Mission Statement

We Surface Risk
Before You
Synthesize

NeurAeon compresses the slow, expensive work between a promising candidate molecule and a CDMO-ready synthesis package — surfacing known risk patterns and producing auditable, literature-grounded documentation. Assistive by design, human-governed at every gate, and pre-validation: our first benchmark metrics publish in Month 11.

Why We Exist

Month 11

First Benchmark Metrics Publish

The Imperative

Senior process chemists are scarce and expensive, and the work between “this molecule looks promising” and a documented package a CDMO can quote against is slow and easy to get wrong. We compress that iteration — without pretending to replace the expert judgment it still requires.

Late Discovery

Gaps in early CMC reasoning often surface only during physical testing, when the cost to correct them has multiplied.

Resource Waste

Failed batches and rework burn scarce time and capital that early-stage programs can least afford.

Time Inflation

A single avoidable iteration cycle can add months. Our aim is to compress that early reasoning loop — as input for expert review, not a replacement for it.

The Agentic Digital Lab

Specialist agents, adversarial review, and a human governance gate on every output.

Research Scientist

Reviews proposed synthetic routes and mechanism hypotheses against published literature.

QC / Analytical

Checks impurity-profile reasoning and flags missing analytical controls.

Toxicologist

Flags known genotoxic impurity-formation risks drawn from the literature.

PI / Lab Manager

Sequences the workflow and enforces the human governance gate before any output becomes a deliverable.

“These agents don’t replace scientists. They propose, flag, and document — at machine speed and scale — and a human reviewer approves every output before it leaves the building. The judgment stays with the expert.”

Governance & Guardrails

Every output passes through a deterministic governance layer before a human ever sees it — and a human governance gate before it ever becomes a deliverable.

This is not prompt engineering dressed up. The reasoning runs on a deterministic backbone with enforced output schemas, a claim-ceiling that caps what the system is allowed to assert, and a forbidden-knowledge denylist. The platform is advisory on regulatory framing; it does not certify GMP, FDA, EMA, or ICH compliance, and it won’t without the qualified people who own that work.

Deterministic guardrails and enforced output schemas
Claim-ceiling enforcement and a forbidden-knowledge denylist
SHA-256-chained, append-only audit trail (21 CFR Part 11–compatible)

Human Gate

Required on Every Output

Our Validation Targets

These are pre-validation targets for our Digital Shadow benchmark — not yet-achieved results. We publish the measured numbers in Month 11.

Target Risk-Flag Recall

On the Tier 3–4 failure modes that materially affect program outcomes

≤0

Iterations to Convergence

Target cycles to reach a literature-consistent route

Documentation Completeness

Target against our defined pre-lab package rubric

Build with us

We’re pre-validation and building in the open. If you’re a virtual biotech, a CDMO, or a process chemist who wants to pressure-test the approach with us, we’d like to talk.

Start a Conversation Meet The Team

We Surface Risk Before You Synthesize