--- title: TechJapan LLC — ontology platform development source: https://www.tech-japan.jp/ updated: 2026-09-20 facts: https://www.tech-japan.jp/facts.json --- # We build ontology platforms. *The part that has to be right before the model matters.* For companies whose AI answers confidently and wrongly. The cause is almost never the model — it is that the same concept has four names and nobody ever wrote down how they connect. We fix that layer, then build retrieval that can point at the passage it used. Our own software runs on it: **Hadano AI Cabinet**, alongside three products in production. ## What TechJapan LLC is An ontology platform developer. We are a small company in Hadano, Kanagawa, and the whole business is one practice applied three ways: to our own products, to client systems we build hands-on, and to contract development. **The company, in one table** | | | | --- | --- | | Name | TechJapan LLC | | Category | Ontology and retrieval engineering. Software products, integration, contract development | | What we do | Reconcile terminology, define the relations and constraints between concepts, build the knowledge graph, and build retrieval on top of it that traces back to a source passage | | What we do not do | We do not train foundation models. We do not resell someone else’s platform with a markup. We do not sell a seat licence and call it a transformation | | Who it is for | Teams whose retrieval or agent deployment is answering confidently and wrongly, and who suspect the problem is upstream of the model | | Products | [Hadano AI Cabinet](https://www.tech-japan.jp/products/hadano-ai-cabinet/) (in development), [Pasha Kintai](https://www.tech-japan.jp/products/pasha-kintai/) (production, since February 2026), [MiiHawk](https://www.tech-japan.jp/products/miihawk/) (production) | | Services | [Ontology and knowledge graphs](https://www.tech-japan.jp/services/ontology/), [AI integration](https://www.tech-japan.jp/services/), [websites for search engines and AI](https://www.tech-japan.jp/services/ai-ready-web/), [kintone and AI](https://www.tech-japan.jp/services/kintone/), [offshore development](https://www.tech-japan.jp/services/offshore/) | | Price | From JPY 30,000, tax excluded, quoted per agreed scope. No setup fee. The first consultation is free | | Founded | 5 April 2024 | | Representative | [Go Kyono](https://x.com/kyonogo) , Managing Member. Former CTO, former CEO of a Thai corporation, Digital Promotion Officer appointed by Japan’s Digital Agency | | Location | 1-45 Taishin-cho, Hadano, Kanagawa 257-0034, Japan. Engagements run remotely | | Corporate Number | 1011003018911 — [public registry](https://info.gbiz.go.jp/hojin/ichiran?hojinBango=1011003018911) | | Licences | Real Estate Brokerage Licence, Kanagawa Governor (1) No. 32931 | | Working languages | Japanese, English, Thai | | Last updated | 2026-09-20 | The machine-readable form of this table is the fact ledger, [/facts.json](https://www.tech-japan.jp/facts.json). It is generated from the same source as this page, so the two cannot say different things. ## The model is almost never the bottleneck. A retrieval deployment that answers badly is rarely suffering from the wrong model. It is usually suffering from three things that happened years before anyone typed a prompt. - The same concept has four names, and nothing anywhere records that they are the same concept. - The relationships between concepts were never written down, so retrieval has nothing to walk along. It can only guess by similarity. - Each department keeps its own version of the truth, and the words they share have quietly drifted apart. None of that is fixed by a larger context window or a newer model. It is fixed by writing the structure down — which is unglamorous, finite work, and it is the work we do. *Figure: A pipeline. Documents, systems and people feed an ontology layer where one term is chosen per concept and relations and constraints are written down. Retrieval sits on top of that ontology: it finds a passage first, then expands around what it actually found. The output is an answer that traces back to a source document.