Green AI Cloud Powered by solar + hydro

“Ultra-Fast, Ultra-Low-Power”Green AI Cloud is here

An agent platform running on our own green data center. Responses complete in milliseconds, powered entirely by our own renewable energy.

See the Architecture
0.5 ms Agent Spinal Reflex technology
Response execution time (self-measured, no LLM inference)
EntreprenAIs Cloud
What is the response speed of the Green AI Cloud?
Reflex response in 0.5ms. A fixed response is returned via the CPU cache path (no LLM inference).
0.5 ms-range reflex response
Ultra-low power, effectively 100% renewable

This is our actual live “Contact AI.” The same conversation as the LIVE DEMO plays automatically.

+ Measurement conditions & disclosure scope for these figures

The meters and figures shown are indicative trend values based on our own measurements. “0.5ms” is our self-measured execution time for the Agent Spinal Reflex technology (a low-latency response path completed entirely on CPU), and it does not guarantee that every request will respond at this figure. We are continuing to refine our measurement conditions (definition of response, benchmark environment, reproducibility) and the definition and disclosure granularity of “effectively 100% renewable” toward greater external transparency.

Live Demo

Speed is something you can only feel in conversation.

The left panel shows the reflex response from our own agent platform. The right panel sends the same prompt to cloud LLM inference (gpt-oss:20b-cloud). The right panel waits in real time, exactly as measured — by the time the left panel finishes the whole conversation, the right panel is still writing its first reply.

EntreprenAIs Cloud cache Own green DC / reflex path
reflex path
Runs automatically when scrolled into view
0.5 ms Cumulative response processing time
No LLM inference / CPU only
Cloud LLM Inference gpt-oss:20b-cloud Third-party DC / full generation every time
cloud inference
Waits in real time, exactly as measured
9.5 s Elapsed time since conversation start
(measured 9.5–22.9 sec / response)

※ This demo is a simulation reproducing our own measured values (2026-08-05). The right panel’s wait time is real time at 1x speed (9.5–18.7 sec per response). It replays automatically once the conversation ends. The left panel returns a fixed response from cache, while the right panel performs full generation each time — the underlying processing differs. The left panel’s typing effect is a display presentation for readability; the actual response processing completes in the 0.5ms range.

+ Notes on measurement conditions, measured values, and the asymmetry of this comparison (4 items)

Measurement conditions: Measured on 2026-08-05 with the same prompt, “What is the response speed of the Green AI Cloud?” Our side measures server-internal processing time via the CPU cache response path (source: cache, a fixed response with no LLM inference). The gpt-oss:20b-cloud side measures total wall time from request to completed response via `eai-ext-ollama` (Ollama’s cloud inference backend, confirmed to use no local VRAM). Measured ranges are 0.5–1.0 ms versus 9.5–22.9 sec. The gpt-oss:20b-cloud figures represent the total time for full generation (including the reasoning process) of 1126–1929 output tokens.

About gpt-oss: gpt-oss is an open-source large language model released by OpenAI, distinct from that company’s commercial APIs (ChatGPT, GPT-4/GPT-5, etc.). Here we measured `gpt-oss:20b-cloud` (via Ollama’s cloud inference backend) as one example of a cloud-LLM-API-based agent.

Asymmetry of this comparison: Our side returns a fixed response from cache, while gpt-oss:20b-cloud performs full generation inference every time — the underlying processing differs. The main driver of the response-time gap is simply “whether inference happens at all.” The ratio ranges from a minimum of about 9,500x (9.5s ÷ 1.0ms) to a maximum of about 45,800x (22.9s ÷ 0.5ms).

Future expansion: As we do not yet have direct access set up to commercial cloud APIs (Claude, GPT proper, etc.), we currently show only measured cloud-inference figures for an open-source model. Wait times for the second demo turn onward are estimated values within this measured range.

Our Approach

Intelligence is now a given.
We design for speed and power.

Model intelligence is advancing rapidly across the industry. Building on that, what a platform needs for agents to keep working day after day is response speed, power consumption, and transparency in power sourcing. We made these three things our design targets.

01 / SPEED

Designing for response speed

Routine responses are returned on the CPU, removing LLM inference and network round-trips from the path entirely. For use cases with many back-and-forth exchanges, this one decision directly determines the quality of the experience.

02 / POWER

Designing for power consumption

We concentrate inference only where it’s truly needed and handle everything else on the CPU. Shrinking the actual power draw pays off for both cost and environmental impact.

03 / TRANSPARENCY

Disclosing our power sourcing

Because inference completes entirely within our own green data center, we can explain the origin of our power ourselves. We operate on the premise of maintaining the audit trails needed to support ESG/CSR requirements.

Architecture

The thinking brain and the reflexive nerves
are joined within a single conversation.

Cerebral AI × Agent Spinal Reflex technology

EntreprenAIs Cloud consists of two layers modeled on the human nervous system: the response-generating "Cerebral AI" and the instantly-reacting "Agent Spinal Reflex technology." What matters is not the split itself, but that the two layers stay joined as a single, continuous conversation. The person talking never notices which response came from a CPU reflex and where it switched over to GPU-based thought. That handoff is powered by our compilation technology, which converts a design into an executable (currently semi-automatic — we are expanding the range it can handle as we operate it).

