AI-NATIVE PEPTIDE FOUNDRYBUILT BY ENGINEERS FROM GOOGLE · AMAZON · TESLA

Stop mass screening.
Ship a peptide in a week.

Brief us a target Monday. Get back IP-clean, manufacturable peptide sequences by Friday — pre-scored for binding, stability, and synthesis cost. Your wet lab runs the assays that prove it; we handle everything upstream.

BUILT FOR R&D IN
  • Food
  • Agriculture
  • Cosmetics
  • Industrial Enzymes
Brief Monday → ship Friday
1 week
Binding · Stability · Synthesis · IP
4 filters
Composition-of-matter, assigned to you
Full IP
Runs validation; we don't touch it
Your lab
SER-1LYS-4ARG-7PHE-10xy
CAND-042
ROT · 30°
1 nm
DOCK · CONVERGED
STRUCTURE ANALYTICS
CAND-042 · LIVE_
RESIDUES12
ΔG BINDING-10.0 kcal/mol
pLDDT (ESMFold)92.4
GRAVY INDEX-0.85
ISOELECTRIC pI6.2
NET CHARGE+1.0
SPPS MANUFACT.A
SOLUBILITYHIGH
SERUM t½27h
IP NOVELTY0.83
STATUS✓ ALL FILTERS PASS
01YOUR R&D REALITY

You don’t have an R&D problem. You have a search problem.

Every random-screening campaign is a lottery. Most hits fail at manufacturability. The rest get blocked on IP. Each cycle costs a budget line and a quarter of calendar. Your team isn’t slow — your search algorithm is. We replaced it with one that works.

BRUTE-FORCE SCREENING
LEGACY
Guess. Test. Burn cash. Repeat.
  • 10,000+ random sequences per campaign
  • 2–3 years of wet-lab screening
  • Hits collapse at stability, cost, or IP
  • Manufacturability discovered late
  • Opex-intensive, headcount-bound
COMPUTATIONAL DESIGN
FOUNDRY
Design it. Verify it. Ship it.
  • Targeted generation of novel geometries
  • 1-week closed-loop computational sprint
  • Filtered for stability, cost, and IP up front
  • SPPS-native sequences only
  • One design engine, not a lab army
02THE ARCHITECTURE

Candidates that cleared your filters
before we sent them.

Generative diffusion. LLM-orchestrated multi-agent search. Evolutionary computation. Bayesian optimization. Four disciplines, one closed loop — all aimed at your target, your receptor, your manufacturing envelope. Not a wrapped LLM. Not a single diffusion model. A choreography.

GEN 000
BEST ΔG
-4.00 kcal/mol
FITNESS · BEST / GEN
LIVE · POPULATION EVOLVING ACROSS GENERATIONS
01

Generative Diffusion

3D backbones, not sequences

Diffusion models propose novel geometries that dock with your target receptor. Structure first, language second — so the sequence is always serving a function, not the other way around.

SE(3)BackboneDocking
02

Inverse Folding

Structure → sequence

Each backbone is decoded into the amino-acid sequence most likely to fold back into it under your physiological conditions. The geometry survives the translation.

ProteinMPNN-classFoldability
03

Multi-Agent Evolution

LLM-orchestrated populations

A swarm of LLM-backed specialist agents — each tuned to binding, solubility, charge, manufacturability, or IP novelty — evolves the population across generations. Selection runs against a fitness landscape built from your target constraints.

LLM agentsEvolutionary searchMulti-agentFitness landscape
04

Bayesian Validation

ΔG · half-life · manufacturability · IP

Every surviving candidate is scored against binding energy, conformational stability, serum or soil half-life, SPPS synthesis cost, and IP novelty. The Bayesian loop updates its priors and the next generation starts smarter.

Bayesian optimizationΔGHalf-lifeSPPS costIP novelty
Multi-agent LLM orchestration drives the population; Bayesian optimization steers the loop. Every generation is cheaper and more confident than the last. No humans in the critical path.
03THE SPRINT

Brief Monday.
Ship Friday.

Your internal R&D team runs physical assays to verify. We do the computational lifting. You lower risk without adding headcount.

MON01

Target briefing

30-minute call: receptor, binding constraints, manufacturing envelope, IP whitespace. We translate your brief into a formal optimization problem and lock the spec.

DELIVERABLE
Signed target spec
TUE02

Structural generation

Diffusion produces ~10⁶ candidate geometries against your target. Inverse folding decodes each into synthesizable sequences.

DELIVERABLE
Candidate library
WED – THU03

Multi-objective optimization

LLM multi-agent swarm + Bayesian loop compresses the library on ΔG, stability, soil/serum half-life, SPPS cost, and IP novelty — in parallel, not series.

DELIVERABLE
Top-N shortlist
FRI04

Delivery

IP-clean sequences + full computational dossier: predicted structure, binding rationale, stability profile, synthesis protocol, freedom-to-operate.

DELIVERABLE
Ready for your wet lab
06ANSWERS

Before the call,
the obvious questions.

Don’t see yours? Ask us directly. We respond the same business day.

NOW BOOKING Q2 PILOTS

Your first peptide.
One week away.

Scope a target with us on a 30-minute call. Brief us Monday, ship sequences Friday — your lab runs the assays that prove it.