AI automation engineer for businesses in Japan, from Tokyo and Osaka to all 47 prefectures

Who builds your automation, and which businesses in Japan it suits

BtechWaleTech is a three-person engineering team based in India. Another of us leads the AI and machine learning side: language models, retrieval, evaluation, data, AWS and deployment. The third of us runs delivery and works on data science, AI and automation, so he is usually the person who maps your current process and turns it into a written scope. One of us is the full stack developer who builds the admin screens, APIs and integrations that make an automation usable by staff who never want to see a prompt. When you message us, one of these three replies, and the same three people write, test and support the code. There are no account managers and no subcontractors.

We have no office, business registration or staff in Japan, and we do not meet clients in person. We also work in English. None of us writes natural Japanese, which matters more for AI work than for most IT projects, because the output of a Japanese chatbot or an AI-drafted email is itself Japanese text. How we handle that is described in detail below: we engineer the system, the prompts, the retrieval and the checks, and a native speaker on your side approves the tone and wording before anything reaches a customer.

This service suits businesses in Japan that already know where the hours go: a sales office retyping FAX orders, a support desk answering the same twenty questions in Japanese and English, a back office copying invoice data into freee or Money Forward Cloud, or a manager who spends Monday writing 議事録 (meeting minutes). It works best when someone on your team reads English, or when a bilingual colleague joins our chat. Foreign-affiliated companies, international startups in Tokyo and Fukuoka, inbound tourism businesses and Japanese manufacturers working with overseas partners are natural fits.

It suits you less if you want a vendor to run 生成AI (generative AI) workshops in Japanese at your office, write your Japanese answer templates without review, or register as a support provider for an IT subsidy. A Japanese AI consultancy or system integrator is the better choice then, and it is better to hear that from us before any money changes hands. Our Japan page explains how we work with Japanese companies on websites, apps and software as well.

One more honest point: we do not have a portfolio of Japanese AI projects to show you, and we will not invent case studies, logos or reviews. What we offer instead is a small, clearly scoped first workflow, a private staging environment you can test with your own documents, and a written scope that lets you judge the work on measured results rather than on our claims.

Why AI automation pays off for businesses in Japan

Japan has been short of people for years, and the gap is widest in exactly the roles automation helps with. The Ministry of Economy, Trade and Industry (METI) estimated that Japan's IT workforce could fall as many as 790,000 people short of demand by 2030, and the working-age population keeps shrinking as the country ages. For a small or mid-sized company that means two things at once: hiring an in-house AI engineer is hard and expensive, and the clerical staff who keep orders, invoices and customer replies moving are also hard to replace when they retire.

Much of that clerical work is repetitive in a very Japanese way. Orders still arrive by FAX and as PDF attachments, quotations are prepared in Excel from price lists that live in someone's head, and confirmation emails follow strict business formats. The same information is typed into a core system, a kintone app, an accounting tool and a Chatwork message. Each step is small, but together they fill whole working days, and every retyping step is a chance for a wrong product code or quantity.

Generative AI changed what can be automated. Older rule-based RPA could click through a fixed screen but broke when a supplier changed the layout of an order form. Language models can read a messy FAX, a polite email or a scanned delivery note, pick out what matters and hand it to ordinary code that validates it against your master data. That combination, AI for reading and drafting and plain software for checking and writing to systems, is what we build for 業務自動化 (business automation).

Service expectations matter too. Japanese customers expect fast, accurate and polite responses, and a support team that replies within minutes on LINE, in the customer's language, with the correct order status, earns trust. Automation lets a small team deliver that level of service without adding headcount, and it frees experienced staff for the cases that genuinely need their judgement.

Where to start: map one process and count the hours

The most common mistake with AI projects in Japan is starting from the technology: a company decides it needs 生成AI or a chatbot, runs a proof of concept, and then cannot find a workflow where it saves real time. We work the other way round, starting from one process that already costs you hours every week.