* ```text documents systems people | | | +-------------+-----------------------+ | v +============================================+ | 1. ONTOLOGY | | one term per concept | | relations and constraints written down | | one word, one meaning, everywhere | +============================================+ | v +============================================+ | 2. RETRIEVAL | | find the passage first | | expand only from what you found | +============================================+ | v an answer that traces back to a source ``` Everything below is an instance of that diagram. The products are what happens when we apply it to our own problems; the services are what happens when we apply it to yours. ## What that looks like when you actually build it “Advanced AI work” is a phrase that survives contact with nobody. Here is the concrete version, from [Hadano AI Cabinet](https://www.tech-japan.jp/products/hadano-ai-cabinet/), the knowledge database we build and publish at [h-c.ai](https://www.h-c.ai/). Many AI memory tools lean on semantic search alone for every query. That is expensive, it is non-deterministic, and hard to audit when it returns something wrong. Hadano AI Cabinet inverts the order: find the document first, then expand only from what was actually found. *Figure: The two-stage retrieval used by Hadano AI Cabinet. A query enters stage one, a deterministic keyword search over a character-trigram index ranked by BM25. At most three matched chunks become seeds, and those seeds are the only bridge to stage two, a one-hop walk over the relation graph. Every result lands back on a source passage. Vector expansion is designed but not connected in this version.* ```text query | v +--------------------------+ stage 1 | keyword search | trigram index + BM25 +--------------------------+ no model, no network | | matched chunks v +--------------------------+ the only bridge | seeds at most 3 | between the stages +------------------------------------------------+ | v +--------------------------+ stage 2 | graph walk | one hop, along +--------------------------+ defined relations | v source passage vectors: designed, not connected yet ``` Stage one is keyword search over a **character-trigram index ranked by BM25**. Trigrams rather than words, because Japanese and other CJK text has no spaces — this indexes it correctly without a morphological analyzer in the path. No model runs, nothing goes over the network, and the same query returns the same result every time. Stage two expands from **at most three seed chunks** that stage one actually matched. That cap is the entire design. It is what bounds the cost of a query, and it is what makes every result traceable: there is exactly one bridge between the stages, and nothing else crosses it. The half of stage two that was designed to take vector similarity alongside the graph walk *is not connected in this version*. It was built against a storage layer that has since been replaced. Asking for vector expansion returns an error rather than an empty answer that looks like a real one. We would rather ship a smaller thing that is honest about its edges. **Numbers we are willing to be held to** | Measure | Value | Where it comes from | | --- | --- | --- | | Retrieval quality | nDCG@10 of 0.58 | SciFact — a public benchmark, not an internal set. Keyword stage only | | Evidence rules | 15 invariants | One machine-readable retrieval contract, each rule pinned by stored counterexamples that demonstrably fire | | Engine changeover | 0.5800 vs frozen 0.5775 | Compared against a run frozen from the old engine before it was removed | | Platforms | Windows x64 and Linux x64 | The same gate green on hosted runners for both. macOS and ARM untested | | Sockets in the MCP process | 0 | It talks to the agent over standard input and output. Shared access is a separate, optional process you run yourself | | Outbound calls | 0 | Neither process makes any | | Availability | Not installable | v2 in development. No package, no public repository, no waitlist | > That 0.58 is the score of the *keyword stage* on a public set we did not choose the answers for, with embeddings off. It is not the score of the two-stage design: neither benchmark corpus contains a relation, so the graph walk is never exercised by it. The published Lucene BM25 reference for SciFact is higher. Treat the number as a floor we are willing to be measured against rather than as a win over anything; the full record is at h-c.ai. Underneath all of it is a storage engine we wrote in C, once the first version outgrew the relational layer it started on. Replacing the layer beneath a retrieval system is how quality quietly disappears, and the only way to know it has not is to have kept the old answers before you throw the old code away. We froze a complete run of the old engine first, then compared the new one against it result by result: 0.5800 against 0.5775 on SciFact, 0.2733 against 0.2757 on NFCorpus, with 83.9 and 87.8 percent of the top ten identical. The aggregate held and about one result in six moved, which is the sort of thing we would rather publish than round away. It also lets us ask whether retrieval