Tool overview
Frekil is listed under Healthcare AI tools.
What is Frekil?
Frekil automates real-world evidence generation for life-science teams. It connects clinical data, harmonizes records, builds cohorts and study plans, generates reviewable statistical code, executes analyses in isolated environments, and produces documented evidence outputs.
Best for
Pharma and biotech teams accelerating rigorous real-world evidence programs
Who is it for?
Decision note
Suitable for evaluation after confirming final commercial terms, permissions, data handling, current product scope, and edit-screen aliases. Keep Needs Review enabled until a human verifies the published profile.
Key features
Automated clinical data harmonization and OMOP mapping
Protocol, cohort, and causal-design generation
Transparent Python, R, SQL, and SAS code
Sandboxed execution with immutable audit trails
Continuous safety and effectiveness monitoring
Use cases
Run real-world evidence studies
Build external control arms
Monitor post-market safety signals
Prepare reproducible clinical analyses
Pros
- Compresses evidence timelines substantially
- Keeps generated analytical code reviewable
- Separates AI models from patient data
Cons
- Commercial pricing requires direct contact
- Regulatory evidence still needs expert sign-off
Limitations
Frekil does not replace epidemiological, biostatistical, clinical, privacy, or regulatory review. Validate cohort definitions, causal assumptions, generated code, source data quality, statistical results, and jurisdiction-specific requirements before decisions or submissions.
Pricing details
Billing options
Pricing note
Frekil publishes flexible per-study and platform pricing but no public fixed amount. Both compact pricing fields use Contact sales. A demo, hypothesis test, or possible product evaluation is not recorded as a self-service Free Trial.
Supported languages
- English
Integrations
EHR data
Claims data
Registries
Databricks
Snowflake
AWS
Google Cloud
Azure
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