You walk us through the process as it runs today, using real but anonymised examples: five FAX orders, ten customer emails, one month of invoices. We write down each step, who does it, how long it takes, which systems it touches and where mistakes happen. That map usually shows that only two or three steps need AI at all; the rest is plain integration work, which is cheaper and more reliable. Five numbers decide whether the project is worth doing:

• Volume: how many items per day or month, for example 40 FAX orders a day or 300 support emails a month.
• Time per item: measured, not guessed. Ask the person who does the work to time ten items.
• Cost of an error: what a wrong quantity, a missed reply or a late invoice actually costs you in money, rework or trust.
• Share of exceptions: how many items are unusual and need a person, such as handwritten notes, new customers or special prices.
• Systems: where the data comes from, where it must end up, and whether those systems offer an API.

A worked example shows the arithmetic. If 40 FAX orders a day take about four minutes each to key in, that is 160 minutes, or two hours and forty minutes of staff time, every day. If AI-OCR and validation handle most orders and a person spends thirty seconds confirming each one, daily effort drops to about twenty minutes plus the exceptions. These are illustrative numbers, not a promise: your own measurements decide whether the project pays, and we will tell you if they say it does not.

Good first projects tend to have high volume, a clear right answer and a person who already checks the result: order intake, invoice filing, enquiry routing, first-draft replies and meeting summaries. Poor first projects are those where the right answer is a matter of taste, or where a mistake is expensive and hard to notice, such as price quotes sent to customers without review.

What we build: chatbots, document search, AI-OCR, LINE bots and workflows

Japanese-language chatbots. Customer-facing assistants on your website or in LINE that answer from your own FAQ, product data, opening hours and policies, in Japanese and in the languages your visitors use, such as English, Simplified and Traditional Chinese and Korean. They hand over to a person when a question is outside their scope, and they log every conversation so you can see what customers really ask. In Japan this is usually searched as AIチャットボット 開発; ours are built on your data rather than a generic script.

Search over internal documents. Retrieval-augmented generation (RAG) lets staff ask questions of your manuals, rules of employment, product specifications and past proposals, your 社内文書 (internal documents), and get an answer with links to the exact source passages. The RAG section below explains why doing this properly in Japanese takes more than uploading PDFs to a chatbot.

AI-OCR for FAX, PDF and paper. Orders, delivery notes and invoices are read, the key fields extracted, checked against your customer and product masters and written to kintone, a spreadsheet or your sales system, with uncertain fields flagged for a person.

LINE Official Account bots. Bookings, order status, reminders and FAQ answers through the LINE Messaging API, with rich menus and LINE Login linked to your customer records.

Workflow automation. The connective tissue that makes AI useful: a website enquiry becomes a HubSpot or Salesforce lead, a paid Shopify order becomes a freee invoice, a signed quotation creates a kintone record and a Chatwork or Slack message to the right team. We build these in n8n, Make or Zapier when that suits your team, or as small Python or Node.js services when volume, cost or data rules call for code. Japanese companies often look for this as 業務自動化 外注 (outsourced business automation).

Drafting and summarising. Meeting minutes from recordings or transcripts, first drafts of quotation emails from a price list, triage of a shared inbox into categories with suggested replies, and weekly summaries of support conversations. Staff edit and send; the AI saves them from the blank page.

Forecasting and analysis. Demand forecasts for stock and staffing from your sales history, with seasonal peaks such as Golden Week, Obon and the year-end period built in, delivered as a simple dashboard or a weekly message rather than a model nobody opens. For larger dashboards and internal systems, see our software development page for Japan.

AI rarely lives alone. If the automation needs a new website or form, our web development service covers it; if the pages your assistant draws on should also attract customers from search, see SEO services; and for AI features inside a phone app, see Android and iOS app development.

What AI development and automation cost in Japan in 2026

To set expectations, we compared our prices with what Japanese companies are quoted. The ranges below are taken from 2026 price guides put out by Japanese agencies and comparison sites, including Web幹事, システム幹事 and 発注ラウンジ, which collect typical quotes from Japanese development companies. Treat them as a picture of the market rather than any single vendor's quote; scope moves prices a long way. Our figures are starting prices converted at about ¥155 per US dollar.

