01
DX consulting
Assess processes, data and systems, then build a DX and AI roadmap prioritised by ROI.
Deliverables
- As-is assessment report
- Phased DX/AI roadmap
- ROI & budget estimate
- Architecture & technology recommendations
SERVICES
DX and AI, tied to business results.
DX consulting, AI integration, system development, dedicated teams and operations, combined to fit where your organisation is today.
WHAT WE DO
What we offer
02
AI solutions & integration
Design, prove and embed AI in your existing systems: GenAI/LLM, RAG, OCR, computer vision and forecasting.
Deliverables
- PoC design & evaluation criteria
- AI models/agents running in your environment
- API integration with existing systems
- Accuracy evaluation report
03
Systems & data platforms
Build web, mobile and line-of-business systems plus cloud data platforms, connected to ERP, CRM and accounting.
Deliverables
- Requirements & design documents
- Source code & deployment environments
- Testing (unit, integration, UAT)
- Operations & handover documentation
04
Dedicated team (Lab / ODC)
A dedicated engineering team for long-term work, with a PM/BrSE as the bridge, scaling up or down by phase.
Deliverables
- Team matched to your stack
- Weekly progress reports
- Transparent Scrum/Agile process
- Knowledge transfer at exit
05
Operations, maintenance & MLOps
Monitor and maintain systems and AI models, improving continuously on real-world data under an agreed SLA.
Deliverables
- Monitoring & alerting
- Model accuracy tracking
- Bug fixes & security updates
- Monthly operations report
AI SOLUTIONS
AI solutions
Five areas where AI pays off fastest, delivered from PoC to production.
Local AI
Run AI inside your own environment to support document writing, data analysis, programming and more, while limiting the risk of confidential data leaving the company.
Typical use cases
- Run LLMs on in-house servers or closed networks
- Document drafting and summarisation
- Data analysis support
- Programming support
Expected outcomes
- Digitise and structure complex, non-standard data
- Lower risk of confidential data leaving the company
- Higher efficiency and less time spent on tasks
- Lower running costs such as API fees
Local LLMOn-premiseData security
AI agents & workflow automation
Automate repetitive back-office work with AI agents plus business rules, cutting hours and errors.
Typical use cases
- Data entry, reconciliation and aggregation
- Document triage with rules + LLM
- Approval and notification automation (Teams / Slack)
- Robust exception-handling workflows
Expected outcomes
- 60–80% less processing time
- Near-zero manual errors
AI AgentLLMWorkflowRPA
Document processing (OCR + LLM)
Read and extract data from paper/PDF invoices, contracts and forms, and push it straight into your business systems.
Typical use cases
- Read invoices, contracts and applications
- Extract and normalise key fields
- Multilingual processing (VI · JA · EN)
- Feed approval flows with exception alerts
Expected outcomes
- ~70% less processing time
- Near-zero data-entry errors
OCRLLMIDP
Visual quality inspection
Replace manual visual inspection on the line with AI image recognition for higher accuracy and less reliance on scarce experts.
Typical use cases
- Detect scratches, foreign matter, misprints
- Retrofit AI onto existing cameras/sensors
- Real-time inspection dashboard
- Defect data loop for continuous accuracy gains
Expected outcomes
- ~70% less inspection labour
- 90%+ fewer missed defects
Computer VisionEdge AIPyTorch
Demand forecasting & recommendation
Analyse sales, inventory and customer behaviour to forecast demand and make recommendations, cutting excess stock and missed sales.
Typical use cases
- Time-series forecasting (seasonality, events)
- Behaviour-based recommendations
- Inventory and reorder optimisation
- API integration with ERP and sales systems
Expected outcomes
- Fewer missed sales
- Better inventory turnover
ForecastingRecommendationML
PROCESS
How we deliver
Every phase ends with clear deliverables and a decision point, so investment risk stays under control.
- 11–2 weeks
Discovery
Clarify goals, KPIs, current workflow and constraints (budget, legal, existing systems).
Output: Discovery notes, target KPIs
- 22–4 weeks
Assessment & design
Inventory data, define requirements, choose the architecture and plan delivery with risks.
Output: Requirements, architecture, plan
- 31–3 months
PoC / MVP
Prove value quickly on real data, measured against agreed criteria before larger investment.
Output: Working PoC/MVP, evaluation report
- 43–6 months
Build & roll-out
Harden for performance, security and integration, train users, and go live.
Output: Production system, docs, training
- 5Ongoing
Operate & improve
Monitor, review regularly, improve models and help your team take ownership.
Output: Operations reports, improvement plan
ENGAGEMENT
Engagement models & scale
Choose by phase and in-house capacity; you can switch models between phases.
| Model | Best for | Duration | Team | Reference budget (Japan) |
|---|---|---|---|---|
| Advisory / assessment | Setting AI/DX direction, technology selection, second opinions on other proposals | 2–6 weeks | 1–2 specialists | from ¥0.5M |
| AI PoC | Proving feasibility and ROI before a full investment | 1–3 months | 2–4 people | ¥3–8M |
| Fixed-scope project | Requirements are fairly clear and you want milestone-based delivery | 3–6 months | 4–8 people | ¥8–30M |
| Dedicated team (Lab) | Long-term development with evolving requirements and a team of your own | Monthly (6+ months) | 2–5+ people | from ¥1.5M / month |
| Operations & maintenance | Live systems/AI that need monitoring, improvement and extensions | Monthly / yearly | Per SLA | On request |
Advisory / assessment
Setting AI/DX direction, technology selection, second opinions on other proposals
- Duration
- 2–6 weeks
- Team
- 1–2 specialists
- Reference budget (Japan)
- from ¥0.5M
AI PoC
Proving feasibility and ROI before a full investment
- Duration
- 1–3 months
- Team
- 2–4 people
- Reference budget (Japan)
- ¥3–8M
Fixed-scope project
Requirements are fairly clear and you want milestone-based delivery
- Duration
- 3–6 months
- Team
- 4–8 people
- Reference budget (Japan)
- ¥8–30M
Dedicated team (Lab)
Long-term development with evolving requirements and a team of your own
- Duration
- Monthly (6+ months)
- Team
- 2–5+ people
- Reference budget (Japan)
- from ¥1.5M / month
Operations & maintenance
Live systems/AI that need monitoring, improvement and extensions
- Duration
- Monthly / yearly
- Team
- Per SLA
Reference budgets reflect projects in Japan. We send a detailed quotation after reviewing your requirements; for tenders we follow the buyer's financial-proposal format.
Estimate the impact of AI
Enter a process type and the hours spent on it to estimate annual savings.
GET STARTED
Start with a free 30-minute consultation.
Tell us about the problem; we'll assess where AI and DX fit and suggest a way forward on the call. We also accept RFPs and tender documents.