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Evidence-bound summary — expand sections for movement, risks, and signals.
Memo snapshot · Jul 2, 2026, 5:25 PM
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TL;DR
Seed (YC)Frekil - AI-Powered Real-World Evidence Infrastructure Accelerate Real-World Evidence (RWE) generation from months to minutes
Latest: Pre-seed. Investors: Y Combinator. (High).
Funding
Latest: Pre-seed. Investors: Y Combinator. (High).
Product / news
11 product/news‑styled row(s); headline risk without filings (High).
Verified facts
+5 more in Recent movement below
YC P25 real-world evidence infrastructure for life sciences.
Weak funding evidence: mention does not include a parsed raise amount/round and may refer to non-funding context.
No open roles indexed yet.
The index price and activity score are algorithmic estimates based on observed public company-level signals. They may be incomplete, stale, or inaccurate and are not investment, legal, tax, or business advice.
Source types found
Strongest / recent news-style rows
About / company · Mon, Jun 29, 09:21 AM · confidence 90%high quality
Wed, May 20, 05:07 PM · confidence 88%high quality
wikipedia · Mon, Jun 29, 10:39 AM · confidence 50%low quality
Newest first · 17 event(s)
Source: Blog
Essays on real-world evidence strategy, RWE methodology, and what we are learning while building the evidence infrastructure layer for life sciences.
Source: Homepage
Accelerate Real-World Evidence (RWE) generation from months to minutes. Self-improving AI agents test hypotheses, build cohorts, and run end to end studies, getting sharper with every run. All from your own clinical data.
Source: official_site
Frekil - AI-Powered Real-World Evidence Infrastructure Product Solutions Who We Serve About Blog Request a Demo Y Backed by Y Combinator RWE Generation in minutes. Test hypotheses, build cohorts and run end-to-end studies on your own clinical data with self-i…
Source: About / company
Frekil is the real-world evidence infrastructure for life sciences. We convert raw clinical data into regulatory-grade evidence in minutes. YC-backed.
Source: Blog / news
RWE timelines are not driven by the final statistical model. The delay comes from protocol specificity, data access, fit-for-purpose checks, cohort logic, and evidence packaging.
Source: Blog / news
Many pharma teams have tried generic AI for RWE and hit the same wall: fluent outputs without enough trust. Here is how Frekil thinks about what LLMs should and should not do.
Source: Blog / news
A practical guide to where RWE fits across discovery, trial design, study execution, regulatory strategy, launch, market access, and life-cycle management - and what can still go wrong.
Source: Blog / news
A plain-English technical guide to propensity scores for survival RWE, using a post-MI statin example to explain matching, weighting, estimands, survival curves, balance checks, and sensitivity analysis.
Source: Blog / news
FDA leaders are signaling that one strong pivotal trial plus confirmatory evidence should become the default approval path. That makes real-world evidence an earlier, more strategic part of development.
Source: Blog / news
External controls can help rare disease and oncology teams answer questions that a conventional randomized trial cannot always reach. The hard part is proving that the comparison is fair.
Source: Blog / news
A statistically significant result in observational data often is not. Here is the actual math of negative controls and empirical calibration - the empirical null model, the calibrated p-value formula, worked examples, and the assumption it all rests on.
Source: Blog / news
A practical walkthrough of causal inference for RWE: why naive comparisons fail, how DAGs clarify assumptions, and how target trial emulation turns messy data into a credible study design.
Source: Blog / news
A treatment can clear regulatory review and still face coverage friction. Real-world evidence helps answer the value, affordability, and effectiveness questions payers and HTA bodies actually decide on.
Source: Blog / news
Essays on real-world evidence strategy, RWE methodology, and what we are learning while building the evidence infrastructure layer for life sciences.
YC P25 real-world evidence infrastructure for life sciences.
2 row(s)
The company's own site — the authoritative description of what they sell and to whom. Marketing-controlled, so treat claims as positioning rather than verified traction.
Accelerate Real-World Evidence (RWE) generation from months to minutes. Self-improving AI agents test hypotheses, build cohorts, and run end to end studies, getting sharper with every run. All from your own clinical data.
Why it matters: Primary source — the company's own positioning; best read for what they sell and to whom, not for traction claims.
Open source ↗Frekil - AI-Powered Real-World Evidence Infrastructure Product Solutions Who We Serve About Blog Request a Demo Y Backed by Y Combinator RWE Generation in minutes. Test hypotheses, build cohorts and run end-to-end studies on your own clinical data with self-i…
Why it matters: Primary source — the company's own positioning; best read for what they sell and to whom, not for traction claims.
Open source ↗3 row(s)
Third-party press coverage. Independent reporting corroborates company claims; repeated coverage across outlets is a momentum signal.
Frekil is the real-world evidence infrastructure for life sciences. We convert raw clinical data into regulatory-grade evidence in minutes. YC-backed.
Why it matters: Independent coverage — third-party corroboration of company claims; recurring coverage indicates rising visibility.
Open source ↗Why it matters: Independent coverage — third-party corroboration of company claims; recurring coverage indicates rising visibility.
Open source ↗Why it matters: Independent coverage — third-party corroboration of company claims; recurring coverage indicates rising visibility.
Open source ↗1 row(s)
Funding announcements and investor-database records. The strongest public signal of capitalization: round, amount, and syndicate quality when disclosed.
YC P25 real-world evidence infrastructure for life sciences.
Why it matters: Funding signal — Pre-Seed per this source; verify against the linked original before relying on it.
Open source ↗11 row(s)
Company blog and newsletters. Shipping cadence and technical depth of posts hint at product velocity and team quality.
Essays on real-world evidence strategy, RWE methodology, and what we are learning while building the evidence infrastructure layer for life sciences.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗RWE timelines are not driven by the final statistical model. The delay comes from protocol specificity, data access, fit-for-purpose checks, cohort logic, and evidence packaging.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Many pharma teams have tried generic AI for RWE and hit the same wall: fluent outputs without enough trust. Here is how Frekil thinks about what LLMs should and should not do.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A practical guide to where RWE fits across discovery, trial design, study execution, regulatory strategy, launch, market access, and life-cycle management - and what can still go wrong.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A plain-English technical guide to propensity scores for survival RWE, using a post-MI statin example to explain matching, weighting, estimands, survival curves, balance checks, and sensitivity analysis.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗FDA leaders are signaling that one strong pivotal trial plus confirmatory evidence should become the default approval path. That makes real-world evidence an earlier, more strategic part of development.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗External controls can help rare disease and oncology teams answer questions that a conventional randomized trial cannot always reach. The hard part is proving that the comparison is fair.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A statistically significant result in observational data often is not. Here is the actual math of negative controls and empirical calibration - the empirical null model, the calibrated p-value formula, worked examples, and the assumption it all rests on.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A practical walkthrough of causal inference for RWE: why naive comparisons fail, how DAGs clarify assumptions, and how target trial emulation turns messy data into a credible study design.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A treatment can clear regulatory review and still face coverage friction. Real-world evidence helps answer the value, affordability, and effectiveness questions payers and HTA bodies actually decide on.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Essays on real-world evidence strategy, RWE methodology, and what we are learning while building the evidence infrastructure layer for life sciences.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Sign in as an active team member to view private notes, watchlist controls, transcript evidence, and interaction history.