Earlier program. Ashiba does not currently market this program. See current Ashiba work →
Ashiba Deep Tech
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 sat under this earlier program. DeepTechTools was the operator hub. ProbSpec explored industrial specification failures. LADDER explored rights for operational data.
DeepTechTools — operator tools running through 2026
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 →
ProbSpec — the working ontology example
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 →
If you are building deep tech, here is the message
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:
- Use AI — for everything that isn't engineering, at the same intensity you use it for engineering. DeepTechTools is the operator hub.
- Make friends on standards bodies — get your domain's failure modes named where buyers, regulators, and incumbents can review them.
- Ontologize your domain — name your failure modes, supplier categories, recovery paths, scoring rules. ProbSpec is the working example for brownfield industrial interop.
- Protect your work — state the allowed use before sharing operational records. LADDER records the earlier approach.
The data engagement track — package operational expertise as a LADDER-cleared AI training asset
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.
Who the data engagement is for
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.
What to bring
The best first conversation is concrete. If you are seriously considering whether you have something, bring this:
- One workflow. Pick the most representative one — not the most impressive.
- Ten cases. Real instances of that workflow, with whatever data you have.
- The outcome signal. How you (or your customer, or your test, or your downstream system) knows whether the answer was right.
- Any known rights restrictions. Customer contracts, employee agreements, sponsor terms, SBIR markings, IRB constraints, MSA clauses. A rough sense is enough at this stage.
- The buyer you suspect would care. Frontier lab, vertical AI startup, industrial AI team, evaluation vendor, vertical SaaS, insurer, compliance operator. Even a guess.
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?
The engagement track
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.
The earlier design used a buyer-readable record for provenance, authority, allowed use, removals, reserved rights, and payment terms. How LADDER worked →
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.
What we will not do
- Sell raw data with vague terms before you understand what you own.
- Let buyers collapse seven rights into one pilot fee.
- Hand a buyer free exclusivity.
- Run an unpaid evaluation that is actually a training license in disguise.
- Take an engagement we do not believe will produce a real asset.
Next step
If you have a workflow you think might qualify, write to [email protected] 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 worked → · Operational data manifesto → · State of the data market →