Services
// Healthcare AI
AI for healthcare, built inside the rules you already work under
Patient data does not get a pilot exception. We design healthcare AI the way your compliance team would if they wrote code — governance first, then the use case.
Talk to our team Request a current-state review
One of two teams in Asia Pacific recognised by Google as a Healthcare AI Expert.
// Where generic vendors stop
Healthcare is where generic AI vendors stop.
Every capable engineering team can build a chatbot. Very few can put one near a patient record and still pass review.
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The compliance review kills the project, late
Governance arrives after the build, as a veto rather than a design input. The work is already sunk by then.
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Clinical data does not behave like commercial data
Free-text notes, Thai and English mixed in one field, coding systems that disagree, and records that cannot leave their system.
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The vendor does not speak hospital
They optimise a funnel metric while the medical team is asking who is accountable for a wrong answer. Both sides leave frustrated.
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Marketing wants growth; the risk register says no
Patient acquisition ideas stall because nobody can show how personal data would be handled at each step.
// What we take on
What we build.
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Governance and data design for PDPA
Consent, minimisation, retention, access and audit trails specified before the first model call. Written so your DPO and your engineers read the same document.
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Patient-data-safe architecture
De-identification, segregation and access boundaries designed so sensitive fields never travel further than they must.
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Clinical and Thai-language NLP
Working with free-text notes and mixed Thai/English clinical language — extraction, structuring and search that survives real records — structured into a platform your analysts can use.
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Agentic AI for hospital operations
Agents for the repetitive internal work: routing enquiries, drafting documentation, retrieving policy and prior cases, escalating to a human by rule.
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Marketing AI for health groups
Preventive, aesthetic, clinical-trial and treatment journeys — personalisation that operates on consented data only, with the boundary written down.
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Evaluation with clinical review in the loop
Evaluation sets built with your clinicians, not by us alone, and scored again after every change.
// How a project runs
Governance is phase one, not a checkpoint at the end.
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Governance and data boundaries
We work with your compliance, IT and clinical stakeholders to define what data may be used, by whom, for what, and where it may travel.
- A data governance specification
- A PDPA-aligned data flow map
- A written scope of what is out of bounds
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Data foundation
Safe access to the sources the use case needs — de-identified or segregated as agreed — plus the structuring work that clinical text always requires.
- An ingestion and de-identification pipeline
- A structured dataset
- A data quality report
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Use case build and clinical evaluation
We build against an evaluation set defined with your clinicians, with refusal and escalation rules explicit from the first version.
- The working system
- Evaluation results reviewed by your team
- Guardrail documentation and an audit log design
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Production under supervision
Controlled rollout, monitoring of both quality and access, and handover to the team that will run it.
- Production deployment
- Monitoring and audit dashboards
- A runbook and staff training
Sequence and timeline are agreed with you before work starts — and in healthcare, the governance phase is not one you can buy your way past.
// Where this has run
Where this has run.
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A major Thai hospital group
A long-running healthcare AI and data engagement with one of Thailand’s largest hospital groups.
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Marketing agentic AI for a hospital group
Agentic AI for patient marketing journeys across preventive, aesthetic and treatment lines, built inside consent boundaries.
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Recognition
Google APAC recognised our team as a Healthcare AI Expert — one of two in Asia Pacific.
Client names and figures appear here only after the client has approved them in writing. In healthcare, that is not a formality — it is the point.
// Technology partners
The Data, AI and Agentic AI stack we build on.
We build on Google Cloud as a certified implementation partner, with multi-cloud delivery across BytePlus and Alibaba Cloud where the workload calls for it. From strategy through production, our deepest work is in Data Analytics, Data Foundation, Healthcare AI and Retail AI.
we are reseller
- Google CloudVertex AI
we are reseller-
One accountable partner.
From the first workshop to production support, you work with the same certified team. We advise, implement and stay accountable for what we ship.
Talk to our team 
we are reseller
- Data Lakehouse
- Streaming & CDC
- ETL & Pipelines
- BytePlusBytePlus Recommend
- Alibaba CloudMaxCompute
- Customer Data Platform
- Agentic AIHarness Engineering
- Agent Framework
- Conversational AI
// Why teams call us
Why healthcare teams call us.
