We build AI agents, document pipelines, and LLM features into the systems businesses already run — quoting, accounting, approvals, reporting, customer replies. Not a chatbot on your website. Our AI systems are in production at more than 200 Japanese firms, and you can open three of our own AI products right now without registering.
Not a strategy deck and not a proof of concept that dies in a browser tab. A working system inside your business, measured against the manual process it replaced. In practice:
The measurement point is the one buyers underrate. Most AI proposals cannot tell you what "working" means. We insist on a number before the build — accuracy on your documents, percentage handled without a human touch, minutes saved per case — because that number is what tells you whether to fund phase two.
Most AI projects that fail in a small business fail for the same reason: someone wrote clever prompts and called it a system. Prompts are the easiest part and the least durable — models change, edge cases arrive, and a pipeline held together by prompt text has no way to tell you it has started being wrong.
So we design the plumbing first. Where does the document enter? What structured shape must come out, and how is it validated before anything touches your accounting system? What is the confidence threshold below which a human must look? What is written to the audit log? Which model does the cheap extraction and which does the expensive reasoning? Those decisions are the system; the prompt is a replaceable component inside it.
The commercial consequence is that you are not locked in. The model sits behind an interface, so when a provider halves its price or a better model appears, you change a setting rather than commission a new project. A shop that hard-wires one vendor's SDK into your business logic has, intentionally or not, sold you a rewrite in eighteen months.
Every consequential action goes through a human. In our own accounting product, AI staff read source documents and draft journal entries; the accountant reviews and approves, and every entry balances to the yen. That is not timidity, it is what makes AI usable in a business where being wrong has consequences — and it is why the system is in production rather than in a pilot. We operate that product ourselves, so we carry the cost of our own shortcuts.
No figures on a web page. But AI projects have unusually predictable cost drivers, and three of the four are about your data rather than our code.
Consistent digital PDFs from three suppliers is a short project. Photographed handwritten forms in two languages from two hundred suppliers is not. Bring us twenty real examples on the first call and we can tell you which situation you are in.
Read a document and output fields is one step. Read it, price it against your rules, check stock, draft a reply, and file it in your accounting system is five — each with its own failure mode and review point. Cost tracks steps, not enthusiasm.
A modern system with a documented API is days. An older on-premise system with no API, or a platform whose approval process takes weeks, is the longest pole in the project and has nothing to do with AI. We surface it in week one.
Getting to broadly useful is fast. Getting to reliable enough that money moves on it takes validation layers, test sets, and review tooling. Deciding honestly which parts need which standard is where the budget is won.
Roughly: a single well-defined automation with a pilot on your own data is typically a 4–8 week build. A multi-step agent workflow spanning several systems, with role-based review and reporting, runs into months. Running costs are per-use model charges plus modest hosting, and we measure the real per-document figure during the pilot so you are not signing up to an unknown monthly bill.
Manufacturers and distributors receiving specifications by email and PDF, then cross-referencing spreadsheets to price them. This is exactly the six hours to 48 seconds case — the highest-return AI project we see in SMEs.
Receipts and invoices read into draft journal entries, expense classification, month-end preparation, and reconciliation — with the accountant approving. In production at 200+ Japanese firms via our own accounting suite.
Turning the files a business already produces into a dashboard with cash runway, department profitability, and a scenario simulator — every number traceable to its source document, which is the only way an owner will trust it.
Retail chains use the same architecture differently: transaction data from the POS systems we build feeds demand forecasting and stock alerts. Property agencies use it for document handling and enquiry triage. The pattern is constant — connect the systems that already hold your data, then let AI do the reading and the drafting.
We do not ask you to take AI claims on trust. Three of our own products are live, running on fictional data, no registration required — and each is a real system in commercial use, not a mockup.
AI staff read source documents, draft journal entries, and prepare the month-end close for Japanese tax firms. The accountant reviews and approves; every entry balances.
A 3D workspace where you hire AI agents like staff — retail, real estate, law, accounting. Switch business type and watch the team work through their tasks.
Health score, cash runway, department profitability, and a decision simulator built from the files a business already produces — with source tracing on every figure.
And the client engagement most relevant to AI automation:
A Japanese precision manufacturer replaced a six-hour manual quoting process with an AI pipeline that answers in under a minute.
Fractional real-estate ownership with regulated investor onboarding, fiat payment rails, and an on-chain token layer.
More nonsense is sold under the word "AI" than any other word in software, so here is our list plainly:
Internal platforms, customer portals, dashboards, and marketplaces — the system your business runs on, not a brochure website.
Point of sale, RFID/NFC cards, payment terminals, and cloud sync for multi-branch retail, food service, and transport operators.
React Native and Flutter apps that ship to both stores from a single codebase, when the app is business logic rather than heavy device work.
You do not, until it is tested on your real documents and your real edge cases — so we do that first. The engagement starts with a narrow, measurable pilot on one process using your own data, with a number attached: how much of it AI handles unaided, and how often a human has to correct it. If the pilot does not clear the bar we agreed, you stop there rather than funding a full build on a hunch.
It will, so the system is designed around that rather than pretending otherwise. Every output carries a confidence signal and a link back to the source it came from, low-confidence items are routed to a person, and nothing consequential is committed without human approval. In our accounting product an AI drafts the journal entries and an accountant approves them — that is the pattern we build, not autonomous decisions nobody can audit.
We use commercial API tiers that contractually do not train on your data, and we design what leaves your systems in the first place — often only the extracted fields rather than the whole document. Where the data is genuinely too sensitive to send anywhere, we can run smaller open models on infrastructure you control, with a frank conversation about the accuracy you trade away. This is settled in writing before the build, not after.
Not if it is built properly. The model sits behind an interface, so switching providers or mixing them is a configuration change rather than a rewrite — we routinely use a fast, cheap model for extraction and a stronger one for reasoning in the same pipeline. This matters commercially: model pricing and capability change every few months, and you should be able to follow that without paying for a new project.
A single well-defined automation — one document type in, one structured output out, with human review — is typically a 4 to 8 week build including the pilot. A multi-step agent workflow touching several of your systems runs longer. Ongoing cost has two parts: per-use model charges, which we measure during the pilot so you know the real number per document, and hosting, which is usually modest by comparison.
That is normally the point. We integrate with major accounting platforms, ERP and CRM systems, email, and shared drives, so the AI works inside your existing process instead of becoming another place staff have to log in. Where a system has no usable API we will tell you honestly what that costs in workarounds before you commit.
In the work we have delivered, it removes the retyping rather than the person. A Japanese manufacturer we worked with cut quote turnaround from six hours to 48 seconds — the sales engineers did not disappear, they stopped cross-referencing five spreadsheets and started answering more customers. If your honest goal is headcount reduction, say so on the first call so we can be realistic about what is achievable.
A 30-minute call with an engineer, in English or Japanese. Bring twenty real documents and the steps a person takes today, and we will tell you honestly whether AI helps and what it would take.