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RedGobble's engineering process is designed to de-risk software delivery at every stage — from requirements to architecture, QA, deployment and long-term maintenance. The same discipline applies whether we are building a landing page or an enterprise platform.
The five-stage process every RedGobble engagement follows.
We document goals, users, constraints and success metrics before writing code. A clear scope prevents the most common cause of failed projects.
Data model, API contracts, security posture and tech stack are designed first — with UI/UX wireframes where the product is visual.
Working, tested increments ship every 1–2 weeks. Demos keep you informed and let us course-correct early.
Automated and manual testing, security review and performance checks run as part of every sprint — not as a final phase.
Staged rollout with monitoring, backups, analytics and post-launch support. Launch is an event, not the end.
Quality is not a phase — it is built into every sprint.
Core logic is covered by automated tests so regressions surface immediately.
Every build is verified on modern browsers and physical mobile devices where relevant.
Core Web Vitals and load behaviour are measured and enforced against budgets.
Authentication, authorization, data handling and dependency vulnerabilities are checked in every sprint.
You validate the software against the agreed scope before anything goes live.
Risky launches are a choice. We choose staged, monitored and safe.
Every change deploys to a staging environment that mirrors production.
Automated backups and a tested rollback path protect against anything going wrong at launch.
Complex systems roll out gradually — region or module by module — with monitoring at each stage.
Uptime, error rates and performance are monitored 24/7 with alerting to the engineering team.
We stay engaged after launch — bug fixes, security updates and feature evolution as your needs change.
Security is considered in every phase — threat modelling during design, secure defaults in code, and dependency scanning in CI.
Encryption in transit and at rest, role-based access control and the principle of least privilege in every system we build.
We build with data-protection regulations in mind — GDPR-style privacy defaults, clear consent flows and audit trails where required.
Automated vulnerability scanning, dependency audits and manual security review before every production release.
Every AI system we build has clear fallbacks, human handover points and guardrails — AI assists, humans decide.
Production AI assistants are grounded in the client's own knowledge base (RAG) so answers are traceable and verifiable.
Client data is never used to train shared models without explicit consent, and PII handling follows strict rules.
We scope AI engagements against metrics — deflection rate, response time, hours saved — so AI investment is provable, not hype.