TechBBQ 2026
Finalist
Copenhagen
NVIDIA
Inception Program
Member · 2026

Every pain drug in history was found by guessing. That is why the world still runs on opioids.

We removed the guessing.

Analgesia AI is an AI platform trained on the one thing no other drug company has: seven years of treating 7,000 pain patients ourselves — every formulation we gave, and exactly what happened to each person. Other AI learns from textbooks, public databases and animal studies. Ours learns from human outcomes.

The drug proves the data works. The engine turns it into a pipeline.

Entering Phase 2aFDA pathway initiated
Non-OpioidZero addiction risk
7,000+ Patients TreatedFirst-party proprietary clinical data
AI Drug Discovery PlatformTrained on our own patient outcomes
5 Pharma PatentsDrug IP · Prosecution-ready
4 AI PatentsPlatform IP · Prosecution-ready
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The Problem
Every year, physicians write tens of millions of opioid prescriptions — not because opioids are the best option, but because topical alternatives are ineffective, wear off in 1–2 hours, and nothing better has come to market in decades. The same molecules have been recycled since the 1940s. Not one new mechanism of action has reached patients.
Our response: We generated what doesn't exist elsewhere: seven years of proprietary clinical data from 7,000+ patients treated with topical pain formulations. From that first-party dataset, we developed a novel prodrug now entering FDA Phase 2a — and are building an AI platform designed to develop the next generation of topical pain treatments, filling a pipeline gap the industry has left open for a generation.

Lidocaine: 1948. Diclofenac: 1973.
Capsaicin: ancient. Menthol: ancient.
These are still the options.

The Two Inadequate Options Available Today
Option A — Topical Anesthetics & NSAIDs

Safe. Local. Gone in under two hours.

Lidocaine, diclofenac, menthol, capsaicin — the entire topical pain pharmacy. All deliver short-duration, surface-level relief. Lidocaine fades in 60–120 minutes. Diclofenac requires multiple daily applications. Menthol and capsaicin mask pain rather than treat it. None sustain therapeutic analgesia beyond a few hours.

The benefit
Targeted delivery. Minimal systemic absorption. Safe for most patient populations.
The failure
Short duration. Frequent reapplication. No mechanism for sustained local release. When they wear off, patients escalate to systemic medication.
or
Option B — Systemic Analgesics

Sustained relief. Whole-body exposure.

Opioids and high-dose oral NSAIDs can maintain pain control for 8–12 hours. But to treat a localized injury, they alter brain chemistry and flood every organ system.

The benefit
Prolonged analgesic coverage. Effective for severe acute pain. Widely available.
The failure
Opioids carry addiction risk, sedation, respiratory depression, and transfer through breast milk. NSAIDs impair coagulation and wound healing. Both expose the entire body to treat a local problem.

This is the gap we built our company to fill.

The Analgesic GapWhere safe local therapy ends and opioid prescriptions begin

Local anesthetic
Unmanaged window
Systemic escalation
0h
1h
2h
4h
8h
12h
24h
Today
Lidocaine
The Gap
→ Opioids or systemic NSAIDs
What's needed
Single application → continuous local analgesia, up to 12 hours
When local anesthesia fades, patients enter a window of acute, unmanaged pain — typically 1–2 hours post-application. This is the precise moment systemic opioids are most commonly prescribed. No currently approved topical product can sustain pain control through this window. The clinical need is not incremental improvement. It is a fundamentally new class of locally-acting, sustained-release analgesic.
The Scale of This Unmet Need
313M
Major surgical procedures per year globally
Lancet Commission on Global Surgery
125K
Annual opioid overdose deaths worldwide
World Health Organization, 2019
1–2 hrs
Duration of topical lidocaine — the global standard of care
Clinical consensus; FDA labeling
0
Topical products delivering both fast onset and sustained relief
FDA product database; clinical review
The Pipeline Problem

This isn't just one missing drug.
It's an entire category that doesn't exist.

Until January 2025, no new class of analgesic had been approved in over two decades. The pipeline for topical, locally-acting, non-opioid pain therapeutics remains nearly empty. Solving this requires more than a single product — it requires a platform capable of systematically designing and accelerating new pain drugs from the molecular level up.