Compare like with like. A minimal FAQ chatbot built from a template on a SaaS tool can cost less than our starting price, and if all you need is a handful of fixed answers on your website, that may be the right purchase. Our US$600 (about ¥93,000) starting price is for something different: one mapped workflow connected to your real systems, with validation, logging, a human check where it matters and documentation. The fair comparison is with Japanese business automation projects at ¥200,000 to ¥1,000,000 and with custom AI builds, not with a template bot.

Monthly fees are where costs add up. Hosted chatbot services and AI-powered RPA are often sold by the month, and the fees continue for as long as you use the tool. We do not charge a platform fee. You pay model usage, hosting and any automation platform directly to the provider, and after two free months of maintenance, ongoing support starts from US$120/mo (about ¥19,000/mo). For some teams a SaaS subscription is still the simpler choice; we will set out both totals over two or three years so you can decide.

What our starting prices buy, and why they are lower

Each of our prices is a starting point for a clearly scoped first version. This is what each one typically covers for an automation project in Japan.

• From US$600 (about ¥93,000), AI automation: one well-defined workflow, for example FAX orders read by AI-OCR, validated against your product master and written to kintone with a Chatwork alert, or a Japanese FAQ assistant on LINE that answers from your documents and hands over to staff. Includes process mapping, prompts, integration, a test set built from your real examples, logging and handover notes.
• From US$900 (about ¥140,000), custom AI web app: for automations that need their own screens, such as a review queue where staff approve AI-extracted orders, an internal document search with login and permissions, or a quotation tool with an approval flow.
• From US$600 (about ¥93,000), mobile app with AI features: one app for iPhone and Android, for example field staff photographing delivery notes for AI-OCR, or a customer app with a support assistant. More on the Android and iOS developer page for Japan.
• From US$120/mo (about ¥19,000/mo), maintenance: monitoring, prompt and model updates when providers retire models, fixes when a connected tool changes its API, and small improvements, after two free months following launch.

What raises a quote: several workflows at once, connections to older on-premise systems without an API, handwritten documents, strict data residency requirements, many user roles, and high accuracy targets that need a larger test set. What keeps it down: one process owner who can answer questions quickly, anonymised sample documents at the start, systems with modern APIs, and agreeing to launch with a human check before removing it.

Why our prices are lower than Japanese vendors. Japanese AI development prices carry Japanese costs: offices in Tokyo or Osaka, sales and project management layers, and on larger projects a chain of subcontractors that each add a margin. Our cost base is three remote engineers in India: no rent for an office, no sales staff, no chain of subcontractors, and the people you talk to are the people who build. The trade-offs are real, English communication and no in-person meetings, and the pricing page sets out what each plan includes.

Japanese-language quality: keigo, tone and Japanese business documents

Current large language models read and write Japanese well, far better than the machine translation many companies remember. That does not make their Japanese ready for customers without care. Business Japanese has registers that must match the relationship: 尊敬語 (respectful language about the customer), 謙譲語 (humble language about your own company) and 丁寧語 (polite forms). A model that mixes them, or sounds too casual in a reply to a complaint, reads as wrong to a Japanese customer at once.

We handle this with engineering, not by pretending to be native speakers. Your team writes or approves a short style guide and a set of model answers: how you address customers, whether you use 様 for individuals and 御中 for companies, your standard opening and closing phrases, and words you never use. Prompts, examples and output checks are built from that guide, and a tone review by a native speaker on your side is part of acceptance testing. Where a reply commits the company, a person approves it.

Japanese documents bring their own technical problems. Scanned forms may use vertical writing (縦書き), hanko seals overlap printed text, dates appear in the Japanese era calendar (和暦) alongside or instead of Western years, and numbers switch between full-width (全角) and half-width (半角) characters. Company names appear with and without legal forms such as kabushiki kaisha, sometimes abbreviated. Our extraction step normalises all of this before validation, so 令和8年 and 2026 are treated as the same year and full-width digits become ordinary numbers.