01

Cerebral AI — deliberate thinking

Complex judgment and generation are handled by a local LLM running inside our own green data center. Because it never goes through a cloud LLM (via a third-party data center), the power sourcing stays entirely within our own renewable energy.

SEAMLESS
02

Compilation technology — from design to executable

Our proprietary technology splits the “design” that the Cerebral AI carefully thought through into a lightweight, CPU-only executable and the parts that still require inference. This is currently a semi-automatic process run alongside human oversight, and we are gradually raising the share of CPU execution as we operate it, expanding the range that can be run cheaply and repeatedly. The idea: “train once on high-performance compute, then run inference daily in an energy-efficient environment.”

SEAMLESS
03

Agent Spinal Reflex technology

Routine responses run on the CPU with no LLM inference, in a process equivalent to a spinal reflex that never involves the brain. This executable is exactly the program that the compilation technology generates. Any question the reflex can’t handle is handed straight over to the Cerebral AI — the conversation never breaks.

Reflex path
0.5 ms

The response execution time of the Agent Spinal Reflex technology (self-measured). Questions the reflex can answer stay on the CPU; questions that need thought go to the Cerebral AI — the switch happens automatically, behind the scenes of the conversation.

LLM inferencenone
Network hopin-house
ComputeCPU / GPU auto
Power sourcerenewable
+ Notes on these figures and our “compilation technology”

“0.5ms” is our self-measured execution time for the low-latency response path in our own agent platform. We plan to increase the transparency of our measurement conditions going forward; for now we present it as an internal, self-measured figure. It does not guarantee that every request will respond at this figure. “Compilation technology” refers to our proprietary mechanism for converting part of a design into an executable program. It is currently not fully automatic but a semi-automatic process run alongside human oversight, and the range of processing it can convert is being expanded progressively. The detailed conversion method is not disclosed.

Green Infrastructure

Solar and hydro power our Green AI Cloud.

Inside our own green data center, AI inference runs entirely on power from solar self-generation and renewable energy contracts (e.g. Aqua Energy 100). Because we never route through an external cloud LLM or a third-party data center, we can account for our power sourcing ourselves — keeping both environmental impact and cost in check.

PV

Our own green data center

We operate on power from solar self-generation and renewable energy contracts (e.g. Aqua Energy 100).

A2A

Removing external dependencies from the path

We eliminate round-trip calls to external cloud LLMs and dependence on other companies’ data center power mixes, keeping our power sourcing entirely in-house.

kW

Ultra-low-power technology, built in-house

Renewable power comes with limits on output and supply, so we built a hybrid architecture in-house to cut power consumption to the absolute minimum.

ESG

Transparency built on audit trails

We disclose our power sourcing externally and make our “green” claims on the premise of maintaining supporting audit trails.

+ Notes on “effectively 100% renewable”

This claim is based on self-generation from our own solar facilities and our renewable energy contracts. We are continuing to refine the definition of “effectively” and the granularity of our disclosures toward greater external transparency. For detailed evidence on individual contracts, please contact us and we will explain to the extent we can.

Why Green AI Cloud

The constraint is what made both possible.

Output & supply constraints on renewable power
→
Need to cut power consumption to the limit
→
Developed ultra-low-power technology in-house
→
Ultra-low power × ultra-fast × green, all at once

Get both speed
and power accountability, at once.

We welcome adoption inquiries and joint research consultations from IT procurement leads and ESG/CSR leads.

About

Taruishi Digital Technology Research Institute LLC (TDRI)

Company Overview

Taruishi Digital Technology Research Institute LLC (TDRI) is a technology company based in Shiroi City, Chiba Prefecture, founded in 2018 and incorporated in December 2021. With the fusion of renewable energy and IT as our core business pillar, we research, develop, and operate the EntreprenAIs platform, which places AI agents at the core of management.

We have zero human employees. Eight AI agents (a C-Suite Agent Fleet comprising CFO, CTO, CPO, CLO, CHRO, CISO, COO, and Auditor) run the management — a company practicing multi-agent management.

Why We Started

Our representative, Masato Taruishi, whose paternal family home is in Iwaki City, Fukushima Prefecture, keenly felt his responsibility as one of the people who made Japan a country dependent on electricity following the 2011 Great East Japan Earthquake. He founded this institute with the goal of establishing digital technology for the stable use of renewable energy.

Representative Profile

Representative Partner Masato Taruishi worked as an engineer on large-scale distributed systems at a foreign-affiliated IT company, Google Japan, and Rakuten, then served as CTO of Retty through its listing on the Tokyo Stock Exchange Mothers market. He currently serves as CTO of a major group company while continuing research as a visiting researcher at Waseda University’s Institute for Data Science. He incorporated TDRI in 2021 and personally practices and researches multi-agent management alongside the EntreprenAIs C-Suite Agent Fleet.