AI for healthcare teams, inside the systems you already run
Clinical analytics, data pipelines, patient engagement and AI built into how care teams already work, designed for sensitive patient data from the first sprint.
We've built a healthcare data platform's core MVP and an oncology analytics platform on millions of patient records, both for US companies and both under NDA.
Who this is for in healthcare
For hospitals, health systems and healthcare companies bringing AI into clinical or operational work, where a wrong answer or a leaked record matters. Typical owners: a CIO or CTO, a head of data or analytics, or a clinical operations lead who needs the data that already exists to answer questions faster.
The problems we hear most
And what we'd build for each one.
Fragmented systems
EHRs, treatment systems and spreadsheets each hold part of the picture.
An integration layer
Manual workflows
Follow-ups, scheduling and reporting still run on phone calls and copy-paste.
Workflow automation
Data you can't reach
Years of treatment and outcomes data, but no fast way to ask it questions.
Analytics and dashboards
Gaps in follow-up
Patients wait for answers, miss appointments and fall through follow-up gaps.
Patient engagement apps
Legacy systems stay
New tools must fit the clinical systems already in place, not replace them.
API-first architecture
Care after the visit
Tools for follow-up between visits are thin or bolted on.
Remote monitoring and care-team messaging
Stretched workforce
Clinicians spend time on admin that software could draft, route or triage.
AI assistants with human review
Security and compliance
Sensitive patient data raises the bar for every feature.
Secure-by-design builds
What we build for healthcare teams
Each capability is labelled with its evidence: shipped in this sector, built in another industry, or a core capability we offer.
- Shipped in healthcare
Healthcare analytics and dashboards
Role-based clinical and operational dashboards: missed appointments, case management against targets, treatment plans awaiting approval, treatment volumes and a data quality scorecard for every site.
US oncology analytics case study - Shipped in healthcare
Clinical platforms
Treatment planning, case management and follow-up tools built around real care pathways.
- Shipped in healthcare
AI-assisted clinical workflows
Assistants, triage and insight generation embedded in treatment planning and follow-up, with clinicians approving every step.
- Shipped in healthcare
Patient engagement and multilingual messaging
Portals, reminders and messages in the patient's language, both ways, that keep patients on track between visits.
- Shipped in healthcare
Integration and data pipelines across sites
Pipelines that pull records from many hospitals and clinics, clean and standardise them, score their completeness and load them into one data lake.
US oncology analytics case study - Shipped in healthcare
Healthcare data platforms
Connected data sources, pipelines drawn on a drag-and-drop canvas, and a data lake ready to query.
US healthcare data platform case study - Shipped in healthcare
Machine learning on clinical history
Similar-patient and outcome and risk models that give oncology experts evidence from past cases. The decision stays with the clinician.
US oncology analytics case study - Built in another industry
Workflow automation
Agents and rules that route tasks, flag exceptions and draft documents for a person to approve.
US logistics AI case study - Built in another industry
Voice and conversational assistants
Real-time spoken intake, FAQs and follow-up, answering from your own knowledge.
UK real-time AI avatar case study - Core capability
Telehealth and remote care
Virtual visits, remote monitoring views and care-team messaging.
- Core capability
Healthcare web and mobile apps
Apps for patients, clinicians and admin staff.
From scattered records to action
- IngestPatient, treatment and hospital data
- CleanNormalise and score data quality
- AnalyseAnalytics engine and AI
- SurfaceRole-based dashboards and alerts
- ActFollow-ups, plans and patient messages, approved by your team
Work you can ask us about
- US oncology analytics case study
A data lake of millions of patient records, similar-patient models and dashboards.
- US healthcare data platform case study
A core MVP from the front end to the data lake.
- UK real-time AI avatar case study
Speech and language on Azure in UK and EU regions.
What your IT, security and compliance teams will ask
- Where does our data live, and who can access it?
- In the region you need, on your cloud or ours (AWS or Azure). Clinicians, coordinators and admins see only what their role needs, behind SSO, MFA and session controls. We work inside your accounts, and our access ends when the work does.
- Is every action logged?
