SDTR
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SERVICES

What we do.

Four ways SDTR helps teams turn AI capability into products that ship and grow.

AI Strategy & Roadmap Planning

Best suited to.
Teams that know AI matters but not where it creates business value, or that have more ideas than a clear sequence for testing and building them.
Typical deliverable.
A scoped assessment and prioritized roadmap: the customer problem, where AI realistically creates business value, technical feasibility, required data, and build versus buy — including model and vendor selection. Priorities and sequencing are tied to goals and KPIs, with a development and review cadence and clear reasons for the options to pursue or rule out.
Result it enables.
A clear, defensible starting point, and a team that can explain what it is building this quarter, why, and how it will measure the result.

Ideation & Validation

Best suited to.
Teams about to commit heavy build to an idea that hasn't been tested — where the risk is spending months before learning whether it works.
Typical deliverable.
Customer discovery and scoped MVPs, proofs of concept, and prototypes that test the assumptions behind a product. Focused PoCs provide timely evidence for specific questions; broader prototypes test the intended experience before full development. When speed matters, this includes hackathon-style build sprints that take an idea to a working demo in days.
Result it enables.
A go/no-go decision grounded in real signal, not opinion — and saved build time on the ideas that don't hold up.

Applied AI Implementation & Rollout

Best suited to.
Teams turning AI capability into a real product — agents, assistants, retrieval-grounded features — that has to survive contact with actual users, not just demo well.
Typical deliverable.
A working AI feature or product with an evaluation approach (task success, quality, latency, cost), sensible guardrails, the data and retrieval it runs on, and a clear line between what the model decides and what deterministic logic decides. Delivery includes production rollout, documented operating procedures, and support for your product and engineering team to take over.
  • System design and implementation: architecture, model selection, data and retrieval, integrations, and the division between model judgment and deterministic processing.
  • Evaluation harness: representative test cases, quality and task-success metrics, repeatable evaluation, regression checks, and records of model, prompt, and settings changes.
  • Guardrails and runtime controls: output validation, grounded-response checks, execution limits, and agent or tool permissions where relevant.
  • Production rollout and handover: monitoring, cost and latency targets, timeouts and fallback behavior, provider failover where appropriate, and documented operating procedures.
Result it enables.
An AI product you can ship with confidence — measured, guarded, and honest about what it does and doesn't do. Production controls and a practical handover help your team operate and improve it within the agreed scope.

GTM & Growth

Best suited to.
Products that need to find, activate, and retain users — including launches that cross borders (Japan ↔ global) where the model has to fit local reality.
Typical deliverable.
Activation, retention, and monetization work — experiments, funnel improvements, pricing, and localized go-to-market — driven by real data. For developer-facing platforms, this can also include developer onboarding and technical content that turn interest into actual use.
Result it enables.
A product that pays off, not just launches — with the growth loops and pricing to sustain it.

How engagements run

What clients provide.
Access to the relevant people and context (product, engineering, data, business as needed), and a clear decision-maker on their side. The more direct the access, the faster the work.
Cadence.
A regular working rhythm with weekly evidence — what was tried, what was learned, what's next — so progress is visible, not a black box until the end.
Ownership and handover.
Deliverables and handover materials are documented as set out in the engagement agreement, with the aim of a practical handover and no unnecessary dependency on SDTR.
Stop, extend, or transfer.
An initial project ends with an explicit decision: stop (you have what you needed), extend (deepen the engagement), or transfer (SDTR built it, your team runs it) — practical transfer without unnecessary lock-in.

Engagement options

Project-based

Defined scope, a specific deliverable — typically 2–6 weeks, depending on scope, access, and client dependencies. The normal way to start.

Fractional / embedded

Ongoing part-time product or AI leadership working alongside your team — cadence, roadmap, and delivery.

Deeper embedded engagements

For teams that need more, scoped case by case.

SDTR takes on a small number of engagements at a time.

Discuss a project →

FAQ

What type of teams do you work with?

Seed-to-growth teams that need speed and focus, corporate innovation groups looking for startup-style execution, and cross-border launches between Japan and the rest of the world.

Do you work on AI-specific products, or product work in general?

Both. Some engagements are AI-specific — AI implementation and rollout, AI strategy and roadmap planning — others are general product strategy, GTM, and roadmap work. Many blend both.

Project-based or fractional — which do you offer?

Both. Most engagements start project-based (a defined scope, typically 2–6 weeks) and can extend into fractional or embedded work if it's a good fit on both sides.

How do we get started?

A 30-minute fit conversation, then a written scope, then a first project — typically 2–6 weeks — with weekly evidence and a practical handover.

How is this different from hiring a full-time product person?

SDTR lets you start in weeks — without waiting on a full-time senior hire, or committing to a permanent role before the problem is understood. Engagements can also transfer to your own team once things are running.

Do you just advise, or do you build?

Both, depending on the engagement. Work can include hands-on delivery — AI features with evaluation, safeguards, and production rollout, prototypes, roadmaps — not only recommendations. Where specialist support is needed, SDTR proposes experienced independent collaborators under the engagement's terms.

Can you work under NDA?

Yes. Non-public client and project information is handled under the confidentiality terms agreed for the engagement, and SDTR can work under an NDA agreed by both parties where required.

Do you work in English, Japanese, or both?

Both. SDTR is Tokyo-based and works fluently in English, with business-level Japanese for local context and stakeholders.

What does "SDTR" mean?

It draws from 育てる (sodateru), the Japanese verb for "to nurture" — the aim of helping clients develop products and capabilities they can keep operating and improving on their own.