ENTERPRISE AI TRAINING

AI for Developers

For engineers who own or shape SDLC

Developers ship the featuresthat make the revenue.The market moves at rabbit pace.They move at snail pace.

A one-day workshop on closing the gap. Not autocomplete. Not vibes. The actual agentic SDLC workflow — plan mode, subagents, hooks, verification loops — that lets a small team ship like a bigger one, without giving up quality. Pick your stack: Claude Code or Antigravity. You'll walk out with a quantified Impact score on PR throughput, cycle time, and MTTR — and a Risk score with verification subagents running on every commit.

Request dates for your team What's covered

The same ticket. Two workflows.

Without an agentic coder

~2 days / ticket

Read ticket, skim codebase           ~30 min
Write code, autocomplete             ~3 hr
Run tests, debug failures            ~1 hr
Open PR, wait for review             + a day
Address review comments              ~2 hr

2 days for a medium feature

With an agentic coder

~3 hrs / ticket

$ <agent> /plan "add rate limiting to /api/signals"
→ Agent reads repo, proposes a plan across 4 files
→ You review the plan, approve or redirect
→ Agent writes code + tests in plan-mode edits
→ Verification subagent runs devil's-advocate review
→ Commit hook: lint + tests + doc regen
→ You open the PR with 80% of review already absorbed

~3 hours for the same feature

WHAT WE MOVE FOR YOUR TEAM

This workshop's Impact and Risk axes.

Quantified at workshop end and verified at 30 days, on PR throughput, cycle time, and MTTR.

Impact axes

Quantified on your repo — score moves on each.

PR throughput ↑
Same engineers, 2–3× more features merged per sprint — measured on your repo, not benchmarks.
Cycle time ↓
Ticket-to-merge cut from days to hours via plan mode + verification subagents on every commit.
MTTR ↓
Agentic debugging on prod issues — incident-to-fix shrinks from a day to under an hour.

Risk axes

Managed throughout — verification subagents on every commit.

Hallucination ↓
Verification subagents catch fabricated APIs, wrong types, broken imports before review — not after merge.
Cognitive atrophy ↓
Review protocols keep your engineers reading code, not rubber-stamping AI output. Plan-mode forces decisions.

PICK YOUR STACK

Which ecosystem are you on?

Apps you’ll use

Claude Code CLI
Plan mode
Subagents
Hooks
MCP servers
/skills

Integrations

GitHub via gh CLI
Local IDE of choice
MCP for internal APIs and data warehouse
Hooks for lint/test/docs

A day in the life

Before: Rohan, a senior engineer, gets the rate-limiting ticket on Monday. He reads it, skims the API folder, opens five files, writes a draft, runs tests, debugs three failures, opens the PR Tuesday afternoon, addresses review Wednesday. Two days for one ticket.

After: Rohan runs `claude /plan "add rate limiting to /api/signals"`. Claude Code reads the repo's CLAUDE.md, proposes a plan across four files, runs in plan mode with verification subagent on every commit. PR opens Monday afternoon with 80% of review already absorbed. Three hours.

WHAT YOU’LL TAKE HOME

Agentic coder across the full SDLC — not just codegen
A repo-wide context file so conventions never get re-taught
A verification subagent that reviews your PRs
Commit hooks that run lint, tests, and docs automatically
The lift, measured — PR throughput, cycle time, MTTR

Take home: a CLAUDE.md/AGENTS.md, a verification subagent, a hooks config, a 30/60/90-day adoption plan.

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.

LinkedIn →Full bio →

On-site or remote. Built for 10–20 engineers on the same codebase. Enterprise subscription required (we'll help you scope it). Duration scoped per engagement.

Mail ajinkya.kolhe@purnamedha.ai for team details.

Request dates for your team