- Yes. Every view and change in the platform is logged and traceable, and so is every AI action, so your team can see who saw what and what the AI suggested.
- Does a person review what the AI does?
- Yes. AI drafts, ranks, translates or surfaces evidence, and a clinician or coordinator approves before anything reaches a patient or a record. In our oncology work, models show similar past cases and likely treatment paths, and the decision stays with the oncology team.
- Will it work with our EHR and existing systems?
- It's built to fit them, not replace them: EHR or HIS through APIs, SFTP and CSV data feeds, webhooks and events, and SMS, email and messaging. Integration scope and standards are confirmed with your team during discovery.
- How is patient data protected?
- Encryption in transit and at rest, isolated services and least-privilege access, token authentication, rate limits and input validation on every API, and versioned APIs. Personal data is kept out of AI prompts where it isn't needed, and AI providers are set up on terms where your inputs aren't used for training.
- What about HIPAA or GDPR?
- Requirements such as HIPAA or GDPR are scoped with your compliance team at kickoff, and the architecture is designed to support them, for example with providers that offer a BAA or DPA. Certification stays with your organisation and your auditors.
- How are scope and price fixed?
- A Blueprint agrees the use case, data model, integrations, AI models, hosting and a cost estimate first. The build then runs at a fixed scope and price agreed in writing. No hourly billing.
- How long does it take?
- It depends on scope and data readiness. You get a written plan with milestones before any build starts.
- What happens after launch?
- We can keep it running: monitoring for errors, downtime and AI cost spikes, a monthly health report and safe changes one request at a time. Or your own team takes it over, as the oncology client's team now runs its platform.
Scan. Blueprint. Build. Run.
The same pipeline as every TechTose engagement, scoped to your organisation.
- 01
Scan
We review your app, code or idea and send a written report.
Signed off by a TechTose architect. - 02
Blueprint
Your architecture decided and agreed: data model, backend, logins and roles, integrations, AI setup, hosting.
Signed off by a TechTose architect. - 03
Build
Built to the Blueprint, at a fixed scope agreed in writing.
Signed off by a TechTose architect. - 04
Run
Monitored, maintained and improved after launch.
Signed off by a TechTose architect.
Agents speed up the work. They don't make the decisions. A TechTose architect reviews every finding, plan and change before it reaches you.
How we handle confidential work
- We sign your NDA before you share code, data or plans.
- Your code and accounts are yours. We work inside them, and our access ends when the work does.
- We never name a client, product or person publicly without written permission.
- We describe the work, not the client, as we have on this page.
Good first projects
- Analytics and dashboards on data you already hold
- AI features for an existing clinical or patient product
- A full product build to a Blueprint
Bring to the call
- The workflow or patient problem you want to fix
- Who will use it: patients, clinicians, admins
- The systems it needs to connect to
Guides for healthcare teams adopting AI
Open-Source vs Closed-Source AI Models: Which Should Enterprises Choose?
23 July 2026Healthcare AI deployment: questions
Can you build AI that works with real patient records?
Yes. We've built a clinical data lake from millions of patient records across US hospitals and clinics, and a healthcare data platform where hospital patient-record pipelines must stay reliable. Both were built under NDA.
Will AI make clinical decisions?
No. AI gives clinicians evidence, drafts or alerts, such as similar past cases, a draft follow-up or a translated patient message. A person makes the decision and approves the action.
Can patients get messages in their own language?
Yes. We've shipped multilingual patient messaging that translates messages both ways between patients and care teams, with the care team in control of what's sent.
Do we need to replace our existing systems?
No. New tools are built API-first to sit alongside the EHR and treatment systems you already run.
Is our data used to train AI models?
No. We set up AI providers on business or enterprise terms where your inputs aren't used for training, turn off data retention wherever the provider allows it, and document the providers and settings for your security team.
Why don't you name your healthcare clients?
We sign NDAs, and we never name a client, product or person without written permission. On a call we can walk you through the work, within what each NDA allows.
How does a healthcare engagement start?
With a call about the workflow you want to fix, then a Blueprint that agrees the use case, data, integrations and cost before anything is built. If you already have an app or prototype, a free Scan comes back as a written report in 48 hours.