SDTR育てる
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SERVICES

What we do.

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

01

AI Readiness & Opportunity Assessment

Best suited to.
Teams that know AI matters but don't yet know where it creates value for them — the "we should be doing something with AI, but where do we start" situation.
Typical deliverable.
A scoped assessment: where AI realistically creates value in your business, what's technically feasible, which data you'd need, whether to build or buy (model and vendor selection), and a sequenced plan — with the low-value and high-risk options ruled out.
Result it enables.
A clear, prioritized starting point instead of a stalled proof-of-concept — and a defensible plan you can act on.
02

Strategy & Roadmaps

Best suited to.
Teams with more ideas than sequencing — where the question is "what do we build, in what order, and how do we know it's working."
Typical deliverable.
A prioritized roadmap tied to clear goals and KPIs, with the delivery cadence and review rhythm to keep it moving.
Result it enables.
A team that can say what it's building this quarter and why — and can tell whether it's working.
03

Design & 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, proof-of-concept prototypes (PoCs), and structured tests — including MVP scoping and PMF validation — that produce evidence before the expensive build.
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.
04

Applied AI Products

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.
Result it enables.
An AI product you can ship with confidence — measured, guarded, and honest about what it does and doesn't do.
05

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.
Result it enables.
A product that pays off, not just launches — with the growth loops and pricing to sustain it.
06

Developer Enablement

Best suited to.
Platforms and tools that need developers to actually adopt them — where interest exists but hasn't turned into usage.
Typical deliverable.
Developer hackathons, onboarding programs, technical talks, and content designed to move people from curiosity to building — including enablement that upskills your own product and engineering people into confident AI builders.
Result it enables.
Developer interest converted into real adoption and a community that grows through substance.

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

01

Project-based

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

02

Fractional / embedded

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

03

Deeper embedded engagements

For teams that need more, scoped case by case.

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

How it actually works

The usual starting point is a single scoped project with fixed outcomes and a fixed timeline — typically 2–6 weeks — ending in a clear go/no-go decision.

  1. A 30-minute fit conversation.
  2. A written scope: outcomes, deliverables, roles, success measures.
  3. A first project — typically 2–6 weeks depending on scope — with weekly evidence, a decision memo, and a practical handover.

Start in weeks — without waiting for a full-time senior hire, or committing to a permanent role before the problem is understood.

FAQ

01What 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.

02Do you work on AI-specific products, or product work in general?

Both. Some engagements are AI-specific — applied AI products, AI readiness assessments — others are general product strategy, GTM, and roadmap work. Many blend both.

03Project-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.

04How 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.

05How 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.

06Do you just advise, or do you build?

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

07What's your approach to validating an idea before a full build?

Customer discovery, prototypes, and structured tests — including MVP scoping and PMF validation — that produce evidence before the expensive build.

08Can 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.

09Do 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.

10What 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.