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Engineering methodology

How We Build

From requirement to production — a structured engineering process for AI, software, automation and digital systems.

Delivery pipeline

Requirement to production

  1. 01

    Requirement

    Understand the customer's business problem, goals, users and constraints.

  2. 02

    Discovery

    Identify scope, workflows, dependencies, risks and missing requirements.

  3. 03

    Architecture

    Define application architecture, database, APIs, integrations, AI components and infrastructure.

  4. 04

    AI & API Analysis

    Determine where AI is actually useful, model/provider requirements, RAG/knowledge needs where applicable, third-party and internal APIs, integration dependencies and expected recurring usage costs.

  5. 05

    Security

    Plan authentication, authorization, permissions, data isolation, secrets, rate limits, auditability, privacy and other applicable controls.

  6. 06

    Design

    Translate the approved architecture into user flows, screens and system interactions.

  7. 07

    Engineering

    Implement frontend, backend, APIs, database, integrations and AI components according to the approved scope.

  8. 08

    Testing & QA

    Validate functionality, security boundaries, integrations, responsive behaviour, error handling and critical workflows.

  9. 09

    Deployment

    Prepare production configuration, infrastructure, environment variables/secrets, database migrations, domain/SSL and release procedures where applicable.

  10. 10

    Handover

    Provide the agreed deliverables, documentation, credentials/access transfer where appropriate, operational notes and maintenance guidance.

Project decision layer

Before development

Development begins only after scope, cost and delivery expectations are sufficiently defined and approved.

Requirement
Feasibility
Architecture
Cost
Timeline
Approval
Work Order
Development
AI engineering

AI where it helps — not AI everywhere

OMS IT evaluates whether AI is actually useful before introducing it into a project. Not every project uses AI — a conventional, well-engineered system is often the correct answer. Where AI adds measurable value, we plan the model, provider, data and cost implications during architecture.

Read more in our AI architecture overview.

Evaluation areas

  • AI agents
  • LLM applications
  • RAG / knowledge systems
  • Automation
  • Classification
  • Extraction
  • Summarization
  • Intelligent workflows
  • Human approval
Integration strategy

Own, third-party, or hybrid

Own API

Build when the functionality belongs to the product/platform.

Third-Party API

Use when an external provider is the appropriate authority/service.

Hybrid

Use an internal abstraction/integration layer around external providers where beneficial.

Third-party provider pricing and availability are verified before final commercial commitment where applicable.

Planning

Cost & resource planning

Project planning considers the full resource picture — not just development hours. Each engagement is estimated individually; no generic price list applies.

  • Development effort
  • Infrastructure
  • AI / model usage
  • Third-party APIs
  • Hosting
  • Storage
  • Email / SMS
  • External specialists
  • Maintenance

One-time

Discovery, architecture, design, engineering, testing, deployment and handover — scoped and approved before development.

Recurring

Hosting, infrastructure, AI/model usage, third-party API subscriptions, email/SMS and maintenance — identified during planning.

Engineering standards

Quality & security

Security

Authentication, authorization, row-level data isolation, secret management and rate limiting planned during architecture — not bolted on later.

Testing

Validation of functionality, security boundaries, integrations and critical workflows before release.

Observability

Logging and error monitoring so production behaviour can be inspected and diagnosed.

Backup & Recovery

Database backup and restore procedures appropriate to the hosting platform and project scope.

Documentation

Operational notes, setup instructions and architecture records delivered with the handover.

Maintenance

Ongoing support, dependency updates and improvement cycles agreed per project.

Roadmap

OMS IT Project Architect Lab

Future capability

A structured internal planning concept for turning customer requirements into implementation-ready project plans.

Requirement
Blueprint
API Analysis
AI Analysis
Cost Analysis
Timeline
Security
QA
Delivery Plan

Have a project in mind?

Start with your requirements. We can turn them into a structured technical plan.

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