Frekil
AI-powered real-world evidence generation from clinical data in minutes
Frekil is a serious tool for teams that need rapid, auditable real-world evidence, especially in regulated settings. The air-gap architecture is a genuine differentiator, and the pipeline depth—from literature review to report writing—is impressive. But the lack of transparent pricing and enterprise-only onboarding means smaller teams may struggle to access it.
Verified 7d ago · liveness 49/100 · cite: rightaichoice.com/tools/frekil
- HEOR teams needing rapid comparative effectiveness evidence and payer value dossiers
- Biostatisticians in pharma running target trial emulations and survival analyses
- Epidemiologists conducting post-market safety signal detection across millions of records
- Clinical researchers generating evidence for label expansion and external control arms
- Teams without access to structured clinical data (EHR, claims, registries)
- Small academic projects with small datasets that don't warrant enterprise onboarding
- Users needing real-time clinical decision support at the point of care
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Skip Frekil if you don't have access to structured clinical data (EHR, claims, registries) or if your organization is not ready for an enterprise-level onboarding and contact-sales pricing model.
Pricing is not publicly listed; you must contact sales, which may involve a substantial annual commitment and enterprise-level fees.
Frekil's pricing is contact-sales only, making it a significant investment suited for enterprise pharma, CROs, and large healthcare organizations. For smaller teams or academic projects, the cost may be prohibitive compared to self-serve options like TriNetX or open-source R pipelines.
In short
Frekil — AI-powered real-world evidence generation from clinical data in minutes. Best for HEOR teams needing rapid comparative effectiveness evidence and payer value dossiers, Biostatisticians in pharma running target trial emulations and survival analyses, Epidemiologists conducting post-market safety signal detection across millions of records. Contact Sales pricing.
What's new in Frekil
Checked 4 days agoAcross the latest 5 updates: 5 news mentions.
Can You Trust That p-value? The Math of Empirical Calibration
Explains negative controls and empirical calibration for reliable p-values in observational studies.
Approval Isn't Access. Here's Why Payers Need Real-World Evidence.
Covers how RWE addresses payer and HTA coverage questions beyond regulatory approval.
Why Real-World Evidence Needs More Than a Chatbot
Argues that generic AI chatbots fail for RWE; Frekil focuses on trustworthiness over fluency.
When the Control Arm Comes From the Real World
Discusses external control arms in rare disease and oncology and the challenge of fair comparisons.
Propensity Scores for Survival Outcomes: What RWE Teams Should Report
Technical guide to propensity scores for survival RWE, including matching, weighting, and sensitivity analysis.
What people actually say about Frekil — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
- +Reduces RWE generation timelines from months to minutes.
- +Auditable trace from raw data to final report.
- +Natural language querying makes it accessible to non-coders.
- +Supports target trial emulation and causal inference methods.
- +Air-gapped architecture ensures HIPAA/GDPR compliance.
- −Very few real user reviews available to date.
- −Pricing is undisclosed, likely expensive for small teams.
- −Integration with legacy EHRs may require extensive ETL.
- −Self-improving agents could amplify biases in small studies.
- −Complex causal DAG builder may require expertise.
- • Data ETL and migration may require extra services.
- • Potential per-study or per-user licensing costs.
Viability Score
How well maintained and how widely used is Frekil? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- No clinical data ever touches AI; code runs in sandbox
- Natural language querying of clinical data
- Automated literature review for evidence grounding
- Cohort building with temporal data mapping
- Causal DAG builder with user review
- Statistical Analysis Plan (SAP) generator
- Data extraction and transformation to OMOP CDM
- Sandboxed execution of statistical code in R, Python, SAS
- Report writer producing publication-ready outputs
- Target trial emulation for comparative effectiveness
- Survival analysis with Kaplan-Meier and Cox models
- Propensity score matching and weighting
- External control arms from historical real-world data
- Sensitivity analysis and empirical calibration
- Medical ontology support: ICD-10, SNOMED, RxNorm, ATC
About Frekil
Frekil is an AI-powered infrastructure platform that automates the generation of real-world evidence (RWE) from electronic health records, claims, registries, and proprietary data. Designed for HEOR teams, biostatisticians, epidemiologists, and clinical researchers in life sciences, it produces publication-ready evidence in minutes instead of months. The platform uses a continuous, self-improving AI engine to orchestrate an eight-stage pipeline: literature review, question clarification, cohort building, causal DAG construction, SAP generation, data extraction, SAP execution, and report writing. Users interact via natural language, review outputs at each step, and can adjust study designs before execution. Frekil supports target trial emulation, survival analysis (including Kaplan-Meier and Cox models), propensity score matching, external control arms, and causal inference with auditable code. Its self-improving agents learn dataset structure, code patterns, and cohort definitions, so each study sharpens the next. The platform resolves clinical synonyms automatically—understanding that "Type 2 diabetes," "T2DM," and "E11.9" refer to the same condition—and maps non-standard terminologies to OMOP CDM. A key differentiator is the architectural air gap: no clinical data ever touches the AI models. The AI generates statistical code in R, Python, and SAS that runs in a sandboxed environment, while the data stays in place. This hard boundary, not a policy, ensures HIPAA and GDPR compliance and addresses the trust concerns of regulated life sciences teams. Frekil connects natively to Databricks, Snowflake, AWS, GCP, and Azure, with region-specific data residency controls. Unlike generic BI tools or AI chatbots that can query data but don't understand medicine, Frekil is purpose-built for epidemiologically sound study designs, transparent evidence generation, and reproducible results. It's positioned as infrastructure for the entire RWE ecosystem—whether you're at a