AI has closed the chemistry gap. Not the translational gap.
AI has proven it can make drug-like molecules — Phase 1 pass rates of 80–90% against a historical ~50%. It has not proven it can pick molecules that work in humans — Phase 2 success sits at ~40%, indistinguishable from the historical ~37%. Pain is the worst case for that gap: the only endpoint is what the patient tells you, placebo response is large and variable, and validated mechanisms fail on endpoints.
Phase 1 success safety, drug-likeness
AI-discovered
85%
Historical
52%
Phase 2 success efficacy in humans
AI-discovered
40%
Historical
37%
Drug Discovery Today (2024); IQVIA 2019–23; ASCO 2026 pipeline analysis
A platform that predicts which candidate clears placebo for the duration that matters — and designs the trial that shows it — is worth a company. That platform needs human outcome data. For pain, that data does not exist in any public repository. We spent seven years generating it.
This Is Our Story
Our Story
This company didn't come from a pitch competition or a Silicon Valley lab. It came from a pharmacy bench — and seven years of watching patients walk in hurting and walk out with something that actually worked.
This is how a compounding pharmacy became an AI drug discovery company.
Compounding pharmacy laboratory
Our Laboratory · 2012–2019

It starts in a compounding pharmacy. Not the kind you're picturing — not a back room mixing creams.

Under FDA regulations, compounding pharmacies operating at this scale maintained clean rooms, analytical laboratories, and qualified clinical pharmacists and chemists on staff. In practical terms, this was closer to a small-scale pharmaceutical R&D facility than a high street chemist.

The patients came in with pain. Post-surgical patients who couldn't sleep. Women recovering from childbirth with no safe options because they were breastfeeding. People with chronic wounds that wouldn't heal and wouldn't stop hurting. They'd already tried everything on the pharmacy shelf — the lidocaine creams that wore off in an hour, the menthol gels that did almost nothing, the diclofenac that required four applications a day and still left them reaching for pills by evening.

The pharmacy didn't hand them a standard tube and wish them luck.

They formulated. Tested. Listened to what patients and their treating physicians reported back. Reformulated. Adjusted concentrations, carriers, penetration enhancers. Listened again. Every outcome was documented — what worked, what didn't, how long the relief lasted, what the side effects were. Then they reformulated again.

Clinical pharmacist and chemist reviewing formulation data

This went on for seven years. Over 7,000 patients. Thousands of formulation cycles. A feedback loop between bench chemistry and bedside reality that most pharmaceutical companies never build — because the economics don't justify it and the patience doesn't exist.

But something happened that doesn't usually happen in this industry. The formula converged. Not incrementally better — fundamentally different.

Chemist working with Unguator mixer and precision equipment

A single application that delivered pain relief lasting up to 12 hours. No systemic absorption. No opioid risk. Safe enough that nursing mothers could use it — breast milk transfer measured below detection threshold. And beyond just numbing: it reduced inflammation and promoted wound healing.

That wasn't supposed to be possible from a topical.

42% pain reduction vs placebo. p < 0.001.
Zero serious adverse events.
Published, peer-reviewed clinical trial · Mount Sinai Hospital · 2022

The formulation was tested in a randomized controlled trial at Mount Sinai Hospital — one of the top-ranked medical institutions in the country. Forty-six postpartum women. The results were published in a peer-reviewed journal. Statistically significant pain reduction. Clean safety profile. A single application lasting up to 12 hours.

In pharmaceutical terms, that's not a concept. That's a clinical asset.

"Most companies would have stopped there."

A validated formulation. A published trial at a world-class hospital. A clear path to licensing or acquisition. That's a reasonable exit for most ventures.

But the data told a bigger story.

Seven years of clinical intelligence sat in those files — dosing patterns, tolerability signals, efficacy curves across pain types, formulation adjustments and their outcomes. Not a summary. Not an abstract. The raw, patient-level data from 7,000+ treatments with novel topical pain formulations.

This was a proprietary dataset for topical analgesics that didn't exist anywhere else. Not in any public database. Not in any pharma company's archives. Because nobody else had spent seven years doing what this team had done.

The cohort was collected in licensed, regulated compounding practice under our own protocols. First-party. Structured. Longitudinal. Rights-clean. Every formulation, every dose, every time-stamped outcome — joinable to the Mount Sinai trial and to every trial that follows.

So instead of selling one drug, a different decision was made: develop the formulation into a novel chemical entity — a new prodrug with composition-of-matter patents, the kind of IP that underpins billion-dollar drug franchises and makes an asset genuinely acquirable — and simultaneously build an AI platform around that proprietary clinical dataset. Not to discover one drug. To generate a pipeline.

