From first conversation to shipped feature. Here's what actually happens — so you can decide whether it's the right fit before the trial starts.
2-Week Free Trial • 1-Day Termination Notice Zero • Client Churn
7-Phase Process
No black box. No surprises. Every phase is visible, auditable, and designed to catch problems before they reach production.
Phase 1
Application Flowchart
A working map of user flows, logic, and features — used to make fast decisions, knowing assumptions will evolve.
High-Fidelity Prototypes
Development-ready screens built with real design systems — so you can validate with users early. It's cheaper to fix decisions now than in later sprints.
Building a new product? See MVP Development and Web App Development
Phase 2
Plan-Before-Build
Every feature is reviewed via an AI-generated implementation plan (architecture, edge cases, dependencies) before development starts — catching issues early instead of mid-sprint.
Deliverable: Project Execution Plan
The Tech Lead breaks down every task, estimates effort, and builds a clear execution plan — timeline, budget, and feature priorities.
Want to understand how we scope and estimate? Talk to a Tech Lead
Phase 3
Deliverable: Feature Prioritisation List
Your team scores each feature by impact. Combined with effort, this creates a clear priority order. High-impact, low-effort features go first — turning scattered decisions into a structured roadmap.
Output
High-impact, low-effort features go first — turning scattered decisions into a structured roadmap.
Phase 4
How It Works
Developers use AI within strict project guardrails, and every feature is reviewed before build to catch edge cases and architectural risks early.
Result
Faster delivery, smaller teams, and fewer downstream issues. Development runs on an AI-accelerated workflow with senior oversight — not AI on autopilot.
See how AI fits into our managed team model: AI-Assisted Development
Phase 5
No Code Ships Without 4 Reviews
AI flags risks first, then engineers and Tech Leads review for logic, architecture, and alignment — with architect sign-off on critical changes. Every PR, every time.
Why It Matters
We catch issues early, not in production — protecting speed without sacrificing quality.
See how this applies to QA: QA & Testing Services
Phase 6
How It Works
Daily Loom updates show real progress, your Tech Lead is available on Slack, and every invoice is tied to a GitHub PR you can audit. No chasing updates. No black box. Just clear visibility into what's being built and billed.
See the full managed team model: Managed Engineering Teams
Phase 7
How It Works
Every release goes through a final QA pass, then the Tech Lead stays involved post-launch for fixes, monitoring, and next steps.
No Handoffs
The same team builds, deploys, and supports your features. Cleaner releases, faster stabilisation, and no context loss after deployment.
See our QA process in detail: Manual QA Testing • Automated Testing
Every engagement starts with a 2-week free trial on your actual work — your codebase, your tickets, your Tech Lead running the process above on real features.
1-day termination notice • No long-term contracts • Real features, real codebase • Zero client churn
Where to Go Next
The core structure — design exploration, project planning, feature prioritisation, AI-accelerated development, 4-level code review, daily transparency — applies to every engagement. The phases that apply depend on where you are: if you're extending an existing product, you skip Phase 1. If you're doing staff augmentation, the process integrates into your existing workflow. The Tech Lead tailors the process to your situation during the first conversation.
Before any feature moves to development, the Tech Lead reviews an AI-generated implementation plan — the architecture approach, the edge cases, the dependencies, and the potential failure modes. The plan gets reviewed and corrected before a line of code is written. This is the step that catches the decisions that would otherwise become sprint problems, refactors, or production bugs.
AI generates code faster than humans. It also generates plausible-looking code that has subtle problems. The 4-level review chain is specifically designed to catch what AI misses — the Tech Lead review checks architectural alignment, the architect sign-off covers security and data model implications, and the AI review layer itself generates a confidence score for every merge. Speed comes from AI. Safety comes from the review chain.
The Loom is a screen recording of actual work — not a slide deck or a summary. You see the code, the PR, the issue being worked on. The invoice links every billed hour to the specific GitHub pull request it produced. If you want to audit a specific day's work, you can trace it to the commit. That's what we mean by transparency as a mechanism, not a value statement.