We build software for hospitals, health systems and digital health companies: prior authorisation and claims tools, ICU early-warning alerts, AI scribes, telehealth video and links between separate EHRs. Patient data stays inside your environment, and a clinician approves anything that reaches the record.
What we build
Each area is backed by work we've shipped. Start with the one you need now and add others as you grow.
Models that read bedside monitors, lab results, CT and MRI scans and wearable data, flag risk early and show the care team why.
Software that reads clinical notes, checks them against each payer's medical necessity rules and approves routine requests, with the evidence saved for payer audits.
An ambient AI scribe that drafts a structured note during the visit. The provider reviews and approves it before it is written to the EHR.
Video visits that scale with demand, post-discharge check-ins that flag pain or confusion to nurses, and wellness apps that sync wearable data.
Links between separate EHRs, cross-site patient matching, and one portal for referrals, documents, admin tasks and reporting.
Trial matching from clinical notes, drug stock forecasts from patient volumes and procedure schedules, and monitoring that isolates compromised medical devices.
Our approach
The usual way
Diagnostic AI is trained by pooling patient records in one place, which raises consent and privacy questions before a model ever reaches a ward.
How we do it
Models train inside each hospital's firewall and share only model updates. In our federated diagnostics work, accuracy rose 40% with no patient data leaving any site.
The usual way
Alerts arrive as a bare score with no reason, so nurses learn to ignore them.
How we do it
Each alert explains itself. Our ICU model reads monitor and lab data and sends nurses an explained alert. It flagged sepsis 4.1 hours earlier.
The usual way
Each site runs its own EHR, so staff re-type data and patient records drift apart.
How we do it
We map HL7 v2 feeds and older databases into one shared record format with FHIR APIs. For a provider running Epic, Cerner and custom databases, records reached 100% consistency.
Results
17 published case studies. Every figure on this page comes from one of them.
For a large health system, we built a claims platform that matches EHR evidence to payer rules, automates routine authorisations and logs every decision. It is built to HIPAA requirements. Claims recovery rose 42%, and staff saved 15k hours.
We built a streaming ML system for a hospital ICU that reads monitor and lab data and sends explained sepsis alerts to nurses. It flagged sepsis 4.1 hours earlier and was linked to an 18% drop in sepsis deaths.
We rebuilt a global healthcare provider's telehealth video platform on distributed video servers (SFUs) that scale with demand and keep patient data out of the logs. It supports 50k+ concurrent sessions with 99.99% uptime.
We helped Eating All Together build a mobile nutrition app that combines personalised meal plans, ingredient scanning, grocery support and nearby dining suggestions in one place.
We built an AI nutrition platform that helps people plan meals, manage groceries, recognise ingredients and find suitable places to eat nearby, without logging everything by hand.
We built an AI scribe that listens during the patient visit and drafts a structured clinical note. The provider reviews and approves the note before it reaches the health record.
We built an AI system for a global oncology research network that reads clinical notes, matches patients to trial criteria and explains each match. Enrolment became 4x faster, with 65% fewer screen failures.
We built an AI security platform for a hospital network's connected medical devices. It learns normal device behaviour, isolates threats and applies patches automatically. Mean time to patch fell 92%, with zero clinical downtime.
We built a federated learning platform that trains diagnostic AI inside each hospital's own firewall. Hospitals share model updates, not patient data. Diagnostic accuracy rose 40%, with zero data leaving any site.
Services
The services behind our Healthcare work. One squad can combine several, so you don't manage separate vendors.
Trust and control
Encryption, data minimisation, retention rules and controlled exports, built to HIPAA requirements. Personal details are removed before data reaches an AI model or a log.
AI drafts notes, flags risk and suggests matches. A clinician reviews each one before it changes a record or a care plan.
Each authorisation, alert and trial match is saved with the data and reason behind it in tamper-resistant logs, ready for payer or regulator review.
Staff see only what their role needs. Admin rights are kept separate, and emergency break-glass access needs approval.
We build around the systems you already run. Standards are ones we design for; certification stays between you and your auditor.
Partnership models
Pick the support that fits your team today, whether you need ongoing capacity or one clearly defined project.
A ready team of developers, a project manager and QA specialists who work alongside you and keep new features shipping.
Explore managed teamsSenior technical guidance, a clear roadmap and help with the big product and platform decisions.
Explore fractional CTOA clearly scoped product or platform, delivered through agreed milestones with regular updates and written approval criteria.
Scope a projectWe report risks as quickly as wins. You can talk to the engineers directly and see the project tools and progress at any time.
We own the work, explain problems plainly and stay involved until the agreed result is delivered.
We release the smallest useful version, learn from how people use it and improve it without lowering the quality bar.
We build healthcare software to HIPAA requirements from the start, with encryption, least-privilege access, data minimisation, retention rules and audit logs. Compliance also depends on how the system is run, so we document every control and give your compliance team the evidence to review. Certification stays between you and your auditor.
We connect to EHRs through FHIR APIs, HL7 v2 feeds and the other integration options your system supports. For a regional health provider running Epic, Cerner and custom SQL databases, we joined all three into one shared record format with cross-site patient matching, reaching 100% record consistency and an 85% cut in response time.
For one large health system, our claims platform recovered 42% more revenue and saved 15k hours. It reads EHR evidence, checks it against each payer's medical necessity rules and approves routine authorisations, storing the reason for every decision for payer audits. Your own results will depend on your payer mix and current denial rates.
Models can run inside your own cloud or on site, so patient records stay where they are. To learn across several hospitals, we use federated learning, which shares model updates instead of records. In our diagnostic AI project, this lifted accuracy by 40% with no patient data leaving any hospital's firewall.
Your clinicians stay accountable for every clinical decision. Each alert, trial match or draft note shows the evidence behind it, and a clinician reviews it before anything reaches the record or the patient. Our ICU system, for example, sends nurses explained sepsis alerts built from live monitor and lab data.
Start with the one workflow that costs your staff the most time, such as prior authorisation, claims denials or clinical notes. We build and prove it with your team before expanding to others. We can sign an NDA before you share any patient workflows or data, and a scoped project can start within 1–2 weeks.
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