Japanese text also behaves differently for search and cost. There are no spaces between words, so keyword search needs morphological analysis, with tools such as MeCab or Sudachi splitting text into words, and model pricing is based on tokens, whose count for Japanese text differs from English. We measure token usage on your real documents during the first week, so the running-cost estimate is based on your data rather than an English rule of thumb.

Multilingual assistants for inbound visitors need the same care in other languages. Japan received a record 42.68 million international visitors in 2025, according to the Japan National Tourism Organization, with South Korea, mainland China and Taiwan the largest markets. A hotel or attraction assistant therefore often needs Korean, Simplified Chinese and Traditional Chinese as well as English. We build language detection and per-language answer sources, and recommend native review of the fixed answers in each language.

RAG over internal documents, done properly in Japanese

Most internal document assistants disappoint for the same reasons: they find the wrong passage, they answer confidently when the answer is not in the documents, and they show staff documents they should not see. Retrieval-augmented generation works when the retrieval half is engineered as carefully as the language model half. These are the parts we pay most attention to.

• Chunking Japanese text. Splitting by a fixed number of characters cuts sentences and tables in half. We split along the structure of each document, such as chapter and article headings in rules of employment (就業規則), numbered clauses in contracts, rows in specification tables and sentence ends marked by 。, and keep the heading path with each chunk so the model knows where a passage came from.
• Hybrid search. Vector search finds passages with similar meaning; keyword search with Japanese morphological analysis finds exact product codes, part numbers and legal terms. Combining both, then re-ranking, is far more reliable than either alone.
• Permission-aware results. A sales assistant should not quote from HR files. Each document carries the access rights it already has in Google Drive, SharePoint, Box or your file server, and results are filtered by the user's permissions before anything reaches the model.
• Citations every time. Every answer links to its source passages so staff can check it in seconds. If the documents do not contain the answer, the assistant says so rather than guessing.
• Freshness. Documents are re-indexed on a schedule or when they change, and superseded versions are removed, so the assistant never cites last year's price list.
• Evaluation. Before launch we build a test set of real questions with known answers from your staff and measure how often the right passage is found and the answer is correct. We rerun it after every change to prompts, models or chunking.

Scanned PDFs need OCR before indexing, and old Japanese manuals often need clean-up of vertical text and ruby reading aids. Excel files, which hold a great deal of Japanese company knowledge, are converted table by table rather than flattened into text. For large collections we index in stages, starting with the documents staff ask about most, so the assistant is useful in weeks rather than after a long migration.

AI-OCR for FAX orders, invoices and paper documents

FAX remains part of B2B ordering in many Japanese industries, especially wholesale, food distribution, building materials and manufacturing supply chains. Orders arrive in dozens of layouts, one per customer, often with handwritten corrections. Traditional OCR needed a template for each layout. Modern AI-OCR combines text recognition with a language model that understands what a 注文書 (purchase order) or 納品書 (delivery note) contains, so a new layout usually works without a new template.

The pipeline we build has four stages. FAXes arrive as images through an internet FAX service or a multifunction printer that sends to email or a shared folder. The AI-OCR step extracts customer, order date, delivery date, product codes, names, quantities and notes. A validation step, written as plain code rather than AI, checks every field against your customer and product masters, unit rules and credit limits. Finally, clean orders are written to your sales system, kintone or a spreadsheet, and anything uncertain goes to a review screen where a person confirms it with the original image alongside.

Accuracy depends on the input. Clear printed FAXes are read reliably; faint, skewed or heavily handwritten ones are not, and we will not pretend otherwise. The review screen and confidence thresholds exist so that accuracy problems become a few seconds of human checking rather than a wrong delivery. We measure the share of orders that pass without correction on your real samples before you decide to go live.

Invoices work the same way, with a legal angle. Since the invoice system began in October 2023, buyers check that a supplier's invoice carries a registration number, and since January 2024 the Electronic Books Maintenance Act has required transaction records exchanged electronically to be kept in electronic form. An automation can read incoming 請求書 (invoices), extract the registration number, date, amount and counterparty, file them with searchable fields and pass entries to freee or Money Forward Cloud for an accountant to approve. Your tax accountant decides how records must be kept; we build what they specify.

