The agentic 360º platform for life sciences and health tech.

ARiDA is an intelligence and valuation platform built natively for the entire life sciences ecosystem. A team member defines the problem, and our specialized agents connect scientific discovery, market dynamics, and financial analysis in parallel to deliver decision-grade reports.

Beyond standard research, our proprietary graph database integrates these signals to power advanced analytics, like probability-of-success and risk-weighted valuations, giving the team the depth of a biostatistician and financial team in minutes.

See the canvas

ARiDA is integrated infrastructure built for high-stakes biopharma decisions: a cross-domain knowledge graph linking 20+ connected sources, an institutional-grade quantitative engine for valuation and risk, and agentic specialists that run in parallel against your question.

Where a standard chat centers on the message, ARiDA centers on the work deliverables: charts, tables, evidence packs, assumptions, and draft briefs appear as live canvas deliverables with source links, controls, and next actions attached. A team member can select a deliverable, inspect what supports it, change the view, ask a follow-up, or release it while the surrounding workspace stays intact.

Specialized AI search tools help teams find and summarize signals. ARiDA turns that work into governed workflows: competitive landscape, target selection, patent and IP scans, valuation, market monitoring, and standing-order briefs run as defined workstreams with source requirements, review gates, and reusable outputs.

ARiDA's advantage is the workflow library itself: purpose-built analytical programs shaped around how biopharma teams run diligence, strategy, and valuation work. Each workflow defines the evidence to gather, the comparisons to run, the checks to pass, and the deliverables to release.

Every finding includes direct source citations, and ARiDA's governed workflows require source-linked evidence before outputs are accepted. The backend is programmatically designed to check that claims, charts, tables, and model outputs carry the required source references, assumptions, and review context before they move into a deliverable.

Everyone from startup founders to BD directors, investors to clinical strategy leads. Medical affairs teams prepare submissions, executives track competitor moves, and consultants source valuation models — all through simple questions in natural language. At a typical 20-person biopharma startup, ARiDA replaces the need to hire a dedicated CI analyst or pay $50K-300K/year for an enterprise intelligence platform.

If you can write an email, you can use ARiDA. Write your brief in natural language, and the platform runs the research program automatically — querying databases, synthesizing evidence, and running valuation models in the background. Your first structured intelligence brief arrives in under 2 hours.

Two layers, one platform.

Layer 1 — Research Intelligence

• Clinical trials: ClinicalTrials.gov (AACT) — 500,000+ trials worldwide
• Compounds and bioactivities: ChEMBL — 2.4M compounds with activity data
• Drug targets: OpenTargets — genetics, druggability, and pathway data
• Proteins: UniProt — curated protein sequences and functional annotations
• Scientific literature: PubMed and PMC — peer-reviewed research and clinical evidence
• Funding and deals: NIH grant databases, company filings, and partnership signals
• Regulatory: FDA, EMA — approvals, warnings, and guidance documents
• Patents: USPTO, EPO, and WIPO — global patent landscapes

Layer 2 — Proprietary Knowledge Graph & Valuation Engine

• Bayesian probability-of-success (PoS) calculations — updated dynamically as new trial data emerges
• Risk-adjusted net present value (rNPV) models — the gold standard for asset valuation
• Real Options Analysis — binomial lattices, Monte Carlo simulations, compound option models
• Portfolio optimization — frontier analysis across multiple assets with correlation modeling
• BD deal simulation — probabilistic outcomes for licensing, acquisition, and partnership scenarios
• Sensitivity analysis — tornado charts, scenario matrices, and joint distributions for risk assessment

See the canvas

Data privacy is a core ARiDA platform feature. We have partnered with E2B through their startup program to ensure access to secure individualized sandboxes. Your research stays completely private. Your data is used only to deliver your workspace experience, and every workspace is encrypted and isolated from others. GDPR-compliant with SOC 2 certification. Your intelligence belongs only to you.

This is what separates ARiDA from every other AI tool in biopharma.

Probability of Success (PoS)

ARiDA calculates composite PoS using Bayesian methods combining historical success rates by therapeutic area and modality, target-level genetic validation (OpenTargets), compound-specific bioactivity data (ChEMBL), trial-level evidence (AACT), and your asset's specific interim data. As new evidence emerges, the PoS updates automatically.

Real Options Analysis

ARiDA quantifies the value of flexibility using binomial lattice models, Monte Carlo simulations, compound option models, and Greeks (Delta, Gamma, Vega, Theta). Output: a decision framework with probabilities and financial impact — “Proceed to Phase II” vs. “Defer 6 months” vs. “Abandon and reallocate.”

Yes. ARiDA can run technical scientific workflows inside a controlled Python sandbox with domain packages already available, including RDKit, Biopython, BioServices, biotite, ProDy, py3Dmol, GSEApy, GOATOOLS, scikit-learn, SciPy, statsmodels, PyMC, XGBoost, LightGBM, SHAP, lifelines, DuckDB, Polars, and PyArrow.

For scientists, that means ARiDA can help profile compounds from SMILES, calculate molecular descriptors, compare fingerprints, map scaffolds, inspect activity cliffs, run pathway and GO enrichment, analyze DNA or protein sequences, summarize protein structures, model survival endpoints, cluster experimental datasets, and generate figures or tables. It can also connect those analyses to ChEMBL, OpenTargets, UniProt, PubMed, PMC, and user-uploaded data so the output is grounded in both computation and source evidence.

• Compound profiling: “Profile this SMILES table for Lipinski, TPSA, LogP, scaffold diversity, and activity cliffs.”
• Pathway analysis: “Run GO and KEGG enrichment on this ranked gene list and summarize the strongest hypotheses.”
• Sequence analysis: “Compare these protein sequences and identify conserved motifs.”
• Target biology: “Analyze this compound series against ChEMBL activity data for EGFR.”

Yes. ARiDA combines historical AACT data, live ClinicalTrials.gov records, PubMed and PMC literature, NIH RePORTER grants, and web evidence to support clinical development work. It can compare endpoints, comparators, eligibility criteria, biomarkers, enrollment patterns, sponsor behavior, geography, and recent registry changes across similar trials.

For design work, ARiDA can run sample-size and power calculations, survival analysis, Kaplan-Meier curves, Cox models, randomization schedules, endpoint precedent reviews, and feasibility benchmarks. For KOL identification, it can rank investigators and institutions using publication footprint, trial participation, NIH funding, collaboration networks, and topic relevance. The result can be a trial design memo, feasibility report, KOL map, or evidence table with citations and caveats.

• Design benchmarking: “Benchmark Phase 2 trial designs in KRAS G12C NSCLC since 2020.”
• Power planning: “Find comparable trials for this endpoint and estimate sample size assumptions.”
• KOL mapping: “Map the top KOLs in NLRP3 inflammasome research using publications, trials, and NIH grants.”
• Protocol review: “Compare our draft protocol against recent AACT and ClinicalTrials.gov precedent.”