AI-Driven In-House Development Support

We embed AI coding agents such as Claude Code into your development workflow and build a team that runs on its own. Below, the three challenges where adoption most often stalls are paired with NASEBANAL support and the technology consulting that backs it.

Challenges We See & NASEBANAL Support

The challenges many organizations face when adopting AI coding, paired with NASEBANAL support. Each item lists the technology consulting that makes it work.

1Scope Selection

Challenge We See

It is never settled how much to hand to AI and how much people should own, so adoption stays at individual use or a stalled proof of concept.

NASEBANAL Support

We prioritize areas that change frequently and see frequent spec revisions — typically the frontend — as in-house candidates, and support the boundary between in-house and outsourced development with contract testing to assure quality across it.

NASEBANAL has a track record of building its own products using AI coding agents such as Claude Code, as well as running contract-test-based quality management in production with open-source tools. We are also in contact with the developers behind OSS tools such as Specmatic and Microcks.

2Development Productivity Evaluation

Challenge We See

With AI coding, rounds of regeneration and rework and unexpectedly high token consumption pile up, and development productivity may fall short relative to the investment.

NASEBANAL Support

We use a template that satisfies cloud-native architecture concepts as the base for AI-coding development, and roll out an internal reference architecture across your apps to avoid rework.

NASEBANAL customizes, for each client, a template built from our own app development experience, and uses it as the input for AI coding. At the same time, we share our know-how with your development team up front to support a smooth start.

3Quality & Cybersecurity

Challenge We See

Reviewing AI-written code falls on a few people; development speeds up, but quality cannot be guaranteed. Cybersecurity risks such as vulnerabilities and risky dependencies slipping into generated code are also more than human review alone can keep up with.

NASEBANAL Support

We build tests, contract verification, and CI/CD quality gates first as the "harness" for AI coding, so generated output can be verified mechanically for both quality and security. Vulnerability, dependency, and secret checks run in the pipeline too, letting human review concentrate on design decisions.

We run contract tests based on API specs and dependency vulnerability audits as quality gates in the CI/CD of several of our own APIs and apps. On the security side, our founder — who led Customer Success at Cloudflare's Japan subsidiary and holds CISSP and CCSP — is involved from the architecture design stage.

Talk to us about adopting AI coding

From scoping what to hand to AI through team setup and quality gates, we support you at the pace your organization needs.