Problem Who We Serve How It Works Platform The Difference About Schedule a Call

You have the data.
Now what?

The evidence intelligence platform for life sciences.
Built for what happens after the science is done.

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Structure Reason Recommend EVIDENCE INTELLIGENCE FOR LIFE SCIENCES.
// The Problem

Strong science doesn't guarantee patient impact.

The last mile of science is where breakthroughs stall. Not because the science failed, but because good science is simply not enough. To survive long enough to reach patients, a life sciences company needs to generate clinical evidence structured for each decision-maker. Every month it stalls, the company burns cash and patients wait.

40%
Of coverage applications denied — not the science, the evidence package
The test worked. The dossier didn't.
50–100+
Documents per submission, assembled manually every time
No system. No standard. No institutional memory.
$1M+
Unrealized revenue every month a proven test lacks coverage
Coverage is the revenue switch. Every month earlier you flip it compounds.
5–7 yrs
From validation to broad patient access
Most of it spent on evidence, not science

40%: AJMC, peer-reviewed analysis of Palmetto GBA MolDx decisions. 50–100+ documents: MolDx TA form requirements, Palmetto GBA; founder operational experience. $1M+: founder analysis based on pre-scale oncology diagnostic test economics (50–100 tests/day, $1,500–2,000/test reimbursement rate). 5–7 yrs: NIH SEED Reimbursement Knowledge Guide for Diagnostics, 2024; Health Affairs.

// Who we serve

Two buyers. One evidence problem.

Built for oncology and molecular diagnostics companies navigating payer coverage, market access, and due diligence — and the investors evaluating whether they’ll get there.

Evidence Generators — Medical Affairs · Market Access
01
Coverage Submission
MolDx · LCD · payer dossier
02
New Indication or Expansion
Coverage secured once. Now rebuilding from scratch.
Evidence Evaluators — Investors · Partners
03
Due Diligence
Evidence readiness · clinical risk · portfolio fit
04
Stakeholder Readiness
They need a structured evidence package. Your team is staring at a spreadsheet.
// How Savvyn Works

Four steps. One continuous loop.

01

Extract

Clinical studies, trial data, published literature — ingested from heterogeneous sources and made machine-readable.

What you have
02

Structure

Evidence mapped to payer frameworks, LCD requirements, and decision-maker standards of proof.

What it means
03

Connect

Gaps identified, weaknesses flagged, and evidence positioned against what coverage decisions actually require.

Where the gaps are
04

Surface

Prioritized actions and submission-ready outputs — so teams spend time executing, not rebuilding.

What to do next
// The Platform

One engine. One goal: the revenue event.

Your MolDx dossier, your NCCN submission, and your investor data room are built from the same evidence. Savvyn structures it once against each set of requirements and tells you exactly where it holds up, where it falls short, and what to prioritize next.

INPUTS
Trial Reports
Reg. Submissions
Publications
Grant Applications
Pitch Decks
Spreadsheets
Operational Data
EXTRACT what you have STRUCTURE what it means CONNECT where gaps are SURFACE what to do next CONTINUOUS LEARNING
DECISION FRAMEWORKS
Payer / Market Access
MolDx · LCD · commercial payer expansion
Clinical Guidelines
NCCN · ASCO · specialty society submissions
Due Diligence
Evidence readiness · clinical risk · portfolio fit
// The Difference

The knowledge was never on the internet.

The workflows connecting clinical evidence to payer policy to regulatory decisions were never written down. Savvyn encodes them.

Encoded Expertise

Built from the inside out.

The logic behind evidence decisions, gap analyses, and submission requirements — structured from lived expertise, not scraped from the web.

Always Current

Actively maintained.

Policies, frameworks, and requirements change constantly. Savvyn's knowledge base is actively maintained — so your intelligence never goes stale.

Domain-Native Reasoning

Made for life sciences.

Every reasoning layer reflects how evidence actually gets evaluated — not how a general model approximates it.

// About Savvyn

We've lived every side of this problem.

Most people in this industry have lived one side of the problem — the science, or the strategy, or the systems. We've lived all three. That's why we know exactly where it breaks.

Catherine Del Vecchio Fitz
Catherine Del Vecchio Fitz, PhD
Founder & CEO · Savvyn, PBC
Stanford PhD · MIT Postdoc · HBS PLD
LinkedIn →

Cancer biologist. Data systems architect. Commercial operator. I’ve worn every hat in this industry — and I know exactly where the system breaks.

I’ve implemented NLP pipelines in clinical contexts before LLMs existed. I know exactly why they weren’t good enough — and what it took to build something that is.

The AI capable of fixing the evidence gap didn’t exist three years ago. It does now. I’m building it to encode what no model has ever been trained on: the real workflows, the lived expertise, the knowledge that was never published. Because time is the most valuable resource we have — and I build systems that get proven innovations to patients smarter, faster, and more efficiently than anyone thought possible.

Savvyn is a nod to being savvy — and just a little bit savage about efficiency.

// Schedule a Call

Let's talk about your evidence challenge.

Tell us what you're working on. We'll be in touch.

Built for evidence generators and evaluators in

Oncology · Molecular Diagnostics

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Share a bit about your situation and we'll find a time to connect.