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The Status Quo

A researcher asks a question. Three weeks later, it's already been answered.

"Do neighborhoods with more green space have lower rates of childhood asthma?"

  1. 20h
    Literature Hunt Week 1

    Skimming 60+ papers to understand what's already been studied.

  2. 16h
    Data Search Week 2

    Finding EPA, Census, and CDC datasets across 4 repositories.

  3. 12h
    Setup & Code Week 2–3

    Writing boilerplate to join and clean the data.

  4. 8h
    The Experiment Week 3

    Running the actual analysis for the first time.

  5. Day 5
    Already answered. Week 3

    A 2019 Nature paper controlled for the same confounders.

56 hours

"I spent three weeks learning I was asking the wrong question."

The Problem

Science has a wasted-experiments problem.

Most of those 56 hours weren't science. They were setup — before a researcher could even know if the experiment was worth running.

01

Literature is vast

Millions of papers per year. No way to know what's been done without weeks of reading.

4M+ papers/year
02

Data is scattered

Relevant datasets live across dozens of repositories in incompatible formats.

No universal data layer
03

Every lab starts at zero

Same boilerplate rewritten every project. No shared memory. No compounding.

8M researchers, same overhead
The Solution

Not another chatbot. A research engine.

Generic AI

  • Hallucinated citations

    Fabricates papers that don't exist.

  • No real execution

    Plausible output, zero real code on real data.

  • Black box

    No audit trail. Can't build on what you can't see.

NeuriCo

  • Only real papers

    Every reference verified from live academic databases.

  • Verifiable code

    Actual execution on real datasets. Reproducible, auditable.

  • Human in the loop

    You review findings before NeuriCo proceeds. Judgment stays yours.

What you actually get

Hypothesis

Your question, plain English.

Lit Scan

What's done, what gaps remain.

Data Discovery

Finds & joins public datasets.

Analysis

Real code. Is there a signal?

Research Brief

Day one, not week three.

Market & Opportunity

The timing is now.

8M Addressable Users

Active researchers across AI, biology, public health, data science — all facing the same overhead.

10× Feedback Compression

"I wonder if…" to preliminary evidence in hours, not the two weeks needed to validate a hypothesis.

$sub Revenue Model

University lab subscriptions. 10 labs at $200/mo = $24k ARR. Scales with institutional budgets.

Why now

LLMs can finally run real code on real data. The loop between "I wonder if…" and "here's evidence" can be hours instead of weeks. NeuriCo is purpose-built to compress that loop — without replacing the researcher's judgment.