Same data, same ceiling
Every foundation model trains on the same public corpus. Performance converges. The differentiation that remains lives in domain-specific knowledge — and that was never published.
Domain Layer
The bridge between industry and intelligence.
Every model trains on the same internet. The ones that work in the real world train on something more — domain expertise from the institutions that hold it. Looni is the domain layer that brings enterprise knowledge into AI. Licensed at the source. Governed end to end. Built to compound.
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The Gap
Models converge. Domains diverge.
Every lab reads the same web and buys the same chips. Models are converging. But the work they need to do — settling a claim, tolerating a part, reading a scan — diverges by industry, by institution, by workflow. Closing that gap takes domain knowledge that no public dataset carries.
Domain expertise is operational, not searchable
How a radiologist reads a scan, how an engineer tolerates a part, how an underwriter prices a risk — that reasoning exists inside institutional workflows, not on the internet.
Compliance blocks the path
Healthcare, finance, manufacturing — regulated industries can’t move knowledge without consent, de-identification, and a governance trail. Willingness alone isn’t enough.
No infrastructure to bridge
Even institutions ready to participate have no pipeline to package, de-identify, and deliver their expertise to AI teams. The bridge doesn’t exist yet.
The Domain Layer
The infrastructure between institutions and AI.
We work with enterprises to operationalize their domain knowledge for AI — and with AI teams to put that knowledge into their pipeline. Licensed at the source. De-identified inside the institution’s walls. Governed end to end.
Clinical knowledge, ready for your pipeline.
Clinical workflows, imaging, diagnostic reasoning, patient pathways. De-identified under HIPAA and DPDP before anything moves. Every record reviewed by practicing clinicians — not crowd workers. Delivered in your format, traceable to its source.
Design intelligence from teams that build.
CAD files, tolerance specifications, simulation data, production records, quality inspection logs. From engineering teams that design and manufacture real products — not synthetic approximations. Licensed directly from the source.
The by-product of real work.
Tickets, CRM records, procurement workflows, contact centre transcripts, codebases, repos, CI pipelines, PRs, and internal communications. Operational data from real organisations — structured, de-identified, and ready for training. The knowledge that sits in enterprise systems, not on the internet.
Domain accuracy, verified by people who do the job.
Credentialed specialists — clinicians, engineers, legal professionals, financial analysts — who review, validate, and annotate every dataset. The model learns from people who do the work it’s trying to do. Not crowd workers. Not contractors.
The trail both sides need.
Full lineage on every dataset: licensing terms, de-identification method, consent records, annotator credentials, version history. Enterprises see exactly where their knowledge went. AI teams show exactly where their training data came from. The documentation procurement, compliance, and regulators need to say yes.
From institution to intelligence — one path.
A single engagement. Two sides connected. Here’s how the bridge works.
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Step 01
Source
An institution holds domain knowledge — clinical records, CAD files, operational workflows, codebases. Valuable to AI. Locked behind compliance, consent, and infrastructure gaps.
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Step 02
License
Looni works with the institution to define licensing terms. Scope, exclusivity, duration, permitted use — set by the data owner, enforced contractually. Nothing moves without a signed agreement.
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Step 03
De-identify
All de-identification happens inside the institution’s environment. Direct identifiers removed. Quasi-identifiers flagged. Residual risk reviewed by a person. Spoken identifiers cut from audio. Nothing leaves the walls until it’s clean.
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Step 04
Expert review
Credentialed domain specialists — clinicians, engineers, analysts — review and validate every dataset. Annotations are tied to the reviewer’s credentials. Quality is verified by people who do the job, not people who label for a living.
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Step 05
Deliver
Datasets are packaged in the AI team’s format and delivered with full provenance — licensing terms, de-identification method, annotator credentials, version history. Plug directly into fine-tuning, evaluation, or alignment pipelines.
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Step 06
Compound
The relationship doesn’t end at delivery. As models improve and requirements evolve, new data flows through the same governed path. The institution earns ongoing value. The AI team gets continuously updated domain knowledge. The bridge compounds.
Who We Work With
Five ways to work with Looni
AI labs & model companies
Licensed domain data and expert validation for the models you train. Healthcare, engineering, enterprise ops — delivered in your format, governed end to end.
For AI teamsEnterprises building AI
Move from a first use case to a governed deployment — with domain data, expert review, and compliance infrastructure that your procurement team can approve.
For enterprisesData owners
Hospitals, engineering firms, contact centres, and enterprises — license the knowledge your operations already produce. We handle de-identification, packaging, and delivery. You set the scope, terms, and pricing.
License your dataData collectors
Capture first-person data — egocentric video, field recordings, sensor logs — under clear consent and fair, written terms. Contribute to datasets that train real-world AI systems.
Contribute dataDomain experts
Apply your expertise to validate, annotate, and review datasets. Clinicians, engineers, legal professionals, financial analysts — your domain knowledge is the quality layer. Compensated. Credentialed. Credited.
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Build with Looni.
The bridge between industry and intelligence is open for partners. Bring your models or your domain — and let the value compound.
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