Asset 1 — The Drug

AAI-201

Novel prodrug. Not a reformulation. New chemical entity. Composition-of-matter IP. Validated in 7,000+ patients. Published clinical trial. Now entering FDA Phase 2a.

Asset 2 — The Engine

A translational engine for pain

Trained on seven years of our own patient outcomes. Give it the pain to be treated and how the drug should behave; it returns a ranked shortlist of molecules scored against what actually worked in our patients — and the design of the trial that proves it. The drug gets acquired. The engine stays and designs the next one.

"One company. Two assets. One dataset that took seven years and 7,000 patients to build — and that no competitor can replicate by starting today."

See Our Solution
The Solution
The platform already has proof. Its first drug, AAI-201: one application, twelve hours of pain relief. No opioids. No pills. No reapplication. Designed to replace the moment a doctor reaches for an opioid prescription — and its chemistry has already been tested in humans, in a randomised, placebo-controlled trial with Mount Sinai Hospital.
The Solution

What if one application could replace an opioid prescription?

AAI-201 · The first topical that lasts long enough to make opioids unnecessary

Today, when a lidocaine cream wears off after one or two hours, the only option for sustained pain relief is a pill — usually an opioid. AAI-201 eliminates that choice entirely. Apply it as a liquid. It gels on contact with skin and locks into place. Pain relief lasts up to 12 hours. The drug stays at the wound — nothing enters the bloodstream, nothing reaches the brain, nothing shows up in breast milk.

01

Apply it like a liquid.

Pour it on the wound. On contact with skin, it transforms into a gel and locks into place — no wrapping, no patches, no repeated application. It stays exactly where the pain is.

Replaces patches, creams, and reapplication
02

Two drugs in one molecule.

The moment it reaches the wound, the molecule splits into two active agents. One stops the pain immediately. The other reduces inflammation and accelerates healing. No other topical does both.

Pain relief + healing in a single application
03

12 hours. One application.

The gel creates a slow-release depot at the wound site. Pain relief is continuous for up to 12 hours — covering the entire window where opioids are currently prescribed. The drug never enters the bloodstream.

Eliminates the opioid decision point
2–3×
Faster onset than current topical anaesthetics
0
Systemic absorption — the drug stays at the wound, not in the body
<0.1 ppm
Breast milk transfer — safe for nursing mothers from day one
Clinical Evidence
Randomised, placebo-controlled trial with Mount Sinai Hospital — 42% pain reduction versus placebo, no serious side effects — on the gen-1 formulation behind AAI-201. Across 7,000+ patients over seven years: zero serious adverse events. Phase 2a is next, and it is instrumented: it tests AAI-201 and grows the training set. Before unblinding, the engine's prediction of the outcome is time-stamped and locked. We will be the first company to show a Phase 2 win and a calibrated prediction of it — side by side.

Validated in Mt. Sinai Hospital Clinical TrialRandomized, placebo-controlled · Gen-1 formulation behind AAI-201 · 46 postpartum women

Neonatal Intensive Care
Vol. 35 No. 1 · Winter 2022
Placebo Group · VAS Pain Score 78 ± 5
Treated Group · VAS Pain Score 35 ± 3
↓
42% pain reduction vs placebo p < 0.001
7,000+
Patients treated
2012–2019
0
Serious adverse
events reported
<0.1 ppm
Breast milk
transfer
12 hr
Single-dose
duration
The Engine
A translational engine, not a chemistry engine. Structure prediction is being commoditised. The defensible layer is human outcome data — plus models that predict which candidate beats placebo through a defined window, plus the trial machinery to prove it. That layer is what we own.
Input

The pain to be treated. How the drug should behave: onset, duration, where it acts, what it must never do.

Engine

Three engines. Target. Molecule + formulation. Translational. Every candidate graded against what worked in our own patients.

Output

A ranked shortlist of molecules to make and test — and the design of the trial that proves it: endpoint, duration window, enrichment, sites.

Engine A

Target & Mechanism

Peripheral only. Human DRG biology and a pain-restricted knowledge graph return ranked peripheral targets, each with a kill memo: why it died before, what must be true for it to live locally, and the 90-day experiment that would falsify it.

Engine B

Molecule, Prodrug & Formulation

In local pain the product is a delivery system with a molecule inside it. The design object is molecule plus formulation, never the ligand alone. Predicted local PK, activation kinetics, release profile, tissue residence, systemic exposure — with CMC veto in the loop.

