Process

How I turn a manual workflow into working software.

I start by understanding the business problem, reduce the biggest technical risks early, and move into implementation only when the next step is clear.

Week 1

Discovery call

I start by understanding the business process, users, constraints, and where the current workflow breaks down.

You get

A written problem map and a clear next-step recommendation

Week 1–2

Technical audit

I review the current systems, data sources, APIs, spreadsheets, or legacy software so the implementation plan is based on reality.

You get

A technical constraints summary based on your real systems

Usually week 2–3

Prototype

When the project involves uncertainty, automation logic, or AI, I usually build a smaller working version first to validate value quickly.

You get

A working proof of concept for the riskiest part

Weeks 3+

Implementation

Once the approach is clear, I build the production-ready application, integration, or internal system with maintainability and performance in mind.

You get

Production-ready software, shipped in working increments

Ongoing

Launch and improve

After release, I help monitor usage, identify the next bottlenecks, and improve the system based on real operational feedback.

You get

Monitoring, fixes, and an agreed support window

Engagement detail

What a Discovery Sprint should give you.

The goal is to leave the sprint with a decision-ready technical direction, not just a loose conversation about ideas.

What you get after discovery

A clearer map of the process, the main bottlenecks, where automation or AI makes sense, and what should not be automated.

What the sprint output looks like

A practical recommendation with 1–3 realistic solution paths, technical constraints, a rough scope, and an estimate for the next step.

When I suggest a prototype first

If the project depends on messy data, uncertain workflow logic, or AI quality, I usually recommend a small proof of concept before full implementation.

30-minute call