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A specialty, not a vertical slide
One of two teams in Asia Pacific recognised by Google as a Healthcare AI Expert, under our Healthcare & Life Sciences Recognition on Google Cloud. This is the work we are known for, not a sector we added to the deck.
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We start where your risk team starts
Governance is phase one. It is the only order that gets a healthcare AI system into production rather than into a review loop.
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Long relationships, not project drops
Our healthcare work is measured in years with the same clients. That only happens if the systems keep working after we leave the room.
// Common questions
What hospital teams ask first.
Can AI touch patient data at all under PDPA?
Yes, within limits you have to design for rather than assume. In practice that means a lawful basis and consent state you can evidence, data minimisation, de-identification wherever the use case allows it, and access and audit controls that show who saw what. We specify all of it in phase one, before any model is involved. We are not your legal advisor — we build to the requirements your DPO and counsel set, and we make those requirements concrete in a governance-first architecture.
Does our data have to leave the hospital?
That is a design decision, and often the answer is no. We work with de-identification and segregation so that identifiable data stays where it is governed, and only what is necessary moves. Where a workload must stay in a specific region or environment, we design for that constraint from the start.
Our clinical notes are messy, in Thai and English, and often free text. Is that workable?
That is the normal starting condition, and it is most of the engineering. Extraction and structuring of mixed Thai/English clinical text is part of the data foundation phase — we do not treat clean data as a precondition.
Who is accountable when the AI is wrong?
Your organisation is, which is exactly why we design for it: explicit refusal rules, escalation to a named human role, evaluation sets reviewed by your clinicians, and logs that let you reconstruct any answer after the fact. A system that cannot explain what it did has no place near clinical work.
Can you help with marketing use cases, not only clinical ones?
Yes — patient acquisition and lifecycle marketing for health groups is a large part of what we do, and it carries its own consent constraints. Same order of work: define what consented data may be used for, then build. It rests on the same customer data platform work we do outside healthcare, inside tighter consent limits.
Bring us the use case your compliance review stopped
We would rather look at the one that failed than the one that is easy. That conversation tells you quickly whether it was the idea or the design that was wrong.
Innovate for the better tomorrow.
// Corporate update
Our
Move
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Buying the platform and building on it used to be two conversations with two suppliers. It is one conversation now: we resell Google Cloud, Alibaba Cloud and BytePlus, and the same engineers who size the environment stay with it through production support.
For teams already running with us, nothing changes technically — the difference is commercial. Licensing, quota and billing sit with the people who know what the workload actually does.
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A certified implementation partner works to the cloud provider's published reference architectures. In practice that means your landing zone, IAM model and network layout look like something any Google Cloud engineer can pick up — including the next team you hire.
It also means the review gates are not ours to waive. Where the reference architecture asks for separation of duties or a break-glass path, it gets built.
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Hospital data arrives in fragments — HIS exports, lab feeds, scanned forms, free-text notes in Thai and English. Before a model sees any of it, someone has to answer where each field came from, who consented to what, and which records must never leave the country.
We build that layer first. It is slower to demo and it is the reason the pilots survive contact with a real ward.
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Live commerce moves fast enough that the recommendation loop has to close in the same session. That puts the weight on the event pipeline, not the model: what counts as a view, when a cart event lands, how quickly the feature store sees it.
We treat the BytePlus components as a stack to be wired properly rather than a switch to be flipped. The lift comes from the wiring.
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The site you are reading ships as static HTML, one stylesheet and one script, served by a Node process with a strict content security policy. There is no analytics tag, no font CDN and no tracker.
It is partly a statement of taste and partly a working sample: the same restraint we bring to a client's platform, applied to our own front door.
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Warehouses fill up faster than they get governed. By the time a model needs a feature, nobody can say which of the four revenue columns is authoritative, and the project stalls in a meeting about definitions.
The fix is unglamorous: contracts on the ingest side, lineage through the transformations, and one owner per domain. Do that and the AI work stops being archaeology.