LINE Official Account bots with the Messaging API

LINE is where many Japanese customers already talk to businesses. LY Corporation reports more than 100 million monthly users in Japan, and a LINE公式アカウント (LINE Official Account) connected to the Messaging API turns that channel into an automated front desk.

Typical builds are booking and reservation flows for salons, clinics, restaurants and schools; order status and delivery questions for online stores; appointment reminders that reduce no-shows; and an AI assistant that answers common questions from your own information and passes anything else to staff in the LINE chat screen. Rich menus give customers buttons for the main tasks, LINE Login links a LINE user to your customer record, and small LIFF pages open forms or membership cards inside LINE without a separate app.

Two practical points shape the design. First, LINE Official Account plans are priced by message volume, so automated broadcasts cost money and irritate customers if overused; we favour messages the customer asked for and carefully segmented broadcasts over mass pushes. Second, LINE Notify, which many companies used for internal alerts, ended in March 2025. Internal alerts need a new route, such as the Messaging API itself, Slack, Chatwork or Microsoft Teams, and we migrate old Notify scripts as part of automation work.

The LINE account, channel credentials and customer data stay in your company's name. We work with the Messaging API channel you create, and if an AI model reads customer messages, your privacy notice and the APPI points described below apply. If the LINE flow should link to a new booking page or member area, our website developer page for Japan covers that side.

Connecting kintone, freee, Money Forward, Chatwork, Microsoft 365 and more

An AI step is only useful if its result lands where your team works. In Japan that is usually a mix of domestic and global tools: kintone for business apps, freee or Money Forward Cloud for accounting, Chatwork, Slack or Microsoft Teams for chat, Google Workspace or Microsoft 365 for mail and files, Salesforce or HubSpot for sales, and Shopify for online sales. We use official APIs and webhooks rather than screen-clicking wherever they exist.

Choosing the tool. n8n, Make and Zapier are all good at connecting cloud tools. Zapier and Make are hosted services, quick to start and easy for non-developers to inspect. n8n can also be self-hosted, including on a server in a Tokyo cloud region, which helps when data must stay under your control. Custom Python or Node.js services cost more to build but less to run at high volume, and give full control over validation and logging. We recommend one based on volume, data sensitivity and who will maintain it, and the account is always yours.

Replacing or extending RPA. Many Japanese companies adopted desktop RPA tools such as WinActor or UiPath to click through legacy screens. Those robots break when screens change, and they cannot read unstructured documents. Where a system has an API, an API integration is sturdier; where it does not, we keep the robot for the final entry step and put AI in front of it to read and structure the input. That is usually what people mean when they look for an RPA alternative in Japan.

Human-in-the-loop: quotes, refunds and anything that commits your business

We design every automation around one question: what happens when the AI is wrong? For a meeting summary, a person reads it anyway and an error costs little. For a quotation sent to a customer, a refund, a contract clause or an answer about health or law, an error can cost money or trust, so a person approves before anything leaves the company. These are the controls we use.

• Draft, then approve. The AI drafts the quotation email or the refund reply; staff review, edit and send with one click. Each approval is logged with a name and time.
• Confidence thresholds. Extracted fields below a set confidence, or that fail validation, go to a review queue instead of straight through.
• Hard limits in code. Rules such as maximum discounts, refund limits and credit limits are enforced by ordinary code, not by instructions to the model.
• Clear handover. Customer-facing assistants hand over to a person on request, on complaints and whenever they cannot find the answer in your documents.
• Audit trail. Inputs, outputs, model versions and approvals are logged so you can explain any decision later.

Removing a human check is a decision for later, made on data. After a few weeks in production you can see how often staff change the AI's output. If the answer is almost never for a given category, you might let that category pass automatically; if staff often correct it, the check stays. We report these correction rates so the decision is yours, not ours.

