Consulting-grade CAR-T target evaluation for biotech founders — in weeks, not months. You know the biology. We help you prove the company. Ligant.ai runs the systematic evidence evaluation a top-tier firm charges $50K–$250K for, across 20+ biomedical databases, with every claim traced to its source.
30-minute live walkthrough. We’ll show you the agentic pipeline on BCMA and GPC3, and have a candid conversation about fit. Or apply for 500 free credits below.
You’ve spent a decade on a specific biology. You’ve identified the target. Now you need the evidence package an investor expects — genetics, expression, safety, competitive landscape, IP, TPP, market model — and you need it before you burn six months and a consulting retainer you don’t have.
No ISO standard, no FDA guideline, and no consensus SOP has ever defined computational target assessment for founders.
You bring the target. The platform runs the assessment. You make the call at each gate.
Each agent is a specialist. The orchestrator coordinates them through three decision gates. Your scientists review at every checkpoint. Nothing advances without human approval.
Agents don’t guess. They query TCGA, GTEx, HPA, UniProt, PDB, and 15 more authoritative databases. Every finding is cited to its source. When evidence is insufficient, agents flag uncertainty instead of fabricating.
Append-only evidence store. PROV-O-modeled provenance. Every query, tool call, and synthesis step is logged with timestamps and SHA-256 integrity hashes — the foundation for SOC 2 and 21 CFR Part 11 compliance on our post-MVP roadmap.
Agents do the work. Scientists make the calls. Human checkpoints are architectural gates. The pipeline halts until you review, adjust weights, and confirm. Decision-support, not decision-making.
A dedicated critique agent argues against the synthesis agent’s conclusions. Claims that fail citation verification are flagged, not delivered. Disagreement is a feature.
General-purpose AI hallucinates. In drug development, that’s not an inconvenience. It’s a risk. Ligant.ai uses a tiered model strategy: heavyweight reasoning for safety-critical assessments, mid-tier models for synthesis and competitive intelligence, lightweight models for high-throughput data retrieval. Model selection is based on catastrophic risk profile, not cost optimization.
A live status banner shows every database queried: green for success, yellow for partial, red for failed, gray for not applicable. You always know exactly what data informed the results and what was unavailable.
Claim-level citation verification. Interleaved reference-claim generation, not post-hoc citation. Synthesis agents compose only from retrieved evidence records — never from model parametric knowledge. When evidence is insufficient, agents flag uncertainty instead of fabricating.
When a database API fails or returns incomplete data, the pipeline continues, clearly marking what’s missing so you can calibrate confidence. No silent failures, no hidden gaps.
Automated fact-checking, cross-database validation, statistical correction (Benjamini-Hochberg), and human expert review work in sequence, so an error has to survive multiple independent checks to reach your team.
Append-only, cryptographically-integritied audit trails. Every query, every score, every human decision logged with timestamps and provenance. Your data never trains our models.
Ligant.ai is built for the domain experts closest to the science — the people who have already identified the target and now need to prove the company around it.
Pre-Series A founders validating a target hypothesis before writing the pitch deck. You’ve spent a decade on the biology; now you need the investor-grade evidence package.
Senior ex-pharma scientists joining an early team. Accelerate your own evaluation without retaining a consulting firm.
Rigorous second opinions for portfolio founders. Defensible, fully-cited evidence that backs — or challenges — the team’s thesis.
Coming in 2027: TCR-T, NK, ADC, and other cell therapy modalities. MVP focus: CAR-T.
We are a team of scientists and engineers who have lived the problem we are solving. We built Ligant.ai because computational target assessment deserves a rigorous, reproducible workflow, not a patchwork of spreadsheets and ad hoc scripts. We build for the scientific community because we are part of it.
The next era of drug discovery is defined by human-AI collaboration. We are building toward a world where computational validation precedes every wet-lab experiment, where AI agents handle the data-intensive work, and where scientists focus on the decisions that matter.
Compress months of manual target evaluation into hours of automated, reproducible computation. Speed without sacrificing rigor.
Validate hypotheses computationally before committing resources to the wet lab. The wet lab becomes a faster execution and confirmation step, not the starting point.
AI agents handle data retrieval, scoring, and synthesis. Scientists review, direct, and decide. Neither replaces the other.
No standard workflow for computational target assessment has ever existed. We are committed to building and validating the first one, with the scientific community.
We’re inviting a small cohort of CAR-T biotech founders to use the platform while we finalize the production release. 500 credits covers roughly a full target assessment across all three gates (Biology → Strategy → Economics), including the structured dossier and data-room-ready deliverables.
30-minute live walkthrough on BCMA and GPC3. Candid conversation about fit before any application.
Request a Demo CallFrom an idea about AI agents in life sciences to the first end-to-end pipeline for computational target assessment. Built methodically, validated rigorously.
Every claim in a Ligant.ai dossier traces back to an authoritative public source. These are the databases our agents query at runtime.
Genetics, drug-target associations, evidence aggregation across human disease biology.
Tumor-vs-normal expression across 44+ tissues, with protein-level evidence for safety calls.
Genome-scale essentiality screens across cancer cell lines to validate tumor dependency.
Every CAR-T trial worldwide, competitive landscape mapping, active program intelligence.
Pan-cancer genomic alteration context across published cohorts.
These are the public data foundations. We are building the agentic systems that make target assessment accessible, reproducible, and production-ready.
30-minute walkthrough of the agentic pipeline on BCMA and GPC3. Candid conversation about fit, where we could accelerate your work, and where we’re still maturing.
Already building? Apply for 500 free credits → · Want to talk directly? Contact us