ABOUT

About SDTR

AI projects can fail even when the technology works — because the problem isn't urgent enough, the value case is weak, or adoption never follows. SDTR starts somewhere different: with what to build and why — the customer's real problem and whether the thing is worth building at all — before any conversation about technical strategy or sprinting on features.

How SDTR thinks.

The what and the why come first.

Before architecture, before Jira, before agile ceremony: who is this for, what's their burning problem, and does solving it actually make business sense — profitability, competitive landscape, real demand. Technology serves that answer; it doesn't lead.

Products, not demos.

An AI feature that wins a demo but breaks in front of real users isn't a product. Evaluation, guardrails, and clear operating limits belong in the build, not as an afterthought.

Evidence over opinion.

Discovery, prototypes, and testing inform investment and go/no-go decisions before a larger build.

Built to hand over.

Engagements are structured to leave the client with documented outputs and a practical handover, with the aim of avoiding unnecessary dependency on SDTR.

Used, not just launched.

A product only counts when people adopt it and keep using it. Activation, retention, and real adoption — 定着 — are part of the work, not what happens after it.

育てる (sodateru) — to nurture growth.

The name reflects the aim: helping clients develop products and capabilities they can continue to operate and improve.

How SDTR works with clients.

Engagement lead.: The person you talk to is the person doing the work — a single senior point of contact throughout the engagement.
Scope.: Paid engagements are governed by a written proposal, statement of work, or other agreement covering objectives, deliverables, responsibilities, assumptions, acceptance criteria where applicable, and handover terms.
Confidentiality.: Non-public client and project information is handled under the confidentiality terms agreed for the engagement. SDTR can work under an NDA agreed by both parties where required.
AI and data handling.: Use of client-provided confidential, personal, or production data with a third-party AI service requires the client's prior written approval of the relevant service and data-handling arrangements.
Ownership and handover.: Deliverables and handover materials are documented as set out in the engagement agreement. Ownership and licence rights — including treatment of pre-existing materials, reusable methods, open-source software, and third-party components — are defined in that agreement. The aim is practical transfer without unnecessary lock-in.

Specific confidentiality, data-processing, security, IP, and handover obligations are governed by the applicable engagement agreement.

Who's behind it.

SDTR is founder-led by Daniel Jimenez — a hands-on product leader with 15+ years taking products from first idea to scale. He has served as CPO of an international DTC brand whose products were featured in international media, led product and growth for an AI-enabled FinTech platform, and works hands-on across applied AI, FinTech, and consumer businesses. He is a published AI researcher (multi-agent systems & semantic web), an AI hackathon winner, and a TEDx speaker. Tokyo-based; fluent in English and Spanish, with business Japanese. Where specialist support is needed, SDTR proposes experienced independent collaborators — design, engineering — for the client's prior approval, under the engagement's confidentiality, IP, and data-handling terms.