Network design, orchestrated agents, latest models.
Built for drug-discovery rigor. Knowledge Graph extraction → LLM verification → agentic deep-dive → ranked outputs with evidence — at scale, reproducibly.
Network Design
Enhanced knowledge-graph network design — dense, typed, and queryable for multi-hop biology.
Agent Orchestration
Purpose-built workflows chain reasoning, retrieval, tool use, and verification.
Curated Agents
Scientifically curated agents encode deep domain knowledge across modalities and therapeutic areas.
Latest Models
Frontier LLMs routed per task — efficient, auditable, and cost-controlled.
Eight curated agents. One orchestration layer. Each runs as a FastAPI workflow with a deterministic interface.
Knowledge graph reasoning
Multi-hop traversal across targets, diseases, drugs, endpoints, and pathways.
Target identification
Genetic, druggability, IP, and safety priors triangulated into ranked candidate lists.
Indication discovery
From a single target, surface and prioritize additional indications.
Hypothesis validation
Stress-test hypotheses against KG and literature; produce a diligence dossier.
Trial design
Endpoint selection, phase-transition prediction, and competitor-aware positioning.
TPP audit
Compare defined Target Product Profiles against actual readouts; surface drift.
Safety assessment
On- and off-target liabilities, population signals, and competitor safety benchmarks.
Reports synthesis
Templated, diligence-grade reports with full provenance and source linking.