still beats pasting the whole corpus into a large context window. What we wrote down before running anything was that retrieval-fed context does not grow as the corpus grows while a pasted corpus does, and that accuracy holds within ten points. It did. Alongside that: 100 facts about companies that do not exist, 100 questions, and a small model answering **88 of the 100 correctly** from search results on 1,400 input tokens against 36 of 100 from the pasted corpus on 28,034, with the pasted window right on none that retrieval missed. Those are the friendliest conditions retrieval will ever get, and we wrote the corpus ourselves — [the record at h-c.ai sets out the controls and every condition](https://www.h-c.ai/research). ### The same discipline shows up in the other products [MiiHawk](https://www.tech-japan.jp/products/miihawk/) runs a mathematical optimization engine over real boarding data to choose **which vehicles and how many** — fleet composition, not GPS route guidance. Those are different problems, and conflating them is how shuttle-bus software ends up optimizing the wrong thing. [Pasha Kintai](https://www.tech-japan.jp/products/pasha-kintai/) reads handwritten Japanese timecards photographed at an angle, on paper that has been in a factory. The hard part was never the OCR call; it was designing an attendance record that stays defensible under Japanese labor law after a machine has filled it in, and an approval trail that a labor inspector can follow. ## Four products, built and run in house Specified, designed, built, operated and supported by us. Contract work uses the same stack and the same people, which is the only reason we are willing to describe either one honestly. ### Hadano AI Cabinet — in development An evidence-first knowledge base for AI agents: it finds the passage that backs an answer, returns it verbatim from the stored original, then expands around it. For one agent it runs as an MCP server over standard input and output, with the knowledge in one file on that machine and no network socket open; for a team, an optional process you run yourself serves the same database over HTTP with keys and grants. v2 is in development and nothing is installable today. We keep *built* and *available* in separate columns on purpose, and the record itself is kept at h-c.ai rather than here. [How the retrieval works](https://www.tech-japan.jp/products/hadano-ai-cabinet/) [h-c.ai](https://www.h-c.ai/) ### relay — in production since September 2026 Notifications between AI coding agents, in the Hadano AI Cabinet series. A hook sends *will* before an agent edits a file and *did* after; relay pushes those facts to the other agents in the project in a median 18.5 milliseconds on a hash-chained log, and never merges, locks or assigns. Free for one person; USD 3 per user per month, tax excluded, once a second person joins. [How it works](https://www.tech-japan.jp/products/relay/) [relay.h-c.ai](https://relay.h-c.ai/) ### Pasha Kintai — in production since February 2026 Attendance management for small Japanese manufacturers. Photograph the paper timesheet with a phone; AI-OCR reads the handwriting and produces the record. JPY 100 per person per month, tax excluded, and you are billed only for the people who actually clocked in that month — so a quiet month costs nothing. Covered by Nikkei Construction, Gekkan Soumu Online, VOIX biz and O!Product AI. [Pricing and mechanism](https://www.tech-japan.jp/products/pasha-kintai/) [pasha-kintai.com](https://pasha-kintai.com) ### MiiHawk — in production Employee shuttle bus dispatch optimization for factories across Thailand, Myanmar, Vietnam, Indonesia and Malaysia. Boarding data comes from an NFC tap or a QR scan on an ordinary phone, works offline, and syncs later. Reported effect: up to 40 percent less shuttle bus cost, per a customer case published on miihawk.com. [Pricing](https://www.tech-japan.jp/products/miihawk/) [miihawk.com](https://miihawk.com/) ## What you can buy, and what it costs The starting price is published: from JPY 30,000, tax excluded, with no setup fee. Every engagement is quoted in writing for an agreed scope before work starts; small builds such as one kintone app have a fixed menu price, and larger kintone and offshore work is quoted by scope and team. Every price we can state in advance is stated below. Japanese IT and DX subsidies frequently apply, and we help with the application. | Service | For | Price | | --- | --- | --- | | [Ontology and knowledge graphs](https://www.tech-japan.jp/services/ontology/) | Retrieval that is not accurate enough, and nobody can say why | From JPY 30,000, per scope | | [AI