleanly via MCP.