Engine C

Translational & Clinical

Placebo propensity. Responder enrichment. Endpoint and duration design. Trial simulation returning the predicted drug–placebo delta and probability of success — per candidate, per surgical model, per patient. This is the engine trained on human outcomes.

The differentiator — nobody sells this for pain
Design Principles

Six rules the engine is built on.

01
Predict the clinic, not the assay.

The north-star metric is calibration of Phase 2 predictions — drug–placebo delta over a window in a named surgical model — not hit rate.

02
Human data is the moat. Models are rented.

We consume the best open and licensed structure, co-folding and ADMET models. Proprietary dollars go only to data nobody else has.

03
Every trial is a data-generation event.

Phase 2a is instrumented so it tests AAI-201 and grows the training set at the same time. Each trial makes the next drug cheaper.

04
Every prediction is locked before unblinding.

Time-stamped, versioned, pre-registered. Every experiment is ordered with a prediction attached and ingested within 48 hours.

05
Formulation and duration are first-class.

Duration and delivery beat novelty in local pain. The engine designs both — the molecule and the vehicle that holds it at the wound.

06
Regulatory-grade from day one.

Model cards, training-data manifests, versioned predictions, audit log — built to the FDA AI credibility framework. It is also what pharma diligence reads first.

The flywheel is an accounting identity.

Human outcome data trains a better reward model. A better reward model means a higher hit rate and fewer molecules per candidate. Cheaper candidates mean better-priced partnerships. Partnerships fund instrumented trials. Trials produce more human outcome data. If the three numbers do not improve, the platform story is false.

Human outcome data → reward model → hit rate → cheaper candidates → partnerships → instrumented trials → ↺
1
Months per partnerable candidateDesign-make-test cycle time, falling
2
Predicted-vs-observed drug–placebo deltaCalibration error, falling
3
Molecules passing target-product gatesShare of engine output that clears flux, irritation, cleavage — rising
Discipline

What we will not build.

A consumer or clinician chatbot that recommends pain treatments
A general-purpose structure-prediction or foundation model
A robotic-lab factory
A systemic me-too before a locally validated target exists
A worldwide exclusive on the engine to the first pharma that returns a call
Partners
Two audiences, one company. Pharma buys the asset on its clinical data. Investors buy the engine that produced it and will produce the next one. AI platforms get the one input their models cannot generate. The platform never substitutes for clinical evidence.
For Pharma

Buy the asset on its clinical data. Rent the intelligence that makes your pain trials succeed.

Pain trials fail on placebo, endpoints and site quality more than on pharmacology. Engine C predicts the drug–placebo delta before you enrol a patient, designs the endpoint and duration window, and tells you which patients to enrich for.

Walled-garden access. No weights leave. No patient rows leave. You get predictions, designs and rankings — audit-trailed to the FDA AI credibility framework.

Pain Trial Design StudioCandidate RankerFormulation Designer
For AI Platforms

We will not beat you at structure prediction. We don't try.

We consume the best open and licensed co-folding, structure and ADMET models — and add the one thing they cannot get: human pain outcome labels. Seven years, 7,000 patients, every formulation and every result, in the therapeutic area where AI's Phase 2 record is weakest.

Engine C is a reward model trained on clinical outcomes. We rent compute and frontier models. We own the data and the reward signal. That is the layer AI drug discovery is missing.

NVIDIA Inception memberHuman-outcome reward modelPeripheral pain labelsFrontier-model consumer
For Investors

Asset-led. Platform-amplified.

The hub owns the engine, the data and the engine patents. Each drug sits in its own spoke. A buyer can acquire a drug without acquiring the engine — and investors hold the engine through multiple asset exits.

AAI-201 is the spine of company value for the next 30 months. Every engine-nominated candidate after it is a second, third, fourth exit from the same machine.

Hub-and-spokeMultiple exits, one enginePre-committed gates
Corporate Structure

A buyer can acquire a drug without acquiring the engine. No single partner can option the whole engine.

Hub — Analgesia AI Ltd: engine, data rights, engine patents, reward-model improvements, all partner licences. Never sold with a spoke. Never exclusively optioned.

Spoke 1 — AAI-201: composition-of-matter and formulation IP, clinical data, CMC. Licensed or sold at Phase 2. The hub retains data-return rights.

Spoke 2+: each engine-nominated candidate gets its own vehicle at nomination, so a partner can buy exactly one program.

