AI 原生开发：速度与质量并行
依托自研 AI 产品运营经验与咨询实绩，我们实现更快交付与稳定质量。
AI 开发力
在需求、开发、测试、运维全流程引入 AI，持续提升交付效率。
AI-enabled delivery ratio
90%+
We embed AI in planning, coding, test design, and documentation workflows.
Fastest launch
1 month
Clear MVP scope projects can be shipped in about one month.
Delivery efficiency
Up to 2.5x
Reusable patterns and in-house AI know-how improve implementation velocity.
Quality control
Standardized
Code review, automated tests, and monitoring are mandatory steps.
Execution Know-how from In-house AI Products
We transfer lessons from building and operating our own AI products to client delivery.
- Real incident patterns and fixes are integrated into delivery templates.
- Performance, cost, and quality are optimized together.
- Proven architecture shortens the design phase.
Strong AI Consulting Track Record
We design for adoption, not just PoC completion, with business and governance alignment.
- We prioritize use cases by ROI and feasibility.
- We include governance and security in the rollout plan.
- We support training and operational onboarding.
AI-Integrated Engineering Process
We run AI-assisted delivery from requirements to testing with clear quality gates.
- AI-assisted implementation and test-case design are standardized.
- E2E tests and observability maintain release quality.
- Short feedback cycles improve post-release outcomes.
最快 1 个月上线，600 万日元起
对于范围明确的 MVP，我们可按 1 个月上线目标推进，并支持上线后优化。
AI 驱动开发服务
以 AI 交付模型同时提升速度、质量与成本效率。
React / Next.js Web 开发
从需求定义到运营提供一体化支持，以 KPI 为导向构建可持续成长的 Web 产品。
We define UI, API, and operation requirements together before development to reduce rework and speed up delivery.
After launch, we keep improving conversion, speed, and retention through measurable iteration.
主要范围
- Next.js (App Router) / React / TypeScript implementation
- Admin panel, account system, and payment integrations
- SEO and Core Web Vitals optimization
- Review, testing, and delivery standardization
iPhone 应用开发（Swift / SwiftUI）
覆盖需求、开发、上架与持续优化，提供完整的 iPhone 应用交付服务。
We optimize native UX, offline behavior, and API integration based on real usage patterns.
We support quality control and release management with crash analysis and retention metrics.
主要范围
- Swift / SwiftUI app development and refactoring
- Auth, payment, push, location, and camera integration
- API design and backend collaboration
- TestFlight operation and App Store submission support
AI 导入咨询
从业务梳理到组织落地，帮助企业将 AI 从 PoC 推进到可运营阶段。
We prioritize AI initiatives by ROI and feasibility to avoid over-scoped projects.
We design governance, security, and operation models for practical company-wide adoption.
主要范围
- AI roadmap planning and prioritization
- Prompt design and governance policy development
- PoC design and measurable validation setup
- Operational onboarding and internal enablement
AI 系统开发
构建与现有业务流程深度整合的生产级 AI 系统，而非一次性演示。
We build RAG, workflow automation, and data integrations with access control and auditability.
We optimize not only model quality but also latency, cost, and operational reliability.
主要范围
- RAG and internal knowledge assistant implementation
- Document generation, review, and classification automation
- Enterprise API integration and workflow orchestration
- Monitoring, audit logging, and cost optimization
客户选择我们的原因
结合 AI 产品实战与咨询经验，提供可落地、可持续的开发服务。
KPI-First Architecture
We design systems backward from business KPIs such as conversion, retention, and operational efficiency.
- KPI-to-feature mapping is defined at project start.
- Measurement and dashboard setup are included in implementation.
- Prioritization is based on expected impact.
Implementation-Ready Requirement Definition
We define screens, data structures, and operational flow before coding starts.
- UI and API specs are created together.
- Exception handling and role-based scenarios are documented.
- Future extensibility is considered in domain design.
Speed with Reliable Quality
Fast iteration is combined with review, testing, and monitoring standards.
- Weekly release planning with clear ownership.
- Design and code review checklists are standardized.
- Root-cause and prevention flow is part of operations.
Adoption-Focused Delivery
We support training and handover so solutions are used in day-to-day operations.
- Administrator and end-user guides are prepared.
- Initial operational support and Q&A process are set up.
- Improvement backlog is reviewed in regular sessions.
交付流程
同时展示行业常见周期与我们的 AI 加速交付周期。
Phase 01
Current-State Assessment
Market average: 2-4 weeks
Our speed: 3-5 business days
Analyze systems, workflows, and data quality to define priority issues.
- Assessment report
- Prioritized issue list
- Initial roadmap
Phase 02
Requirements and Design
Market average: 3-6 weeks
Our speed: 1-2 weeks
Define functional and non-functional requirements with operation design.
- Requirement spec
- UI/API/data design
- Test strategy
Phase 03
Prototype / PoC
Market average: 3-5 weeks
Our speed: around 1 week
Validate high-risk assumptions and lock implementation direction.
- Prototype
- Technical decision log
- PoC evaluation
Phase 04
Build and QA
Market average: 12-24 weeks
Our speed: 4-12 weeks
Implement in short cycles with quality gates through review and tests.
- Production-ready application
- Test suite
- Monitoring setup
Phase 05
Release and Enablement
Market average: 2-4 weeks
Our speed: 3-7 business days
Plan launch, train teams, and establish operation procedures.
- Release plan
- Operation manuals
- Support workflow
Phase 06
Continuous Improvement
Market average: monthly cycle
Our speed: weekly cycle
Run KPI reviews and prioritize ongoing enhancements.
