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Making access to medical data frictionless and transparent with AI

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Our Services

AI That Understands Healthcare

Transform how you access and utilize healthcare data with our specialized AI assistant.

What can I help with?

Add document

Analyze

Extract Data

Summarize

What can I help with?

Add document

Analyze

Summarize

Patient Search

Instant Clinical Insights

Find patient records, treatment histories, and imaging results across all your systems with natural language. No complex queries needed.

Complex patient cohorts

20+ Data integrations

AI Processing

Medical Intelligence

Our AI understands medical terminology, clinical context, and healthcare workflows, delivering accurate results every time.

Medical terminology recognized

Clinical context understood

Searching for data

DICOM

FHIR

Clinical notes

Ductal carcinoma in situ
756 patients identified
Search complete
Data extracted in 2.1s
Ready for export
Pulmonary embolism
524 patient identified
Search complete
Medical terminology recognized
Ready for export
Pneumothorax
2539 patients identified
Search complete
Clinical context understood
Ready for export
Subdural hemorrhage
1396 patients identified
Search complete
Results generated in 1.3s
Ready for export
Ductal carcinoma in situ
756 patients identified
Search complete
Data extracted in 2.1s
Ready for export
Pulmonary embolism
524 patient identified
Search complete
Medical terminology recognized
Ready for export
Pneumothorax
2539 patients identified
Search complete
Clinical context understood
Ready for export
Subdural hemorrhage
1396 patients identified
Search complete
Results generated in 1.3s
Ready for export
Ductal carcinoma in situ
756 patients identified
Search complete
Data extracted in 2.1s
Ready for export
Pulmonary embolism
524 patient identified
Search complete
Medical terminology recognized
Ready for export
Pneumothorax
2539 patients identified
Search complete
Clinical context understood
Ready for export
Subdural hemorrhage
1396 patients identified
Search complete
Results generated in 1.3s
Ready for export
Ductal carcinoma in situ
756 patients identified
Search complete
Data extracted in 2.1s
Ready for export
Pulmonary embolism
524 patient identified
Search complete
Medical terminology recognized
Ready for export
Pneumothorax
2539 patients identified
Search complete
Clinical context understood
Ready for export
Subdural hemorrhage
1396 patients identified
Search complete
Results generated in 1.3s
Ready for export

Identify

Extract

Searching for data

DICOM

FHIR

Clinical notes

Ductal carcinoma in situ
756 patients identified
Search complete
Data extracted in 2.1s
Ready for export
Pulmonary embolism
524 patient identified
Search complete
Medical terminology recognized
Ready for export
Pneumothorax
2539 patients identified
Search complete
Clinical context understood
Ready for export
Subdural hemorrhage
1396 patients identified
Search complete
Results generated in 1.3s
Ready for export
Ductal carcinoma in situ
756 patients identified
Search complete
Data extracted in 2.1s
Ready for export
Pulmonary embolism
524 patient identified
Search complete
Medical terminology recognized
Ready for export
Pneumothorax
2539 patients identified
Search complete
Clinical context understood
Ready for export
Subdural hemorrhage
1396 patients identified
Search complete
Results generated in 1.3s
Ready for export
Ductal carcinoma in situ
756 patients identified
Search complete
Data extracted in 2.1s
Ready for export
Pulmonary embolism
524 patient identified
Search complete
Medical terminology recognized
Ready for export
Pneumothorax
2539 patients identified
Search complete
Clinical context understood
Ready for export
Subdural hemorrhage
1396 patients identified
Search complete
Results generated in 1.3s
Ready for export
Ductal carcinoma in situ
756 patients identified
Search complete
Data extracted in 2.1s
Ready for export
Pulmonary embolism
524 patient identified
Search complete
Medical terminology recognized
Ready for export
Pneumothorax
2539 patients identified
Search complete
Clinical context understood
Ready for export
Subdural hemorrhage
1396 patients identified
Search complete
Results generated in 1.3s
Ready for export

Identify

Extract

Research projects

Waiting for approval

Pneumothorax
Completed 2 weeks ago
Subdural hemorrhage
Completed 2 days ago
Ductal carcinoma in situ
Approval pending
Subarachnoid hemorrhage
Approval pending
Small cell lung cancer
Completed 10 days ago

Research projects

Waiting for approval

Pneumothorax
Completed 2 weeks ago
Subdural hemorrhage
Completed 2 days ago
Ductal carcinoma in situ
Approval pending
Subarachnoid hemorrhage
Approval pending
Small cell lung cancer
Completed 10 days ago

Research Dashboard

Accelerate Research

Identify patient cohorts and extract relevant data for clinical studies in minutes instead of weeks. Speed up your research without sacrificing accuracy.

AI Patient search

Automated data monetization

Our Process

How It Works

We design, develop, and implement automation tools that help you work smarter, not harder

Step 1

Seamless Integration

Securely integrate with your existing EMR, EHR, and healthcare databases. Our platform supports all major healthcare data standards.

Our solution

Your stack

Step 2

AI enabled data search

Our team builds AI agents to find complex requests for medical data. Enter a natural language query and our engine scans every connected provider.

Analyzing current workflow..

System check

Process check

Speed check

Manual work

Repetative task

Step 3

Extract data

Layer filters for diagnosis, meds, labs, or demographics to zero in on exactly the patients you need.

Searching

Finding relevant data

Request submitted

Request submitted for review

Data exported

Completed 4 days ago

Step 4

Accelerated AI development

We help developers find the right data they need to make the best possible models for medical AI

class ClinicalRiskModel:

def __init__(self, risk_threshold):
self.risk_threshold = risk_threshold
self.status = "unfitted"

def fit(self, X, y):
self.status = "trained"
return "Model trained on clinical data."

def predict_risk(self, patient_vector):
risk_score = patient_vector.mean()
if risk_score > self.risk_threshold:
return "High-risk patient identified"
else:
return "Risk below clinical threshold"

def get_status(self):
return f"Model status: {self.status}"
class ClinicalRiskModel:

def __init__(self, risk_threshold):
self.risk_threshold = risk_threshold
self.status = "unfitted"

def fit(self, X, y):
self.status = "trained"
return "Model trained on clinical data."

def predict_risk(self, patient_vector):
risk_score = patient_vector.mean()
if risk_score > self.risk_threshold:
return "High-risk patient identified"
else:
return "Risk below clinical threshold"

def get_status(self):
return f"Model status: {self.status}"

FAQs

We’ve Got the Answers You’re Looking For

Quick answers to your AI automation questions.

How is your search different from other medical data platforms?

What kind of data do you analyse?

Who are your data sources?

How do we get started?