Grants are indication- and target-class-limited · data-return rights are a condition of every deal · no early sale of the dataset
HUB
Analgesia AI Ltd
engine · data rights
engine patents
SPOKE 1
AAI-201
NCE + formulation IP → licence or sale at Phase 2
SPOKE 2+
Follow-on NCEs
engine-nominated, chemically distinct
SPOKE 3
Formulation IP
505(b)(2) line extensions
SERVICES
Trial intelligence
licences · no weights · no patient rows
Business Model

Four revenue layers. Layers one and two fund the engine. Layer three produces the nine-figure events.

1
Pain-trial intelligence

Trial simulation, placebo and enrichment design, endpoint and duration modelling, site selection — for sponsors running pain trials on their own chemistry.

Early revenue · proof pharma pays · feeds the flywheel
2
Partnered programs

Target-to-candidate or candidate-to-Phase-2 design on a partner's chemistry. Upfront, milestones and royalties per program.

Pipeline breadth without our capital at risk
3
Owned assets

AAI-201 and every engine-nominated local NCE. Each in its own spoke. Licensed or sold to pharma at Phase 2 while the engine stays.

Nine-figure events · sets valuation
4
Formulation IP

Formulation and release-kinetics IP; 505(b)(2) line extensions for specialty pharma. Cheaper, faster, a separate buyer set.

Monetises Engine B outputs cheaply
The Market
Analgesia AI operates at the intersection of two high-growth markets: an $11 billion topical pain relief market that hasn't seen meaningful innovation in decades, and an AI drug discovery market growing at over 30% annually. The drug targets a market where a single product generates $575 million per year. The platform targets a market where companies with no approved drugs carry multi-billion-dollar valuations.
Two massive markets. Two revenue streams. One company at the intersection.
The Drug Market · AAI-201
TAMNon-Opioid Pain · Global
$45B → $70B by 2030
The global shift away from opioids across all pain categories. Growing 7.7% annually.
Grand View Research, 2024
SAMTopical Pain Relief · Global
$11B → $18B by 2034
The specific product category AAI-201 enters. Dominated by generic molecules from the 1940s.
Market.us, 2026
SOMObtainable · 2–3 Assets, Rx + OTC
$1B+
The leading local non-opioid product sold $575M in 2025 on 2011-era chemistry. Our target spans 2–3 assets across prescription and over-the-counter channels. SOM is a company target.
Public filings; company analysis
The Platform Market · AI Engine
TAMAI in Drug Discovery · Global
$3.8B → $15.2B by 2030
The fastest-growing segment in pharmaceutical R&D. Growing over 30% annually.
BCC Research, 2025
SAMAI in Topical & Pain Drug Design
Underserved Niche
Broad-spectrum platforms focus on oral oncology and CNS therapeutics. AI-driven design of topical and transdermal analgesic candidates is an unoccupied niche.
Grand View Research, 2025; company analysis
SOMPlatform Enterprise Value
$1.5B–$4.2B valuation range
Based on current market valuations of comparable AI drug discovery platforms — none of which have proprietary first-party clinical data or a clinical-stage drug asset.
See Company Comparables below

What a single product does in this space

Leading local non-opioid depot
$575M/yr
Surgeon-applied. 2011-era chemistry. No new molecule. Duration and delivery beat novelty — for over a decade.
FY2025 net sales, public filings
Lidocaine patch, at peak
$1B+
One molecule. One indication. Now generic. The category economics are proven; the open axis is duration beyond 72 hours.
Historical public filings
First new analgesic class in 20+ years
Jan 2025
FDA approval validated regulatory appetite for non-opioid innovation. Oral, not topical. Local pain remains the open box.
FDA approval, January 2025

These are single products with one molecule, one delivery format, and no engine generating the next candidate.

Every AI drug-discovery company valued above $1 billion has one of three things: a positive Phase 2, a repeatable engine with paying partners, or three big-pharma logos. None of them owns human pain outcome data. Our 28-month plan is sequenced to earn each in turn — and to be the first to show a Phase 2 win beside a calibrated prediction of it.

Why the Window Is Open Now

January 2025

NOPAIN Act

Separate Medicare reimbursement for non-opioid postsurgical therapies in outpatient settings. First structural payer incentive for non-opioid adoption at scale.

CMS Proposed Rule, 2024
2025–2026

FDA AI Credibility Framework

FDA's draft guidance on AI supporting regulatory decisions requires credibility assessment plans, documented training data and lifecycle governance. Trial-design AI must be validated and versioned. We are built to it from day one.