- KPI report
- Improvement backlog
- Quarterly roadmap
周期会因需求复杂度、外部系统对接和审批流程而变化。
周期与预算样例方案
以下为代表性方案。明确 MVP 可从 1 个月 / 600 万日元起。
Fast Launch
MVP / small-to-mid business app
Focus on core features and release quickly to validate market response.
From 1 month
600 万日元起（不含税）
PM x1 / Full-stack x2 / AI+QA x1
- Intensive requirement workshop
- Core flows implementation
- Auth, admin panel, analytics setup
- Launch support and initial operation guide
Growth Delivery
Service refresh / mid-scale expansion
Modernize existing assets while improving architecture and operations.
Around 3 months
JPY 12,000,000-20,000,000 (excl. tax)
PM x1 / Frontend x2 / Backend x2 / QA x1
- System assessment and migration plan
- UI/API redesign and operational improvements
- E2E test and monitoring setup
- Improvement cycle enablement
Enterprise Program
Large multi-department delivery
Roll out in phases with governance and security controls for enterprise scale.
Around 6 months
JPY 30,000,000-60,000,000 (excl. tax)
Program PM x1 / Tech lead x1 / Engineers x4-6 / QA x2 / AI consultant x1
- Enterprise architecture and phased rollout design
- Audit log and permission control implementation
- Cross-team training and governance setup
- Post-launch optimization roadmap
以上为初期估算，不含基础设施与第三方服务费用。
质量、安全与运维保障
我们将质量控制与治理能力标准化到交付流程中。
Quality Assurance
Unit, integration, and E2E tests are built into delivery from the beginning.
Security
Access control, audit logs, and vulnerability management are standardized.
Operational Monitoring
Alert thresholds and incident response runbooks are prepared before release.
Cost Optimization
Cloud cost and performance are continuously monitored and optimized.
Documentation
Design decisions and operation procedures are kept maintainable for handover.
Improvement Management
KPI-based reviews drive prioritization and ongoing feature refinement.
案例
每条服务线提供 3 个案例，展示可量化成果。
React / Next.js Development Cases
Three examples of web product delivery and modernization.
B2B Order SaaS Frontend Redesign
Trading SaaS Company
Unified ordering workflow and reduced manual processing time.
挑战
- Complex UI flow
- Frequent input errors
实施方式
- Workflow-driven UI redesign
- Type-safe form architecture
成果
- 42% faster input
- 35% fewer inquiries
技术栈
E-commerce Performance Optimization
D2C Brand
Improved page speed and conversion during campaign peaks.
挑战
- Slow mobile pages
- Limited analytics
实施方式
- Rendering optimization
- Measurement redesign
成果
- LCP improved to 1.7s
- 1.6x mobile CVR
技术栈
Legacy Portal Modernization
Education Service Company
Migrated legacy modules without downtime.
挑战
- No downtime requirement
- Data consistency
实施方式
- Phased migration
- Operational automation
成果
- Zero downtime migration
- 55% less manual operation
技术栈
iPhone App Development Cases
Examples across field operations, EdTech, and healthcare.
Field Work Reporting App
Maintenance Service Company
Digitized reporting and reduced back-office workload.
挑战
- Paper-based operations
- Input quality issues
实施方式
- Offline-first mobile flow
- Photo/location integration
成果
- 50% faster reporting
- Defect rate dropped to 6%
技术栈
Learning App Retention Improvement
EdTech Provider
Redesigned onboarding and notifications for retention growth.
挑战
- Low retention
- Unclear user behavior
实施方式
- Onboarding redesign
- Segmented push strategy
成果
- 1.4x 7-day retention
- 41% push open rate
技术栈
Medical Device Connectivity App
HealthTech Company
Stabilized BLE connectivity and data synchronization.
挑战
- Unstable reconnection
- Data gaps
实施方式
- Connection state machine
- Retry-safe sync design
成果
- Data loss reduced to 0.08%
- 40% crash reduction
技术栈
AI Consulting Cases
From roadmap design to organizational adoption.
Call Center AI Roadmap
BPO Company
Defined practical use-case priority and rollout phases.
挑战
- Too many use cases
- Unclear investment basis
实施方式
- Log analysis
- ROI + feasibility prioritization
成果
- 8 priority areas selected
- Approved implementation budget
技术栈
AI Enablement for Sales Team
SaaS Company
Standardized proposal and meeting-summary generation workflow.
挑战
- Inconsistent output quality
- Security concerns
实施方式
- Prompt templates
- Governance policy design
成果
- 38% less prep time
- 80%+ adoption in 3 months
技术栈
Back-office AI Opportunity Assessment
Mid-size Manufacturer
Defined practical AI initiatives for HR, legal, and finance.
挑战
- Cross-team process differences
- No AI owner
实施方式
- Process mapping
- 90-day rollout planning
成果
- 60% of target workstreams planned
- AI governance team launched
技术栈
AI System Development Cases
Production-grade AI implementation across multiple business workflows.
Enterprise Knowledge Search Platform
IT Service Company
Built RAG-based search with access control and monitoring.
挑战
- Scattered documents
- Low search success rate
实施方式
- Index pipeline
- Metadata and permission design
成果
- 29% fewer internal inquiries
- 45% faster information lookup
技术栈
Contract Review Assistant
Listed Enterprise
Automated first-pass review with legal team governance.
挑战
- Review bottlenecks
- Rule inconsistency
实施方式
- Clause classification
- Human-in-the-loop workflow
成果
- 52% faster first review
- Better quality consistency
技术栈
Support Ticket Classification & Reply Drafting
E-commerce Company
Automated ticket routing and suggested responses for agents.
挑战
- Slow response in peak periods
- Quality variance
实施方式
- Priority classifier
- Reply draft and approval flow
成果
- 46% faster first response
- 18% fewer repeat inquiries
技术栈