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How we work

Our AI-native process

Eight phases across four stages, every phase AI-accelerated and senior-led. And running across all of them, an AI layer that keeps the product reliable: data, models, evaluation, guardrails, cost, and response quality.

01/Four stages

  1. 01 – 02

    Discover

  2. 03 – 04

    Design

  3. 05 – 06

    Build

  4. 07 – 08

    Launch

02/The AI layer

Beyond the SDLC: the AI Product Lifecycle

Building with AI isn’t the old process with a model bolted on. The classic software lifecycle still applies, but AI adds a layer of responsibilities on top of it.

  1. Discover
  2. Design
  3. Build
  4. Launch

Runs across all phases

  • Data quality
  • Model selection
  • RAG / agent design
  • Prompt & model evaluation
  • Guardrails & AI security
  • Cost monitoring
  • Continuous response-quality improvement

SDLC builds software that works.

AI Product Lifecycle builds software that works reliably with AI.

03/The pipeline

01

Planning

DiscoverWeek 1

Every build begins with clarity. We run structured discovery to understand your goals, constraints, users, and success metrics, then pin down what “done” actually means.

  • Stakeholder alignment workshops
  • Goals & KPI definition
  • User & journey mapping
  • AI opportunity identification

Output

Project charter & success metrics

02

Scoping

DiscoverWeek 1-2

We break the problem into deliverable units and weigh each against business value and AI leverage, identifying what AI builds in hours versus what needs human engineering.

  • Feature decomposition & prioritization
  • AI automation mapping
  • Build-vs-buy analysis
  • Milestones & risk mitigation

Output

Scope document & sprint plan

03

Design

DesignWeek 2-3

Wireframes, high-fidelity UI, flows, and data models. Design isn’t just how it looks. It’s aligned with the architecture underneath it.

  • Wireframes & UX flows
  • High-fidelity UI design
  • Data model design
  • API contract specification

Output

Design system & prototypes

04

Architecture

DesignWeek 2-3

Our senior architects design the scalable, secure foundation your product runs on. The decisions made here shape the next five years of your product.

  • System architecture design
  • Cloud infrastructure planning
  • Security & compliance design
  • AI model & pipeline design

Output

Architecture decision records

05

Code

BuildWeek 3-8

AI agents write boilerplate and accelerate every layer; our engineers direct, review, and own the result, keeping quality, security, and architectural fit intact.

  • AI-assisted code generation
  • Senior engineering review
  • Feature implementation
  • Continuous integration

Output

Production-ready codebase

06

Test

BuildWeek 4-8

Testing runs alongside development. AI generates suites and finds edge cases; humans own judgment on critical paths and security.

  • AI-generated unit & integration tests
  • End-to-end automation
  • Security & penetration testing
  • Performance & load testing

Output

Test reports & quality sign-off

07

Delivery

LaunchWeek 8

We deploy with care: staged rollouts, monitoring, and performance checks. Then we hand over clean documentation so your team is set from day one.

  • Staged deployment to production
  • Monitoring & observability
  • Documentation & handover
  • Knowledge transfer

Output

Live product & runbooks

08

Support

LaunchOngoing

Launch is the start, not the end. The AI product loop (monitor, evaluate, improve) keeps response quality, cost, and safety on track long after go-live.

  • Cost & response-quality monitoring
  • Model & prompt tuning
  • Guardrail & safety updates
  • Feature iteration

Output

A continuous improvement loop

Ready when you are

Let’s build something that ships.

A product idea, a workflow to automate, or a legacy system to modernize. Tell us where you’re headed and we’ll tell you how we’d get there.

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