01
Operational AI agents
We start with highly structured work — inbound enquiries, internal procedures, quotation drafting, inspection records — and design with explicit human checkpoints.
APPLIED AI
Never stop at the PoC.
Projects that produce good validation results but never ship almost always fail for the same reasons: nobody owns operations, permissions and logging are undefined, and there is no agreed standard for what to do when accuracy drops. We design from there.
02
RAG platforms
Retrieval over internal policies, technical documentation, drawings and forms, built for Japanese orthographic variation and real document structure, with permission-aware retrieval scope.
03
MLOps / LLMOps adoption
Evaluation, deployment, monitoring and rollback moved onto CI, so that who changed what and when is always answerable.
04
AI strategy & ROI modelling
Sequencing, payback projections and the build-versus-buy split, presented at a level of granularity usable in a board meeting.
05
Internal guidelines
Permitted data, prohibitions, handling of generated output and incident escalation paths, documented at a granularity people will actually follow.
06
Capability transfer
Hands-on training alongside joint development, with the goal that your team runs it independently. Handover assumes the system keeps working after our engagement ends.
Problems this solves
Problems this solves
- The PoC succeeded but production ownership and procedures were never settled
- You want internal document search but permissions and disclosure risk remain unresolved
- Dependence on external vendors persists and no knowledge accumulates internally
- Leadership has never been given a basis on which to decide the investment
Engagement model
Engagement model
| Contract type | Time and materials (assessment, embedded support) or fixed scope (production build) |
|---|---|
| Typical duration | Assessment 2–4 weeks, PoC 6–10 weeks, production build 3–6 months |
| Team | One solutions architect plus two to four engineers, scaled to the engagement |
| Deliverables | Infrastructure-as-code, design documentation, runbooks, evaluation datasets, training materials |
| Sectors | Manufacturing, financial services, research institutions, SaaS, public sector (as subcontractor to a prime integrator) |
Bring us in while it is still an idea.
The most common questions we receive are "how many GPUs should we buy" and "should we own or rent". We welcome conversations long before requirements are settled.