ENTERPRISE AI TRAINING
AI Feature & Product Building
For backend, full-stack, ML engineers
A bootcamp + hackathon on turning that edge into an agent only your company can ship. Claude API, structured memory, tools wired to your systems, evaluation harnesses, MCP servers — a production agent, not a demo. Every feature scored on Profitability and Validation during the hackathon — week 1 prototype proves the score.
Two ways to ship an agent.
Wrapping Claude
A chatbot UI on top of the API. System prompt with a few paragraphs of context. Two or three tools, hand-rolled. Prompts tested by eye. Demos well. Breaks on the second real user.
An agent your company owns
Claude API + structured memory (working + long-term). Tools wired to your actual systems — CRM, ticketing, internal APIs, data warehouse. An MCP server exposing your proprietary capabilities, reusable across every Claude surface. Eval harness with golden traces from real user paths. Deterministic guardrails where failure isn't an option.
The wrapper can be cloned in a weekend. The agent can't — because the knowledge is yours.
WHAT WE MOVE FOR YOUR TEAM
This workshop's Profitability and Validation axes.
Scored during the hackathon — week 1 prototype either earns the build or kills the idea cleanly.
Profitability axes
Scored before the hackathon ends.
UX lift ↑
Quantified user-experience improvement on the candidate feature, measured against your existing UX — not vendor demos.
Build + run cost ↓
Engineer-weeks to ship + per-request infra cost in production, both scoped before commitment — not discovered after launch.
Net ROI ↑
Does the feature pay for itself in Q1? If you'd need a discount to make it profitable, the idea was bad or the build was wrong.
Validation axes
Validated in days. Built in week 1.
Time-to-validate ↓
Test an AI feature idea in days, not months. The bottleneck stops being engineering and becomes deciding.
Kill rate ↑
Most candidate ideas die before any code is written. A 0% kill rate means you're building everything regardless of validation.
WHAT YOU’LL TAKE HOME
- Memory-aware agents — rolling, long-term, compaction, scratchpads
- Tools wired to your systems — schemas, parallel calls, errors you can operate
- ReAct vs Plan-then-Execute vs multi-agent — when each fails
- An eval harness with golden traces — the closest agents get to unit tests
- A custom MCP server exposing one of your proprietary capabilities
- Production-ready — cost, latency, safety guardrails
Take home: a memory-aware agent, an eval harness, a custom MCP server, an SDK-vs-LangChain matrix.
WHO TEACHES THIS
Ajinkya Kolhe
CTO
11 years in AI — from Google Cloud to IIT Bombay to Morgan Stanley. 300,000+ developers across 7+ countries. Built the SIRA risk framework and the live Pulse dashboard. 2x TedX Speaker.
On-site or remote. Built for 6–15 builders — backend, full-stack, ML — who'll ship the agent together. Duration scoped per engagement.
Mail ajinkya.kolhe@purnamedha.ai for team details.
Request dates for your team