Lab-report extraction
Pull biomarkers and results from lab PDFs and scans into structured data for review.
Who it’s for
Who this is for and the problems it solves
Typical clients
- Health-tech companies adding AI features to a patient or clinician product
- Clinics, labs and diagnostic centres handling large volumes of reports and forms
- Wellness and preventive-care providers personalising programmes from health data
- Healthcare operations teams buried in manual documentation and follow-up
Common problems
- Lab reports and clinical documents arrive as PDFs and scans that staff re-key by hand
- Patients ask the same questions repeatedly and staff time goes on routine replies
- Health data sits in separate systems, devices and formats
- Clinical teams need AI output they can review and trust, not a black box
Use cases
What we can build
Examples of our capabilities, not descriptions of specific client projects.
Patient-facing assistants
Answer common questions from approved content, with clear escalation to staff.
Clinical documentation support
Summarise notes and records for review by clinicians.
Health-data integration
Bring wearable, questionnaire and record data into one view for care teams.
Scope
Scope, deliverables and what we need from you
What we deliver
- Discovery with clinical and operational stakeholders
- Data mapping and a data-handling plan agreed with your team
- Prototype evaluated on de-identified or sample data
- Production application, integration or API
- Review workflow so clinicians approve AI output where required
- Documentation and handover
Data & integration requirements
- Sample documents or data, de-identified where possible
- Access to the systems we connect to (EHR, LIS, CRM or your own app) or their API documentation
- A named clinical reviewer for evaluation
- Your compliance requirements and hosting constraints
Delivery
How we deliver and evaluate
Discovery
Understand the workflow, the data and what success looks like. Agree scope and measurable targets.
Prototype & evaluate
Test the approach on a sample of your data against agreed criteria before a full build.
Build & integrate
Develop, connect to your systems and test with your team, with regular demos.
Deploy & support
Release to your chosen environment, hand over documentation and agree ongoing support.
We measure accuracy against examples reviewed by your clinicians and agree acceptance thresholds before go-live. Clinical decisions stay with qualified staff; our systems support review rather than replace it.
Evidence
Relevant work
Kubit’s product
BioHarmony
Kubit’s own preventive-care AI platform, with AI models trained internally by our team. It includes lab-report processing (OCR, biomarker parsing and interpretation). Feature stages are team-reported; request a demonstration to confirm current availability.
Healthcare work is guided by our clinical adviser, Dr Sanjay Sachdeva (MBBS, PhD).
Options
Ownership, deployment and support
Ownership
Ownership of the code, prompts, evaluation sets and any model weights we produce for you is agreed in the statement of work, subject to the licences of any third-party models or components used.
Deployment
Your own cloud account (AWS, Azure or Google Cloud), your on-premises environment, or hosting managed by Kubit’s.
AI models
Hosted models from major providers, open-source models run in your environment, or models fine-tuned on your data, chosen per use case for accuracy, cost and data handling.
Support
Post-launch support and maintenance are agreed per project, including monitoring, updates and response times.
Compliance
We agree data-handling, access control, hosting location and audit logging with you and work with your compliance team on the requirements that apply to you. We don’t certify compliance on your behalf.
FAQ
Questions about this service
Do you need real patient data to start?
No. We usually start with de-identified or synthetic samples, and only move to real data once handling, access and hosting are agreed.
Can the system run in our own environment?
Yes. We can deploy into your cloud account or on-premises infrastructure so data stays under your control.
Will the AI make clinical decisions?
We design systems to support qualified staff. Where output affects care, a clinician reviews it.