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rinselabs

Applied research / AI Engineering

Applied AI.
Engineered for the real world.

Research becomes useful when it meets a real problem. We adapt models, build intelligent systems, and engineer applications with Claude, Codex, rigorous evaluation, and human judgment.

Bring us an engineering problem

The differentiator

Not just AI tools. AI inside a disciplined process.

Anyone can prompt a model to write code. Shipping dependable software still requires clear requirements, deliberate architecture, meaningful tests, and careful review.

More engineering leverage. Faster iteration. Quality designed into the process.

Honest expectations

Actual acceleration depends on project complexity, requirement clarity, integrations, testing needs, and quality standards. We don't promise a fixed multiplier — we explain where AI helps on your project and where it doesn't.

Across the lifecycle

Where AI assists — and where engineers decide.

Conceptual modular software architecture linked by verification pathways
SYSTEM STUDY / AI-NATIVE ENGINEERING

Engineering loop / illustrative

architecture / define boundaries

Requirements · interfaces · security

implementation / Claude + Codex

AI-assisted code · engineer-led decisions

verification / before release

Tests · review · evaluation · CI gates

Human judgment at every release.

Requirements analysis
AI · Summarise stakeholder input, surface gaps, draft acceptance criteria.
ENG · Confirm scope, priorities, and trade-offs.
Architecture & planning
AI · Explore design options, draft ADRs and task breakdowns.
ENG · Choose the architecture; own security and data decisions.
Code generation & implementation
AI · Claude and Codex generate features, scaffolding, migrations, and refactors from clear specs.
ENG · Direct the work, integrate, and judge the output.
Testing & validation
AI · Generate unit and integration tests; propose edge cases.
ENG · Define what 'correct' means; review coverage meaningfully.
Code review & refactoring
AI · Pre-review diffs for bugs, style, and risky patterns.
ENG · Approve every merge. No unreviewed AI code ships.
CI/CD & deployment
AI · Draft pipeline configs, infra-as-code, and runbooks.
ENG · Control releases, environments, and rollback plans.

Methodology

Five stages, AI in the loop.

  1. 01

    Understand

    Requirements, constraints, business context, and acceptance criteria.

    AI in the loop

    AI helps summarise inputs and draft acceptance criteria — people own the decisions.

  2. 02

    Architect

    System design, data models, APIs, security, and implementation plans.

    AI in the loop

    AI explores options and drafts specs; engineers choose and document the architecture.

  3. 03

    Build

    AI-assisted implementation using tools such as Claude and Codex, guided by engineering standards.

    AI in the loop

    Highest leverage: scaffolding, boilerplate, refactors, and well-specified features.

  4. 04

    Verify

    Automated testing, code review, security validation, and performance testing.

    AI in the loop

    AI generates tests and pre-reviews diffs. Human review and CI gates remain mandatory.

  5. 05

    Deliver

    Deployment, CI/CD, monitoring, and continuous improvement.

    AI in the loop

    AI assists with pipeline config and runbooks; releases follow controlled processes.

Work with the lab

Three ways to turn exploration into impact.

01 / APPLIED ENGINEERING

Applied AI

Adapt emerging model capabilities to the problem at hand.

Model adaptation · Fine-tuning · Evaluation · AI applications

02 / APPLIED ENGINEERING

Intelligent Systems

Connect reasoning, knowledge, and tools into dependable workflows.

AI agents · Orchestration · Automation · Integrations

03 / APPLIED ENGINEERING

AI-Native Engineering

Translate promising ideas into software with quality built into the process.

Architecture · Applications · Testing · Modernization · Deployment

Have software to build or modernize?

Share what you're building and your constraints. We'll outline a practical approach and where AI acceleration is realistic.

Start a custom software project Read: Claude & Codex in engineering