The honest limits of AI automation

AI is useful, not magic, and being clear about its limits saves money. These are the ones we plan around in every project, and the reason our quotes include validation, review screens and test sets rather than just prompts.

• Models make things up. Without retrieval and citations, a language model can produce a fluent answer that is wrong. We ground answers in your documents and design assistants to say when they do not know.
• Accuracy is never 100 percent. AI-OCR on poor scans, handwriting and unusual layouts will make mistakes, which is why validation and review exist.
• Models change. Providers update and retire models, and outputs can shift. A test set and a maintenance plan keep behaviour stable.
• Costs scale with use. API usage grows with volume and document length. We estimate it from your data and add usage alerts.
• Bad processes stay bad. Automating an unclear process produces unclear results faster. Sometimes the right first step is to simplify the process, and we will suggest that.
• Some decisions should stay human. Hiring, credit and medical decisions carry legal and ethical weight; we build tools that support people there, not replace them.

If a project does not make sense once the process is mapped, we will say so before you spend more. A smaller, reliable automation that staff trust is worth more than an ambitious one they work around.

Security: prompt injection, data leakage and access control

An AI automation reads text from outside your company, such as customer emails, FAXes, web forms and documents, and that text can contain instructions. Prompt injection, where input tries to make the model ignore its rules or reveal data, is one of the main risks listed by the OWASP project for large language model applications. We design on the assumption that any input may be hostile.

• Least privilege. Each automation gets its own API keys with the narrowest scopes it needs: read-only where possible, write access only to the specific kintone app or chat channel it uses.
• No secrets in prompts. Credentials, other customers' data and internal notes never go into the model's context unless the task needs them.
• Tools behind checks. When a model can call tools, such as looking up an order, the tool itself enforces who the user is and what they may see, whatever the model asks for.
• Output handling. Model output is treated as untrusted: it is validated before being written to systems and escaped before being shown on web pages.
• Data minimisation. Personal data is masked or removed before a document reaches the model when the task does not need it.
• Logging and retention. Logs record what the system did without storing more personal data than necessary, with retention periods you set.

Our own practices are ordinary but consistent: two-factor authentication on every account, access through accounts you control rather than shared passwords, dummy data in development, and removal of our access when a project ends if you prefer. We do not hold ISO 27001 or similar certifications, and if your procurement rules require them, a certified vendor is the right choice.

Where your data and models run: Japan regions and hosting options

AWS, Google Cloud and Microsoft Azure all operate cloud regions in Tokyo and Osaka, so the application side of an automation, meaning the database, document index, workflow engine and logs, can run in Japan with backups in the other Japanese region.

The model is a separate decision. The major clouds offer managed access to large language models, and some of those models can be used in Japan. Microsoft offers Azure OpenAI deployments in its Japan East region for a number of models, and Amazon Bedrock has a Japan geographic cross-Region inference option that keeps requests for supported models, including some Anthropic Claude models, inside its Tokyo and Osaka regions. Which models are offered in which region changes often, so we check current availability for your chosen provider at the start of the project and record it in the scope. If data must stay in Japan, we choose a model and deployment that meet that requirement, or a smaller open model hosted in your own cloud account, and we explain clearly what that means for quality and cost. Three hosting patterns cover most projects:

• Cloud APIs with Japanese application hosting: the workflow and data in a Tokyo region, with model calls to a provider's API under business terms. Quickest to build and usually cheapest to run.
• Managed models inside your cloud account: model access through your own AWS, Azure or Google Cloud account in a Japanese region where the model is offered, so data handling falls under your existing cloud contract.
• Self-hosted open models: an open-weight model on your own GPU server or cloud instance, for strict requirements. More control and higher fixed costs, and usually weaker than leading models for complex Japanese writing.

Every account, whether cloud, model provider or automation platform, is opened in your company's name, so you control data, billing and access, and you can move to another vendor without asking us.

APPI, AI guidance and other rules for AI automation in Japan

This is practical background, not legal advice. We are engineers; for formal answers, ask a lawyer or adviser qualified in Japan.