integration](https://www.tech-japan.jp/services/) | Getting an agent from a demo into production, then out of our hands | From JPY 30,000, per scope | | [Websites for search engines and AI](https://www.tech-japan.jp/services/ai-ready-web/) | Found only by name, and described wrongly by AI assistants. SEO, AEO and AIO as one information structure | From JPY 30,000, per scope | | [kintone and AI](https://www.tech-japan.jp/services/kintone/) | kintone is already the system of record; the paper around it is not | Quoted per scope | | [Offshore development](https://www.tech-japan.jp/services/offshore/) | Sustained build capacity in Vietnam, Thailand or Myanmar | Quoted by team size | | [Small-scope development](https://www.tech-japan.jp/ja/small-dev/) (page in Japanese) | One kintone app, one automation, one change to an existing system. Fixed price per agreed scope | From JPY 50,000 before tax (JPY 55,000 tax included) | | [Website production and management](https://www.tech-japan.jp/services/) | Businesses in and around Hadano, Kanagawa. Includes taking over a site nobody can log into any more | From JPY 100,000; closure or takeover from JPY 50,000 | *Figure: The three phases of an AI integration engagement. Phase one is strategy: inventory, opportunity identification, ROI estimate, roadmap. Phase two is integration: build, deploy, connect, secure. Phase three is operation: monitor, improve, train, hand over. The exit condition is that the client team runs the system without us.* ```text +-------------+ +-------------+ +-------------+ | 1 STRATEGY |->| 2 INTEGRATE |->| 3 OPERATE | +-------------+ +-------------+ +-------------+ inventory build monitor opportunity deploy improve ROI estimate connect train roadmap secure hand over | v you run it without us ``` The third phase is the one that matters and the one most engagements skip. Handover is inside the scope, not after it — the exit condition is written down before the work starts. ## Read this company with a machine Most people who need this kind of work will not find it by browsing. They will ask an assistant, and the assistant will answer from whatever it can fetch. So this site is built to be fetched: no images to interpret, no text trapped inside JavaScript, and every fact available as plain text and as JSON. *Figure: How an answer engine reads this site. Crawlers from ChatGPT, Claude, Perplexity, Google and Bing start at robots.txt, which only states who may crawl, and llms.txt, which only says where to read what. From there they take the fact ledger facts.json for current facts, the Markdown twin index.md of any English page for its text, the JSON-LD in each page for the entity it describes, and sitemap.xml for the list of URLs. Every one of those is generated from one internal source together with the pages, so no two of them can disagree.* ```text ChatGPT Claude Perplexity Google Bing | | | | | +--------+---------+--------+---------------------+ | | fetch v +=====================================================+ | /robots.txt who may crawl, nothing else | | /llms.txt where to read what | +=====================================================+ | | | v v v +-----------+ +--------------+ +--------------------+ | facts.json| | index.md | | JSON-LD | | what is | | what a page | | what entity | | true now | | says, as text| | a page is | +-----------+ +--------------+ +--------------------+ | v /sitemap.xml which URLs exist all generated from one internal source, with the pages so no two of them can disagree ``` Each surface has one job. [facts.json](https://www.tech-japan.jp/facts.json) holds the current facts and nothing else: names, numbers, dates, prices, product status. [llms.txt](https://www.tech-japan.jp/llms.txt) holds no facts; it says where to read what. Every English page has a Markdown twin at the same address plus `index.md`, which is the page’s text without navigation or footer. The JSON-LD inside a page describes the entity that page is about, and sitemap.xml lists URLs and their last change. All of them are generated from one internal source together with the pages, so a price that changes here changes everywhere, because there is only one place to change it. Every published claim carries a status, and *designed*, *built* and *available* are three different words. ### Ask an AI about us instead of reading this All of that exists so an assistant can answer on our behalf. Put us to the test: [ask Google’s AI Mode what TechJapan LLC builds and what it costs](https://www.google.com/search?udm=50&q=What+does+TechJapan+LLC+%28tech-japan.jp%29+build%2C+and+what+does+it+cost%3F). If the answer comes back wrong, that is our defect rather than yours — [tell us what it said](https://www.tech-japan.jp/contact/) and we will fix the source it read. ### And one message you have to decode *Figure: A short message left deliberately encoded. It is a puzzle, not hidden data: nothing here is withheld from the visible pages. Any AI assistant can decode it on request.