If you run software for multi-location operations (retail, logistics, field services) you need to ship small improvements quickly and safely. At Wakapi, we treat AI as part of the workflow, not a novelty. The result is shorter feedback loops, more predictable releases, and higher reliability.

The problem we used as a lens

A leading fast-food chain asked us to standardize inventory tracking and streamline procurement across many stores. Success required real-time visibility, fewer stockouts, and a release cadence that didn’t interrupt operations.

Our baseline architecture

We favor a clear, typed stack that makes AI assistance accurate and reviewable.

• Frontend: React with a mobile-first design system for store devices
• Backend: Node.js + NestJS, typed DTOs, modular boundaries
• Data: PostgreSQL for transactions; Redis for hot reads
• CI/CD: GitHub Actions, IaC for reproducible environments, feature flags for safe rollouts
• Observability: central logs, app metrics, synthetic checks mapped to SLAs

Why those choices? Strong conventions and types give AI the context it needs to generate useful scaffolds, tests, and docs while keeping human review efficient.

Where AI fits, end to end

We weave AI into each stage to remove toil and compress the cycle.

• Discovery & planning: first drafts of PRDs, acceptance criteria, and edge cases
• Coding in the IDE: scaffolding modules, mapping DTOs, proposing validation and glue code
• API & contracts: suggested controller/service signatures from a spec and sample payloads
• Testing: outlines for unit, integration, and E2E flows that engineers harden with assertions and fixtures
• Docs & comms: READMEs, runbooks, and release notes generated from merged PRs

A sprint, narrated

Grooming surfaces gaps like offline counts and partial transfers. Tech leads sketch modules; AI converts them into checklists of interfaces, data contracts, and tests. During build, developers rely on AI completions while linters and type checks guard PRs. E2E scenarios run on every change, with AI helping deflake tests quickly. When we ship, feature flags and small canaries reduce risk; AI compiles readable release notes and links them to dashboards so ops can monitor impact.

Lessons that scale beyond one project

• Conventions beat clever prompts; types keep suggestions on track.
• AI boosts productivity but never replaces engineering judgment.
• Test generation is high-ROI even when humans refine it.
• Internal prompt templates create consistent results across teams.
• Measure what matters: PR cycle time, deployment frequency, coverage, incident rate, MTTR.

Security and governance you can trust

We never place sensitive payloads in prompts and use synthetic or masked data for examples. We track licensing/attribution for generated code. Human approvals remain mandatory for architecture changes, secrets, data residency, and schema migrations.

Impact you can expect

• Teams building workflow-heavy apps typically see:
• Faster releases: shorter test cycles and safer rollouts
• Higher reliability: broader, behavior-focused coverage and fewer regressions
• More scope with the same team: boilerplate and docs take less time

Ready to try it?

If your roadmap includes modernizing a legacy workflow app or launching a new operational system, an AI-assisted development workflow is a pragmatic force multiplier. Contact us at hello@wakapi.com and let´s discuss your next project.