The operator-facing program. Tools for the deep-tech founder running every non-engineering function on AI. An ontology for the industrial domains those founders sell into. A contract substrate for licensing the operational expertise they produce.
Three artifacts under one program. DeepTechTools is the operator hub: free library plus a live tool catalog covering IP, sales, data, fundraising, and science. ProbSpec is the typed failure ontology for brownfield industrial interop — the working example of what an ontologized domain looks like. LADDER is the standard clearance contract for licensing operational expertise as AI training data. The rest of this page covers the operator tools at deeptechtools.com, the ontology at probspec.com, and how to engage Ashiba on the data work specifically.
Companion site deeptechtools.com hosts the operator-facing toolset. Free library is live: seven downloadable artifacts including buyer maps for humanoid robotics, funder maps for deep-tech VCs, the deep-tech operator's reading list, and primers on patent reading, vendor selection, and operational-data classification. Five tool categories ship through 2026:
Replace $5K-15K associate-hour searches and $25K-100K FTO opinions with structured AI-assisted output that produces the brief your patent attorney can act on. Patent Landscape Generator MVP ships May 2026.
Find the right buyer at the right account faster than your competitors, with the right framing for the vertical. Vertical Buyer Maps ships first; the humanoid robotics map is the working sample in the free library.
Access training data and market data your competitors don't have, on terms that don't get you sued. Includes the licensing rate card the AI-data market does not publish openly.
Run cap table, fundraise, board, and corporate operations at the speed of decision rather than the speed of consultants. Stage- and sector-specific templates trained on what actually closes deep-tech rounds.
Literature review, hypothesis generation, and experimental design at the speed of conversation. Domain-specific templates that match what your sector's reviewers and standards bodies actually expect.
Visit DeepTechTools → · Monday briefing →
For the deep-tech founder asking "what does ontologizing my domain actually look like?", ProbSpec is the answer in production form. A typed failure ontology for brownfield industrial interop — twelve named failure classes (link path, serial config, addressing, register model, polling pressure, scaling/units, write path, physical layer, firmware compatibility, hidden vendor behavior, batch drift, false-restore), each with intervention sequences and observed repeat priors. Plus a supplier-reliability schema and recovery playbooks indexed by problem class.
The methodology generalizes. The artifact is licensable to system integrators, OEM support teams, industrial maintenance operators, and consultancies. probspec.com →
Use AI. Make friends on standards bodies. Ontologize your domain. Protect your moats.
Before the machines come.
The next decade decides whether your domain's vocabulary gets fixed in standards bodies you helped shape — or in standards bodies you ignored. Whether your operational expertise becomes a licensable asset — or gets absorbed into a training corpus you don't get paid for. Whether the AI built on your work answers to your ontology — or to someone else's. Ontological moats compound. Standards bodies are where they form. Show up.
Each imperative maps to one Ashiba program:
The rest of this page covers the LADDER engagement specifically — how Ashiba helps deep-tech operators turn workflow data into licensable AI training assets without giving the buyer everything for one vague pilot fee.
If you are running test data, design iterations, lab notebooks, instrument logs, calibration records, or applied-research workflows — and you have outcomes — that data may be worth more as a packaged asset than as a closed competitive advantage. SBIR/STTR data has a 20-year protection period. Most teams do not realize they can license selectively from inside that protection.
Failed protocols, modified methods, replication notes, instrument quirks. The judgment that does not make it into the published paper is exactly the data frontier labs are paying for. We help convert that into rights-clean evals and recurring workflow environments without compromising publication or sponsor obligations.
Machine shops, calibration labs, repair operators, regional integrators, specialty test houses, field-service teams, manufacturing and robotics startups, HVAC / electrical / plumbing specialists, compliance and audit operators. The work you do every day — diagnosis, quote, repair, inspection, recovery — is the next training corpus. Either you license it on your terms or someone trains on it without paying you.
Patent prosecutors, technical compliance specialists, vertical-software operators, anyone whose work pattern is input → expert decision → reasoning → outcome on a repeated basis. If you can score the answer afterward, you may have an asset.
The best first conversation is concrete. If you are seriously considering whether you have something, bring this:
From there we can answer the only question that matters at the start:
Is this just operational exhaust, or is it a licensable AI data asset?
We score the workflow against the asset criteria: real workflow, outcome signal, expert decision, hidden state, edge cases, scorability, proprietary access, rights clarity, de-identification path, buyer relevance. Output: a written assessment that tells you whether you have a paid expert engagement, a pilot-ready asset, a strategic licensing position, or operational exhaust that should stay internal. We will tell you no when the answer is no.
Before any data leaves the building, we map who can authorize what. Customer contracts, employer agreements, sponsor terms, SBIR markings, IRB constraints. Output: a rights map that says clearly what is licensable, what needs renegotiation, what cannot leave the operator's control, and what should stay reserved as Schedule B know-how.
Raw operational data is treated as contaminated by default. We clean it through a documented process so the buyer can rely on what they receive. Output: clean skill episodes — initial state, available information, expert observations, decision path, actions, outcome, scoring rule, with sensitive material removed and a documented log of what changed.
The first paid engagement most operators run is a 25–50 task pilot. We design the task structure, write rubrics, build the scoring approach, and define the use rights so the buyer cannot quietly turn an evaluation pilot into a training license. Indicative pilot pricing: $25K–$100K depending on domain and rights scope.
Each cleared asset gets a LADDER Passport: a buyer-readable clearance record covering provenance, authority, allowed use, what was removed, what was reserved, what royalty class applies. License terms separate the seven rights buyers usually bundle. Different rights, different prices. How LADDER works →
You do not need to sell directly to OpenAI on day one. We help match the asset to the buyer category most likely to convert quickly: frontier lab pilot evals, RL environment vendors, vertical SaaS benchmarks, enterprise AI guardrails, compliance/audit risk taxonomies. The first buyer is rarely the biggest; it is the one who values your edge cases.
If you have a workflow you think might qualify, write to cv@ashibaresearch.com with the five items above (workflow, ten cases, outcome signal, rights restrictions, suspected buyer).
We respond to written problem statements faster than to "quick call" requests. The five-line note is enough — that is the form of the work.
How LADDER works → · Operational data manifesto → · State of the data market →