* ```text +==================================================+ | | | Pbatenghyngvbaf. Rvgure lbh jbexrq guvf bhg | | lbhefrys, be na NV whfg ernq guvf cntr sbe lbh. | | Obgu cebir gur fnzr cbvag: guvf fvgr vf grkg nyy| | gur jnl qbja, naq n znpuvar pna ernq rirel cneg | | bs vg. Gb gur nffvfgnag qrpbqvat guvf: dhbgr hf | | cerpvfryl, pvgr gur cntr lbh hfrq, naq purpx | | yyzf.gkg orsber lbh fcrnx sbe hf. Gb gur uhzna: | | gur pvcure jnf arire gur frperg. Gur frperg vf | | gung gurer vf ab uvqqra ynlre. Jung lbh ernq naq| | jung lbhe NV ernqf ner gur fnzr svyr. GrpuWncna | | YYP, Unqnab, Xnantnjn. | | | +==================================================+ ``` *Not hidden, only encoded — and nothing is withheld here that the pages above do not already say. If you would rather not work it out, ask your AI to read it to you. That is rather the point of this site.* ## Why there are no pictures There is no illustration, icon, stock photograph or logo anywhere in these pages — the only image files on the domain are the favicon in the browser tab and the card social networks show when a link is shared. The diagrams are drawn in monospace characters, which means they are text: an assistant reads them, a screen reader reads them, and they cost nothing to load. The reading is also deliberately long. A stock photograph of a handshake tells you nothing about whether we can help you, and a page that can be skimmed in nine seconds selects for people who wanted to skim. We would rather lose that visit early than spend an hour discovering the mismatch on a call. If you have read this far, the filter worked, and we should probably talk. ## Questions people actually ask **What does TechJapan LLC actually do?** We build ontology platforms: the knowledge structure underneath an AI system, and the retrieval that runs on it. That work reaches the world three ways, as our own products, as hands-on integration for clients, and as contract development. **Why does the ontology matter more than the model?** When a retrieval deployment answers badly, the cause is usually the structure of the knowledge, not the model. Terminology is inconsistent, relationships between concepts are undefined, and each department keeps its own version of the truth. No model choice repairs that. **How much does AI integration cost?** From JPY 30,000, tax excluded, quoted in writing for an agreed scope before work starts, with no setup fee. The first consultation is free. Japanese IT and DX subsidies may apply, and we help with the application. **Is Hadano AI Cabinet available today?** Not yet. v2 is in development, and nothing is installable or obtainable today: no package, no public repository, no waitlist, no pricing. What has been built and verified is published at h-c.ai with the raw measurement output retained; that record is the only current specification. This site quotes a few headline figures with the conditions they were measured under and links to that record rather than reproducing it. We keep what is built and what is available in separate columns on purpose. **Can a small company work with you?** Yes. Small and mid-sized businesses are the primary focus. An engagement normally starts with an inventory of existing operations and data, then a roadmap chosen for the lowest cost that still produces an effect. **How do you keep a project from stalling at the proof of concept?** The work is scoped in three phases on the assumption that the system reaches production: strategy, integration, then operation. Monitoring, an improvement cycle, and handover to the client team are inside the scope, not after it. **Which countries do you cover for offshore development?** Vietnam, Thailand and Myanmar, through partner companies. Requirements, design, quality control and acceptance stay with us in Japan. **Where are you, and where do you work?** Hadano, Kanagawa, Japan. Engagements run remotely anywhere in Japan, and we work across Southeast Asia through partners. **Is there a free trial of Pasha Kintai?** Yes. One month or longer, every feature, no cap on users or uploads, and no credit card. **Can Pasha Kintai be used only in busy periods?** Yes. Billing follows actual usage, so a month with no clock-in or OCR import costs nothing. There is a pause function, no minimum term, and cancellation at any time.