Privacy by design
PHI/PII-aware pipelines built to support HIPAA compliance.
From Healthcare Data To Smart Clinical Decisions
What We Do
We help healthcare and medtech teams turn clinical and physiological data into secure, actionable insights combining signal processing, secure data pipelines, machine learning models, and AI agents to generate clinically relevant recommendations.
Data Engineering
Build secure, reliable data pipelines for healthcare ingesting EHR, device, and wearable data.
Machine/Deep Learning
Design, train, and validate ML/DL models—then take them from prototype to FDA-ready deployment.
Signal Processing
Turn raw physiological signals into usable features filtering noise, detecting beats, and removing motion artifacts.
AI Agents & Automation
AI agents that translate complex patient data into plain‑language explanations and next‑step recommendations.
End-to-End Digital Health Solutions
- 01 Proof of Concept
- 02 Data Collection
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03
Algorithm Development
& Validation - 04 FDA Submission
- 05 Post-Market Support
Our Expertise
10+ years working with physiological waveforms and clinical data, building and validating signal processing and ML algorithms from bench to bedside.
Physiological Waveforms
- ECG (electrocardiography)
- EEG (scalp and intracranial recordings)
- EMG (electromyography)
- Photoplethysmography (PPG)
- Blood pressure (invasive arterial line + non-invasive cuff)
- NIRS (near-infrared spectroscopy)
- SCOS (speckle contrast optical spectroscopy)
- PCG (phonocardiogram)
Clinical systems / EHR & EMR Integrations
- EPIC
- Oracle health (Cerner)
- AIMS (Anesthesia Information Management System)
- Philips IntelliSpace Critical Care and Anesthesia (ICCA)
Bedside & Perioperative Monitors
- Philips IntelliVue Series
- GE Carescape
- Edwards Lifesciences HemoSphere
Proven algorithms published in peer reviewed journals and deployed in products that achieved regulatory clearance (FDA / CE Mark).
Beat / Event Detection
- Right-heart pressure waveforms
- Arterial line pressure
- Heart sounds (phonocardiogram)
- 3-lead ECG
- Blood-flow index
Parameter Estimation
- Cardiac output
- Respiration rate
- Pulmonary capillary wedge pressure
- Blood pressure
- Brain cortical activity
Classification/Prediction
- Abnormal heart sounds
- Muscle fatigue
- Aortic stenosis
- Right ventricular dysfunction
- Hypotension
- Hemodynamic instability
Deep experience with clinical data collection and validation across diverse populations, including subgroup performance analysis, V&V documentation, and FDA PCCP support.
Validation data strategy
Inclusion/exclusion criteria, endpoints, sampling plans, multi-site collection, ground truth and adjudication.
Generalization & Fairness
Performance across age/sex/race/ethnicity, comorbidities, device types, and care settings.
Evidence package
Locked model evaluation, traceability, risk controls, performance reports, and audit-ready documentation.
PCCP
Planning—what can change, how it will be controlled, and how updates will be verified/validated.
Your MedTech AI Technical Lead
I have spent years driving innovation across healthcare organizations, bringing AI algorithms for cardiovascular and hemodynamic monitoring to market from concept and model design through verification/validation, regulatory clearance, commercialization, and clinical adoption.
Have a medtech AI challenge? Let’s talk
Digital Health Client Stories
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PhilipsCase Study 01
The Challenge
Detect valvular disorders from heart sound recordings of healthy subjects and pathological patients.
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PhilipsCase Study 02
The Challenge
Detect valvular disorders from heart sound recordings of healthy subjects and pathological patients.
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PhilipsCase Study 03
The Challenge
Detect valvular disorders from heart sound recordings of healthy subjects and pathological patients.
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PhilipsCase Study 04
The Challenge
Detect valvular disorders from heart sound recordings of healthy subjects and pathological patients.
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PhilipsCase Study 05
The Challenge
Detect valvular disorders from heart sound recordings of healthy subjects and pathological patients.
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ClienteCase Study 06
The Challenge
Detect valvular disorders from heart sound recordings of healthy subjects and pathological patients.
Trust, Safety & Compliance
Audit-ready
Audit logs, reproducible runs, and documentation that supports regulatory submissions.
Responsible AI
Continuous model monitoring, bias checks, and full data lineage.
Get in touch with us
Tell us about your project. One of our engineers will reply within one business day.
Our Process Explained
01 · TELL US
Start with the problem
Tell us what you’re building, what it runs on, or what’s blocking you. Share your requirements, repo, or documentation
02 · SCOPE
Define the scope
We turn the problem into a clear plan: what we’ll build, what’s out of scope, and how we’ll measure success.
03 · SPRINT
Build in a focused sprint
Our team builds and tests in short, time-boxed cycles, with a working update you can review at each step.
04 · PROOF
Get proof it works
You receive validated results and working code, with the evidence your team, investors, or regulators need.
Fixed Prices. No call required to see them.
01
R&D / Discovery: Hourly Billing
R&D / Discovery is designed for technical work where the solution, data, or implementation approach is not yet fully defined. Work is billed at an agreed hourly rate based on the time required to research, experiment, test, and validate the approach.
When to use it:
- The technical approach is not yet established
- You need to investigate data, signals, models, or technologies
- You want to test whether an idea is technically feasible
- Requirements may change as research progresses
- You need a proof of concept before defining a production project
How billing works:
You pay for the actual engineering time completed at the agreed hourly rate.
Typical rate
$180 – $400 / hour
Depending on technical complexity and specialization.
02
Project-Based: Fixed Fee
Project-Based work is designed for initiatives where the problem, technical requirements, scope, and expected deliverables are clearly defined.
When to use it:
- The solution and technical approach are already defined
- You know what needs to be built
- The final deliverable can be clearly specified
- Requirements are stable enough to establish a fixed scope
- You need predictable project costs and delivery milestones
How billing works:
The project fee is agreed before implementation begins, based on the defined scope, requirements, deliverables, and timeline.
Typical project range
$60,000 – $350,000
Depending on scope, complexity, and deliverables.