Product · Jungroo AI Studio
Private AI, trained on your own work.
Jungroo AI Studio is our domain-adaptive language model studio. It turns your documents, such as regulations, policies, case files and contracts, into a custom language model that runs inside your infrastructure. One interface covers the whole model lifecycle, with no notebooks or command-line pipelines.
No data leaves your environment at any step.
Why a domain model
For organisations that cannot send data to public AI services.
Data-sovereignty, privacy and regulatory rules keep many organisations away from cloud AI APIs. A model trained on your own documents and run on your own hardware removes that barrier, and is built for the work you actually do.
Fully on-premise
The model runs inside your perimeter, air-gapped if required. Documents and queries never reach an external AI service.
You pay for the server
No per-query or per-seat AI fees. As a reference, a 30-billion-parameter-class model in FP8 runs on a single 80 GB GPU server.
Traceable output
Answers cite the documents in your verified corpus, so every reference can be checked. Your team stays the decision-maker.
The model lifecycle
From raw documents to a deployed model, in one studio.
We set the studio up inside your environment and run it with your team.
Bring your corpus
Regulations, policies, manuals, case files and contracts. Every version is tracked.
Adapt to your domain
We start from a security-vetted open-weight model and train it on your documents, then fine-tune it on worked examples your experts review.
Measure on your tasks
Test the domain model on your own tasks and compare it with general-purpose models before anything goes live.
Run it where your data lives
Access-controlled and audited inside your infrastructure. Each release is validated before deployment.
Where it fits
For any organisation that can't send its data outside.
Healthcare, education, finance, government, legal and any other regulated field. These are sample use cases; the best place to start is your highest-value workflow.
Virtual assistant
Voice and chat self-service for customers, patients, students or citizens, around the clock and in several languages.
Enterprise search and copilot
Search policies, summarise reports, draft documents and query internal data, all on-premise.
Document intelligence
Contracts, forms, reports and records, read at scale with traceable references.
Agent assist
Live answers for service teams, call summaries and quality scoring on every conversation.
Clinical knowledge assistant
Search and summarise guidelines, protocols and records on-premise, so patient data stays inside the hospital.
Learning and assessment
Adaptive learning and assessment built on your own curriculum, with student data kept within the institution.
Risk, fraud and compliance
Fraud and AML monitoring, KYC checks, credit decisioning and regulatory reporting.
Research and drafting
Research and first drafts grounded in statutes and case law, as in GST Amicus.
In use
In production and pilots.
Domain models built with the studio, in use with clients. Client names are withheld to protect confidentiality.
A domain model in place of a general AI service
We built and piloted a domain model to replace a general AI service, at a fraction of the inference cost. The pilot was a success, and we handed the model over to the client's team.
Tax adjudication
GST Amicus, built on the studio, is in an active state pilot with commercial tax officers.
Free two-week benchmark
See it work on your own data first.
We benchmark a domain model on one of your tasks and compare it with the AI service you use today. Two weeks, at no cost.
Questions
Frequently asked.
What is a domain-adaptive language model?
A language model that is further trained on the documents of one field, such as tax law, clinical guidelines, a curriculum or a company's own records, so it uses that field's terms and rules correctly. It is usually smaller than a general model and cheaper to run.
Where does the model run?
Inside your infrastructure: on-premise, in your private cloud, or fully air-gapped. Your documents and queries do not go to an external AI service.
What hardware is needed?
It depends on the model size. As a reference, a 30-billion-parameter-class model in FP8 runs on a single 80 GB GPU server. You pay for the server, not per query.
How do we know it will work on our data?
We run a two-week benchmark on your own task, at no cost, and compare the domain model with the AI service you use today.
Contact
Deploy with precision.
We partner with a limited number of enterprises at a time, so every engagement gets senior attention. Brief our leadership on your roadmap.