Act on the Protection of Personal Information. Under the APPI (個人情報保護法), a business must use personal information within the purposes it has published, keep it secure and supervise contractors that handle it. If an AI step processes customer or employee data, your privacy notice should cover that use. Sending personal data to a model provider or a vendor outside Japan raises further questions that your adviser should assess before go-live.

Access by our team in India. If our engineers in India can see personal data you hold, the APPI treats that as handing data to a third party located in a foreign country. The usual routes are the person's consent, obtained after telling them which country is involved and how its data protection system works, or a recipient that maintains protection measures equivalent to the APPI. India's Digital Personal Data Protection Act, 2023 is being phased in. We plan projects so that we rarely touch real personal data: dummy data in development, masked samples for testing, minimal production access and signed data-handling terms. Your adviser decides which basis your company relies on.

AI Guidelines for Business. In April 2024 METI and the Ministry of Internal Affairs and Communications compiled the AI Guidelines for Business (AI事業者ガイドライン), version 1.0, since updated, integrating three earlier sets of government AI guidelines. They are not legally binding, but they set out principles for companies that develop, provide or use AI, such as safety, fairness, privacy, security, transparency and accountability, with checklists. The controls on this page, including review queues, citations, access limits and audit logs, map naturally onto them, and we document them in English so your team can align them with your internal AI policy.

The AI Promotion Act. Japan's first AI law, the Act on Promotion of Research and Development, and Utilization of Artificial Intelligence-related Technology, was passed by the Diet in May 2025 and came fully into effect in September 2025. It focuses on promoting AI and on a national AI Basic Plan rather than on prohibitions, and it carries no fines, but the government can issue guidance and ask businesses to cooperate. For most companies automating their own work, the practical effect today is to keep an eye on the guidance that follows from it.

Copyright. Article 30-4 of the Copyright Act allows works to be used for purposes such as data analysis that do not involve enjoying their expression, within limits, and the Agency for Cultural Affairs has published a general understanding of how this applies to AI. For an automation the practical questions are narrower: whether you may put a document into a system under its licence, and whether generated text reproduces someone else's work. Your own manuals and records are the simple case; paid databases, purchased reports and third-party publications should be checked against their licences and with your adviser before indexing.

Representations and stealth marketing. A customer-facing assistant speaks for your company. Under the Act against Unjustifiable Premiums and Misleading Representations, claims about quality and price must not mislead, and since October 2023 undisclosed advertising, known as stealth marketing, is regulated too. We restrict assistants to your approved facts and do not let them invent reviews, discounts or comparisons.

External transmission rules. The external transmission rules added to the Telecommunications Business Act in June 2023 oblige many online services to tell users which of their data flows to outside companies via embedded tags and SDKs. A chat widget or analytics script on a covered service may fall under it; a corporate site promoting only your own business usually does not. We list every third-party script so your adviser can decide.

Electronic records and invoices. Automations that handle invoices and receipts should respect the invoice system in force since October 2023 and the Electronic Books Maintenance Act's electronic storage requirement from January 2024, including search by date, amount and counterparty. Regulated sectors such as healthcare, finance and legal services have extra rules on what an assistant may say, which your compliance team should review.

Accessibility. Reasonable accommodation for people with disabilities became mandatory for private businesses in April 2024 under the Act for Eliminating Discrimination against Persons with Disabilities, and JIS X 8341-3, aligned with WCAG, is Japan's web accessibility standard. Chat widgets and review screens we build work with a keyboard and screen readers, and a chat assistant never replaces a phone number or email address for customers who prefer them.

Running costs: model usage, LINE plans and automation platforms

Every AI automation has running costs on top of our fee, and they are billed by the providers directly to you. We estimate them in the quote from your volumes and sample documents, and set usage alerts so there are no surprises. The usual items are:

• Model API usage: charged per token by model providers directly or through AWS, Azure or Google Cloud. The cost depends on the model, document length and volume, so a light workflow and a high-volume document pipeline can differ widely.
• Cloud hosting: servers, databases, document storage and the search index, usually in a Tokyo region.
• Automation platforms: Zapier, Make or n8n cloud plans priced by tasks or executions, or the server cost of self-hosted n8n.
• LINE Official Account plan: a monthly plan based on message volume, paid to LY Corporation.
• OCR and FAX services: internet FAX lines and any dedicated OCR service priced per page.
• SaaS seats: any extra kintone, Slack or CRM users the automation needs.