Behind the Verdict
If you're in HEOR, biostatistics, or epidemiology at a large pharma, CRO, or research institution, Frekil could cut your RWE generation timelines from months to minutes. The eight-stage pipeline is built around the way epidemiological studies actually work—literature grounding, question clarification, cohort building, causal DAGs, SAPs, execution, and reporting. That's not something a generic chatbot can do. The air-gap architecture is the standout. Because no clinical data touches the AI models, you get HIPAA and GDPR compliance by design, not by policy. That's a huge deal for teams that have been blocked by privacy concerns in the past. It also means your data never leaves your environment—Frekil generates code that runs where your data lives, on Databricks, Snowflake, AWS, GCP, or Azure. But watch out: the platform's effectiveness depends on the quality and structure of your underlying data. It handles messiness, but if your data isn't well-documented or doesn't follow standard terminologies, you'll still need to invest in upfront harmonization. Frekil automates a lot, but it doesn't eliminate the need for solid data governance. Pricing is opaque—contact sales only. That makes sense for enterprise deals, but it's a barrier for smaller teams or academic groups that just want to try it on a modest dataset. You'll need to go through a sales process to even get a number, and that's a real friction point. Compared to generic analytics platforms like Databricks or Tableau, Frekil offers medical ontology understanding and study design awareness that those tools lack. It speaks the language of RWE, not just SQL. Compared to manual statistical programming in R or SAS, it automates the heavy lifting while keeping you in control. Where it bites: if you need real-time
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Real-world workflow fit
Concrete scenarios for the personas Frekil actually fits — and what changes day-one when you adopt it.
You need to run a target trial emulation comparing two antihypertensive drugs using your EHR data.
Outcome: Within hours, Frekil generates a causal DAG, statistical analysis plan, and executes survival analysis with auditable code, giving you a hazard ratio and confidence intervals ready for regulatory submission.
You need payer-ready comparative effectiveness evidence for a new drug.
Outcome: Frekil automates the full pipeline from literature review to report generation, producing a value dossier with tables and figures that your market access team can present to payers.
You need to detect adverse event signals in post-market safety data.
Outcome: Frekil's self-improving agents scan millions of patient records, running sensitivity analyses and empirical calibration to flag signals before they become public health issues.
Use Cases
- Run a target trial emulation comparing ACE inhibitors vs ARBs for heart failure, generating survival analysis and auditable code.
- Validate a new prognostic score for sepsis using hospital records, producing publication-ready tables, figures, and listings.
- Extract a complex patient cohort and perform sensitivity analysis on treatment sequences with automatic temporal data mapping.
- Generate comparative effectiveness evidence for a payer value dossier to support formulary access.
- Detect adverse event signals in post-market safety analyses across millions of patient records within days.
Limitations
- The tool requires access to structured clinical data sources such as EHR, claims, or registries and generates statistical code for analysis.
- The website emphasizes expert review and reproducibility, but does not disclose specific model names or version numbers.
- Public pricing, API availability, and mobile applications are not specified on the site.
as of 2026-08-11
Verification history
We have re-verified Frekil 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Frekil's pricing actually pencils out — and where peers do it cheaper.
Frekil's pricing is contact-sales only, making it a significant investment suited for enterprise pharma, CROs, and large healthcare organizations. For smaller teams or academic projects, the cost may be prohibitive compared to self-serve options like TriNetX or open-source R pipelines.
Setup time & first value
How long it actually takes to get something useful out of Frekil — broken out by persona, not the marketing-page minute.
For teams with data already in supported platforms (Databricks, Snowflake, AWS, etc.), initial setup typically takes a few days to connect data sources and configure ontologies. Once connected, you can run your first study within hours. Expect a week for full onboarding and validation.
Integrations
Resources & Guides
Tutorials & Learning
UX Roast #07 | Frekil :AI platform for medical imaging annotation and research #frekil #bottomlineUX
Saasfactor UX Design Agency
【3min Effekseer】01_1 サンプルを見る＆使ってみよう！ ヘルプをチュートリアル動画化：How make 3D effect tool tutorial. #shortsvideo
Worl Glish End
YOU MUST LEARN - Master Class Scratch Lesson 1 - How To Do A Delayed 2Click Flare ① - ディレイド2クリック
TAIJI official
Official links
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Featured Head-to-Head Comparisons
Frekil vs Codametrix
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Frekil vs Isomorphic Labs
Choose Frekil if your goal is to generate real-world evidence from clinical data quickly and transparently, especially for HEOR. Choose Isomorphic Labs if you're a pharma company seeking an AI partner for novel drug discovery. They serve entirely different stages of the drug lifecycle.
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