Nine linked pipelines. One production AI product.
From raw enterprise data to a governed, observable AI product in production. You'll build the full stack in one workshop instead of piecing it together from nine different tutorials.
09 Pipelines Covered · 1 Reference System Shipped · 0 Toy Demos
Every stage below is something you'll stand up yourself. The numbered stops map straight onto the nine workshop modules, so by the last session, this is a system you actually own, not a slide you saw once.
Turn systems, documents, and events into clean, structured input the rest of the stack can trust.
Event-driven updates that keep the knowledge base current as source systems change.
Vector, graph, and keyword search fused into one ranked hybrid retrieval pass.
Ranking, compression, and formatting tuned to the model you're actually calling.
One stable interface routing, rate-limiting, and failing over across models and providers.
Calling a chat API is an afternoon's work. Running that same system against real company data, at production traffic, without leaking context or silently degrading, that's a different job, and it's the one companies are actually hiring for.
One notebook, one document, one clean answer. Works every time because nothing ever changes.
Ten data sources updating on ten different schedules, conflicting answers, and a retrieval layer that has to know what it doesn't know.
A single call to a single model, judged by whether the output "looks right."
A gateway routing across models and providers, an evaluation pipeline scoring every change, and a trace for every request when something breaks at 2 AM.
Nine production pipelines across six phases. Raw enterprise data in the morning, a governed, observable, evaluated AI system live by the end of the day.
Turn systems, documents, and events into clean, structured input the rest of the stack can trust: chunking, normalization, and metadata design that decides everything downstream.
Keep the knowledge base current as source systems change, without a full reprocessing run every time something upstream moves.
Model relationships between entities so answers reflect how the business actually connects things, not just what happens to be textually similar.
Combine vector similarity, keyword search, and metadata filters so retrieval finds the right passage instead of just the most similar-sounding one.
Assemble the right context window: ranking, compression, and formatting tuned to the model you're actually calling, not a generic prompt template.
Route, rate-limit, cache, and fail over across models and providers behind a single stable interface your application never has to know changed.
Give the system hands: tool calls, multi-step plans, and the guardrails that decide what it's actually allowed to do on its own.
Trace every request, token, and decision so you can debug a system you didn't personally watch run, the difference between guessing and knowing.
Measure quality against real metrics, not vibes, so you know a change made things better before your users find out it didn't.
Apply the cross-cutting layer that runs under everything: access control, PII handling, and per-model cost tracking wired in from the start.
Chain all nine pipelines, ingestion through evaluation, into one working system: enterprise data in, a governed, observable AI application out.
Built from years architecting data systems. Keynotes and workshops, in-person or virtual, that leave you informed, capable, changed. Over 1,000 professionals across past workshops walked out doing things differently.
Eight capability domains. One production AI platform, built end to end with real enterprise data. Built live, in one day.
Design the full stack end to end with AI-assisted trade-off analysis: ingestion, retrieval, gateway, and agent layers reasoned together, not bolted on.
Design ingestion from systems, documents, and events with Claude Code: chunking strategy, metadata design, and update triggers, directed instead of hand-wired.
Direct AI to tune vector, graph, and keyword retrieval together, at a depth and speed manual relevance tuning can't match.
Build entity and relationship graphs that ground retrieval in structure, not just semantic proximity, with consistency across the knowledge layer.
Architect what actually reaches the model: ordering, compression, and budget decisions that separate a reliable answer from a hallucinated one.
Embed access control, PII handling, and action guardrails into the stack's design, not as an afterthought, but as architecture.
Trace requests end to end and build automated evals, so regressions get caught before they ship, not after a user complaint.
Route, cap, and track spend per model and per pipeline through the AI gateway, so cost stays visible instead of surprising you at month end.
One day separates the engineers who ship production AI systems from the ones still wiring an API call to a prompt.
Your org has an AI initiative that needs to move past the demo. You're the one who can wire ingestion through evaluation into something that holds up in production, not just a notebook.
Product needs a working RAG system by Friday? You ship Thursday. Agent pipeline due next quarter? Architecture's ready next week. The person who makes the deadline leads the next one.
Most engineers have opinions about AI. You have artifacts: a working retrieval pipeline, a knowledge graph, an observability dashboard, a physical handbook. "I know how to do this" versus "here is how I did it."
You're not working longer. You're working with a full stack others are still assembling piece by piece. Your team notices. They start asking what changed.
40 seats.
One cohort. Chennai.
Our last data architecture cohort's early bird filled in 4 days. Everyone in this room is building the same nine pipelines you're expected to own within the next year, whichever side of that curve you're on before it becomes the baseline instead of the edge.
Early Bird ₹2,999 for the first 10 seats. Standard pricing of ₹3,999 after that. Cohort closes at 40.
This is not an introduction to prompting. It is a precision workshop for senior professionals who already know how enterprise systems work and want the full production AI stack, with AI directing the heavy lifting. Junior engineers are not the audience.
The AI stack is the leverage skill your systems experience is missing. Claude Code compresses years of learning retrieval and agent design into one day.
Who make cross-team technical decisions and need AI platform trade-off analysis, retrieval, gateway, agents, in the same toolkit as their software judgment.
AI is the new leverage tool for the platform design work you already do. Move from a single API call to a full production stack in hours, not weeks.
Who build and operate pipelines and want to move from implementation to full architectural ownership of the retrieval and knowledge layer.
Who evaluate platform decisions across software and AI, and want production AI fluency to move as fast as their software judgment already does.
Who lead engineering teams and want AI stack literacy to raise their team's platform decisions, not just their code.
Real feedback from a past Developer Road Ahead workshop.
Practical eye-openers for many Claude AI tools and tricks. The session pushed me to purchase a Claude Pro subscription and use it for my personal products. I followed up by attending the Agent harness session. Looking forward to the Claude Data Architect session.
Hands-on experience was great. It was great learning about skills, tools, and sub agents.
Architecture-first AI development, building maintainable codebases, and the importance of clear requirements, thinking beyond rapid development.
Spec-driven development using Claude, LLM integration, Skills usage, and Hooks.
Very nice and interactive.
Here is what this day is worth, line by line.
No. You need to be comfortable writing code and working with APIs. Everything ML-specific, embeddings, retrieval, evaluation, is taught from first principles inside the workshop.
Open, swappable components throughout. You'll learn the pattern behind each pipeline, not a single vendor's SDK, so the architecture transfers to whatever your company already runs. Bring your laptop and a Claude subscription.
Architecture is a discipline that sharpens in rooms where engineers push back on each other's assumptions. The real value of this day is not the content alone; it's implementing a real AI stack with expert oversight in a space where every question gets a direct, technically honest answer. Online courses cannot replicate that.
One full day, 9:30 AM to 5:30 PM. No advance prep required beyond bringing a laptop and an active Claude subscription; every pipeline gets built live, during the session.
Yes. A full refund is available up to 7 days before the workshop date. After that, your seat is non-refundable but transferable to a teammate.
Real implementation. Real enterprise scenarios. Real expert. 40 seats only.
Peninsular Research Operation
3rd Floor, 319, Babu Mudali St, Ellaiamman Colony, Teynampet, Chennai 600086 · 🚇 DMS Metro