AI-First
AI-first product delivery
We build mobile products AI-first, using AI as a force multiplier for senior engineers, not a replacement for them. The result: faster to feature parity, extra languages at a fraction of the usual cost, and a production-grade quality bar where it matters.
In plain English
M-Squad helps internal product and engineering teams turn AI-first mobile ideas into clear concepts, working prototypes, build-ready architectures, and implementation support.
What "AI-first" means here
AI handles the 80% of work that is well-understood, boilerplate, glue code, tests, adapters, translations, at a fraction of the time. Senior engineers spend their judgment where it matters: architecture, edge cases, security and product. If the model cannot solve something, the developer does, same as today, just faster on everything around it.
This is the opposite of "vibe coding". Architecture is decided by people before code is written; every pull request is reviewed to the same standard as any other shipped app; specifications, the type system and decision records are the source of truth, not a chat log. More on AI-assisted engineering vs vibe coding.
How AI-assisted delivery works
A spec-driven loop where AI accelerates the work and engineering gates keep it production-grade:
↻ Evals & observability feed learnings back into specs and prompts.
The scaffolding that makes it production-grade
AI accelerates engineering, but engineering scaffolding is what makes it production-grade. The pieces we put around the tooling:
Specs & contracts first
OpenAPI / GraphQL schemas and typed interfaces are the ground truth. AI generates against contracts, not vibes, so the output integrates instead of drifting.
Strong typing as a guardrail
Typed languages (Dart, TypeScript) catch fabricated APIs at compile time. AI-generated code that does not type-check never reaches a pull request.
Context, not chat history
Per-repo conventions, architecture notes and live tool access (issues, CI, error tracking) give the assistant real context, no guessing from a chat window.
Guardrails & quality gates
Lint, type-check, unit tests and AI-assisted review gate every commit. Pinned model versions mean no surprise behavior shifts.
Evals & observability
Regression tests for prompts and AI outputs, plus cost and quality dashboards, keep AI-assisted work measurable and stable over time.
Humans own the code
Senior engineers decide architecture before code is written and review every pull request to the same bar as any production system.
Proof: a working app in days, not months
In one internal prototype sprint, one senior engineer using AI-assisted development rebuilt the core of a mobile operator's customer self-care app as a working prototype, native cross-platform shell integrating the existing backend, secure login, an AI assistant with live account tools, voice input and output, and multi-language support, in four days. Around 18,000 lines of hand-reviewed code, in a handful of commits. It was a prototype, not a production launch, a demonstration of how much faster a senior team can validate product direction when AI is used inside a disciplined engineering process.
The lesson is not "AI writes everything". It is that a senior engineer with the right scaffolding now produces in days what used to take a small team weeks, and the resulting codebase is standard, documented and owned by the client.
From assistant to agent
The next step is products that don't just answer, but act. We design an in-app AI assistant first, with explicit confirmation before any write action and a full audit trail, then expose the same capabilities through a secure, OAuth-scoped Agent Gateway. That lets a customer's own AI, or trusted third parties, manage their account safely on their behalf. The app becomes a platform, ready for the agentic shift ahead of peers.
Who acts
- Your app's AI assistant
- The customer's own AI
- Trusted third parties
Agent Gateway
- OAuth-scoped access
- Confirmation before writes
- Full audit trail · revocable
What it reaches
- Account & billing
- Plans & options
- Usage & SIM
What this means for you
- Faster to value, working increments every phase, parity in a fraction of a typical large-team timeline.
- Multilingual by default, additional languages at much lower marginal cost, QA-validated.
- Quality-first, test-driven, with AI-assisted CI catching regressions before they ship.
- No lock-in, we produce a standard codebase any team can maintain; the AI tooling is an accelerator, not a dependency.
- Privacy-respecting, EU-hosted, zero-retention model options and a DPO-ready data flow when customer data is involved.