Your data cannot go to a public API
Every useful assistant seems to require uploading records to someone else's cloud. Legal says no, and they are right to.
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Healthcare
Clinical AI that never asks you to send patient data somewhere else.
Private models, compliant pipelines and managed infrastructure for hospitals, diagnostics chains and health-tech companies. The data stays where your compliance team can point at it.
The three things we hear in the first meeting.
If none of these sound like you, we are probably not the right call yet — and we will say so.
Clinicians are doing admin, not medicine
Discharge summaries, prior-auth paperwork and coding eat hours that were meant for patients.
The last vendor quoted a number that kept moving
Per-seat AI pricing on a 4,000-staff hospital does not survive contact with the finance committee.
We speak both clinical and infrastructure.
Most AI vendors understand models. Most IT vendors understand hospitals. The work in healthcare sits exactly between the two — and that gap is where projects stall for a year.
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Data stays in your environment.
Models run inside your VPC, your on-premise rack, or a dedicated Stellar Cascade tenancy in-country.
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Every answer cites its source.
Retrieval over your own protocols and records, so a clinician can check the citation before acting on it.
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Audit trails by default.
Who asked, what the model saw, what it returned, retained for the period your policy requires.
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Clinicians in the loop from week one.
The people who will use it sit in the discovery sessions, not just the review at the end.
Who we build for
Different teams, different first project. The platform underneath is the same.
Multi-speciality hospitals
Discharge summaries, bed and theatre planning, and a single search layer across HIS, LIS and PACS.
Diagnostics chains
Report drafting, abnormal-result triage, and turnaround-time analytics across collection centres.
Health-tech products
The AI feature your roadmap promised, built to survive a hospital's security review.
Payers and TPAs
Claims intake, document extraction and fraud signals on documents that arrive as scans.
Clinical research
Protocol search, eligibility screening and structured extraction from unstructured notes.
Hospital IT teams
24/7 management of the clinical estate, so your team runs the roadmap, not the pager.
What we actually deliver
Four things, in this order. Each one is useful on its own.
AI Discovery for clinical teams
Two weeks with your clinicians and IT to pick the two use cases worth funding.
Compliant data platform
Ingestion, de-identification, access control and retention across your clinical systems.
Private clinical assistant
Retrieval-grounded answers over your own protocols, formulary and patient context.
Managed clinical infrastructure
GPU capacity, backups, patching and 24/7 monitoring for systems that cannot be down.
How a healthcare engagement runs
No surprises about sequence, and no invoice before there is something to look at.
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01
Clinical discovery
Two weeks shadowing the workflow. We come back with the use cases ranked by hours saved and risk carried.
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02
Architecture and sign-off
One document your CIO, compliance lead and clinical head can all approve. Data flows, residency, retention and cost.
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03
Pilot on one ward
A real workflow, real users, four to six weeks. Measured against the baseline we recorded in discovery.
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04
Roll out and run
Department by department, with our team on the pager while yours learns the platform.
What good looks like six months in
- 0
- Patient records leaving your environment
- 100%
- Answers with a traceable source
- <2 s
- Median response at the bedside
- 24/7
- Monitored, with a named engineer on call
The Healthcare AI readiness checklist
The questions we work through before recommending anything — data readiness, hosting constraints, review process and the running cost at year two. Use it with any vendor.
Healthcare questions we get asked first
Short answers. Longer ones are a conversation.
Where does patient data actually sit?
Inside your environment. That is either your own cloud account, your on-premise hardware, or a dedicated single-tenant Stellar Cascade region in India. Nothing is sent to a public model API unless you explicitly ask for that and sign off on it.
Can you work with our existing HIS and LIS?
Yes. We integrate at whatever layer your vendor exposes — HL7, FHIR, direct database reads, or file drops if that is all there is. We have done all four. The integration approach is settled during discovery, not assumed.
How do you stop the model inventing a clinical answer?
Answers are retrieved from your own documents and always carry the citation. If the retrieval finds nothing relevant, the system says so rather than generating something. Clinical-risk use cases additionally go through a human review step that your team defines.
What about DPDP and hospital accreditation requirements?
We build to data-residency, consent and retention rules from the start, and hand over the documentation your accreditation and audit processes need. We are not a law firm — your compliance team signs off, and our job is to make that sign-off straightforward.
How long before clinicians see something real?
A working pilot on one ward or one department typically runs four to six weeks after the architecture is signed off. Discovery is two weeks before that.
What does it cost to run?
Infrastructure is sized to your actual usage and quoted as a monthly figure before the build starts. There is no per-seat AI licence, so adding staff does not change the bill.
Talk to someone who has done this in a hospital.
Tell us the workflow that is hurting. We will tell you whether AI is the right answer — including when it is not.
- A reply within one working day, from an engineer rather than an account manager.
- An honest read on whether this is worth doing now, or in a year.
- No marketing list. Your details reach the solutions team and stop there.