Ship faster with
AI agents your team
actually trusts.
I come to your office, pair-program with your engineers on real backlog items using AI coding agents, and set up the workflows and quality gates so they keep shipping this way after I leave.
Already delivered for teams at ThriveDesk and LazyCoders.
1–2
Projects
Built from your backlog
1
Full Day
On-site with your team
25+
Years Exp.
Shipping real products
Tools & Topics
1–2
Projects
1
Full Day
25+
Years Exp.
Tools & Topics
“We had Copilot and ChatGPT but honestly no one knew when to use what. Emran spent a day with us and gave us a proper workflow. PR turnaround is noticeably faster now and the team actually enjoys the process.”
Parvez Akther
Founder & CEO, ThriveDesk
“Some of my devs were skeptical, so Emran just opened our repo, picked a real ticket, and built it live. That was the turning point. They’re using Claude Code daily now without me asking.”
Noor M Khan
Founder & CEO, LazyCoders
“I expected a tools demo. What we got was a complete workflow, planning to deployment. Best part? The team started following it on their own the next week. Didn’t have to push anyone.”
Ahmed Naser
Co-Founder & CEO, Crebsol
The Problem
Your team has AI tools.
But nothing has actually changed.
Copilot is installed. ChatGPT tabs are open. Maybe someone tried Cursor. But your team is not shipping faster—and they know it.
Everyone prompts differently
No shared patterns, no consistent output. Each engineer gets different results from the same tools, and nobody trusts what comes out.
AI made things slower
Review cycles doubled. Debugging AI-generated code takes longer than writing it from scratch. The speed promise didn’t land.
Impressive demos, broken production
The AI demo looked great. Then it hit real code, real edge cases, and real users. What works in a playground breaks in production.
The Shift
This is what changes
Before
Each engineer prompts their own way
AI experiments that never reach production
No review process for AI-generated code
Different results from the same tools
More time debugging AI output than writing code
After
One shared workflow the whole team follows
AI agents that ship real features, not prototypes
Review gates that catch problems before merge
Consistent output across the entire team
Engineers who choose AI because it’s faster
Deliverables
What you leave with
01
Working Features Shipped
We pick 1–2 tickets from your actual backlog and build them during the workshop, on your codebase, with your stack. You end the day with merged PRs, not a sandbox project.
1–2
Features shipped
Your
Codebase & stack
02
AI-Powered SDLC Playbook
A documented process your team can follow the next day. How to plan work, generate code, run reviews, write tests, and deploy, all with AI agents wired in.
03
Engineers Who Know the Workflow
After the workshop your team knows how to manage context windows, steer agents with AGENTS.md, and set up quality gates. They practiced it on real code, so it sticks.
Pair-programmed on their own codebase
CLAUDE.md and AGENTS.md configured
Can run autonomous coding loops solo
Is This For You?
This workshop is for builders, not browsers
If you’re looking for an inspirational AI keynote, this isn’t it. This is a working session where we write code and build systems together.
You run a software company and your team writes code every day
You want to ship faster, not just talk about AI
Your team tried Copilot or ChatGPT but nothing stuck
You learn better by building than by watching presentations
You want a process your team follows after the workshop ends
How It Works
From first call to working system
Step 01
Discovery
I review your codebase, understand your stack, and we decide what to build during the workshop.
30-minute call
Step 02
Workshop
I come to your office. We build real features with AI agents, pair-programming with your team.
Full day, on-site
Step 03
SDLC Playbook
You get a documented dev process your team can follow independently, adapted to how you already work.
Custom for your team
Step 04
Follow-Up
Post-workshop check-ins to ensure adoption sticks. Direct access for real questions.
2 weeks included
Capabilities
Skills your team keeps
Managing Context Windows
AI agents lose track in large codebases. The Plan / Execute / Clear loop keeps them focused and useful.
Steering AI Agents Reliably
AGENTS.md files, custom skills, and progressive disclosure give you control over what the agent does and doesn’t do.
Planning with PRDs
Break features into chunks that fit a context window. Validate the architecture with a tracer bullet before writing the rest.
Integrating AI into CI/CD
Your pipeline can run AI-powered tests, reviews, and checks automatically. We set that up during the workshop.
Running Autonomous Loops
Let agents code on their own while you review at checkpoints. Useful for large refactors, test generation, and boilerplate.
Preparing the Codebase
Most repos aren’t set up for AI agents. Small structural changes make a big difference in what the agent can do.
Who Leads This
Mohammad Emran Hasan
Co-Founder & CEO, FIGLAB
I’ve spent 25+ years building software products that have survived real users, real constraints, and real growth. My focus has never been on chasing trends, but on building systems that remain useful long after the initial excitement fades. I treat AI-augmented engineering the same way.
LinkedIn ProfileYour team could be shipping
with AI agents next month.
One full day on-site with your team, building real features and setting up a process that works the next morning. I take on a limited number of teams per quarter.
Book a Workshop