FDA Draft Guidance, January 2025; final expected 2026
Ongoing

125,000 Deaths/Year

Annual opioid overdose deaths worldwide. Regulatory pressure, hospital mandates, and payer incentives to find non-opioid alternatives have never been stronger.

World Health Organization, 2023

The category economics are proven: $575 million a year for a single local depot with 2011-era chemistry, over $1 billion at peak for a single lidocaine patch. AI drug-discovery companies with no human outcome data and no approved drug are valued at billions. Analgesia AI enters with a clinical-stage drug, a published trial, a first-party dataset of 7,000 patients, nine patent applications, and a translational engine designed to produce the next candidate — and the one after that.

Invest
The business is the engine, not one drug. Each candidate it produces is a separate asset, sold or licensed to pharma at Phase 2 while we keep the platform. AI drug-discovery companies with no human pain data are valued at billions today. This round: $8.9M at $36.1M pre-money. It funds the engine's first self-designed molecule, the AAI-201 Phase 2a result, and nine patent families — in 28 months.

The drug proves the data works.
The engine turns it into a pipeline.

AI has proven it can make drug-like molecules. It has not proven it can pick the ones that work in humans — Phase 2 success for AI-discovered drugs is indistinguishable from the historical base rate. That gap is the whole game, and pain is where it is widest. We are the only company positioned to close it for pain, because we own the one input that closes it: seven years of human outcomes on our own formulations, in 7,000 patients.

The engine already has proof. Its chemistry was tested in a randomised, placebo-controlled trial with Mount Sinai Hospital — 42% pain reduction against placebo, no serious side effects. AAI-201, the optimised successor, now enters Phase 2a. Phase 2a is where pharma buys non-opioid pain drugs. And this Phase 2a does something no pain trial has done: it runs beside a pre-registered engine prediction of its own result.

Two audiences, one company. Pharma buys the asset on its clinical data. Investors buy the engine that produced it and will produce the next one. The hub owns the engine; each drug sits in its own spoke. A buyer can take a drug without taking the engine. Investors hold the engine through every exit.

$8.9M
Raise · $36.1M Pre-Money
AI drug-discovery companies with no human outcome data and no clinical asset are valued at billions today. We enter with both.
$45B
Target Market
Non-opioid pain, global — growing to $70B by 2030. Topical analgesics alone: $11B, dominated by generics from the 1940s.
9
Patent Applications
5 pharma + 4 AI. Protecting the drug and the platform. Two independent IP portfolios. Prosecution-ready.
How We Expect to Be Valued

We state this ourselves rather than wait to be told.

Company value is the risk-adjusted value of AAI-201, plus option value on two to three engine follow-ons, plus a platform multiple earned rung by rung — as the engine nominates chemically distinct candidates that pass pre-registered gates, and as pharma programs sign.

AAI-201's risk-adjusted value today is the floor, not the ambition. A positive Phase 2a with a pre-registered platform prediction roughly doubles it and moves the company onto the first rung of the ladder.

Capital Discipline

OpEx is released against milestones, not calendar.

  • We rent compute and frontier models. We own the data and the reward model. No foundation-model training.
  • Engine build hard-capped at $2.4M through Phase 2a. Majority of funds to the asset.
  • Decision gates pre-committed and reported to the board quarterly. If Phase 2a misses, if the engine can't nominate, if calibration slips — the response is written down now.
  • Non-dilutive first. NIH HEAL / SBIR via the US C-Corp; Innovate UK, BII and EIS via the UK Ltd.

$8.9M. Three things in 28 months. At the end: human data from Phase 2a, a molecule the engine designed, and all nine patent families filed.

57.5% · $5.1M
Phase 2a and Patents
Runs the next human study of AAI-201 through to its result. Files all nine patent families.
26.5% · $2.4M
The Engine
Takes the AI from architecture to its first molecule, made and tested in the lab.
16.0% · $1.4M
Team and Operations
Core hires, clinical operations, legal and regulatory support across the 28 months.

The one metric. At the end of 28 months the company shows three things — human Phase 2a data, a molecule the engine designed and tested in the lab, and nine patent families filed — plus a pre-registered prediction of the human outcome beside the outcome itself. Every AI drug company valued above a billion can show a Phase 2 win or three pharma logos. We can be the first to show a Phase 2 win and a calibrated prediction of it.

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