We design to keep these low: smaller, cheaper models for simple classification, stronger models only for the hard steps, caching for repeated questions, and sending one relevant page rather than a whole document when that is enough. Budgets are planned on current prices, and the usage dashboard shows your spend by workflow so you can see what each automation costs against the hours it saves.

AI automation across Japan: all 8 regions and 47 prefectures

We work remotely with businesses in all 47 prefectures. The engineering is the same everywhere, but what is worth automating differs with the local economy, so these notes describe the processes that typically dominate in each region. None of them is an office location; we have no staff in Japan.

Wherever you are based, automation often works best alongside other improvements. If an assistant or document search should also bring in new customers, pair it with SEO services for Japan so the pages it draws on rank in Google and Yahoo! JAPAN. An overview of every service we offer to Japanese businesses is on the IT services in Japan page.

AI automation in Tokyo and Japan's 20 designated cities

Automation needs follow each city's economy. The table lists Tokyo's 23 special wards and Japan's 20 designated cities, with the processes most often worth automating in each. We serve all of them, and every other city and town, remotely from India.

Outside the big cities, the case for automation is often stronger, not weaker. Regional capitals like Toyama, Kochi, Oita, Morioka and Naha, and the many smaller towns where Japan's manufacturers and food producers operate, face the sharpest hiring difficulties. A modest automation that saves a few hours a day can matter more to a twenty-person company in a regional city than to a large Tokyo office.

Working hours in Japan time

India Standard Time is three and a half hours behind Japan Standard Time all year, since neither country changes its clocks. We are available from 9 am to 9 pm IST, which is 12:30 pm to 12:30 am JST, seven days a week on WhatsApp, and a morning call around 10 am JST can be booked in advance.

For automation work the overlap is useful in a specific way. You can test a workflow with the morning's real FAXes or emails, send us the cases it got wrong before lunch, and see fixes in the staging environment the same evening. For go-live, we schedule the switch-over and first-day monitoring in your afternoon, when we are online and your staff are still at their desks.

Hiring an offshore AI automation engineer from Japan: checklist and red flags

Many AI projects in Japan stall after the proof of concept: the demo impressed, but nothing reached daily use. The causes are predictable, and a short checklist helps whichever vendor you choose, including us.

• Ask who builds it. Get the names of the engineers, and ask whether any part will be subcontracted.
• Start from a process, not a demo. A good vendor asks about volumes, time per item and exceptions before showing you a chatbot.
• Demand a test set. Accuracy should be measured on your real examples, with numbers you can check, before go-live.
• Check who owns what. Model accounts, API keys, the automation platform, cloud, code, prompts and workflows should all be yours.
• Ask about data flows. Which provider receives your data, in which region, under what terms, and who at the vendor can see it.
• Ask what happens when models change. Providers retire models; the vendor should explain how prompts and tests are maintained.
• Agree the language bridge. If your team works in Japanese, name the bilingual person who reviews the scope and the tone of answers.
• Stage payments. An advance, approval of the workflow design or prototype, and the balance before go-live is a fair structure.

Red flags: promises of 100 percent accuracy or full automation from day one; a vendor who wants the model or cloud account in its own name; no plan for human review; a "proprietary AI" that turns out to be an unexplained wrapper around someone else's model; reluctance to show the prompts and workflows you are paying for; and client logos that cannot be verified.

When to choose a Japanese vendor instead: if you need Japanese-language workshops, on-site interviews with staff, a vendor who writes and owns the Japanese answer content, certified security management, or a subsidy-eligible registered provider. Otherwise, contact us with one process and a few anonymised samples, and we will tell you honestly whether it is worth automating.