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A room full of target
experts, on demand.

Specialist AI agents for anyone working in or curious about adoptive cell therapy. Explore modalities, targets, safety, and the competitive landscape, pressure-test a hypothesis, or get a ranked, cited target shortlist from a disease. Every claim traced to its source across 23 biomedical databases.

Start free with 500 credits

500 free credits to get started. No credit card, no waitlist.

Live walkthrough: CAR-T target assessment, end-to-end

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Transparency in Practice

Every claim, traced to its source

General-purpose AI hallucinates. We built Ligant.ai so it can’t. Every number, every claim, every recommendation is anchored in a primary source. Every evidence we generate can be validated. Our goal is to reduce hallucination to zero, and every step we take is in service of that.

Ligant.ai chat interface showing a CAR-T target assessment for CD19 in B-ALL, with inline PMID citations and an Assets panel listing every PubMed and ClinicalTrials.gov source the agents queried
Transparency
Every agent step is visible. No black boxes between you and the evidence.
Provenance
Every datapoint links back to the database, paper, or trial it came from.
Citation
PMIDs, trial IDs, UniProt accessions: rendered inline, clickable, verifiable.
Accuracy
Claims that fail citation verification are flagged, not delivered to you.
Construct architecture comparison table with rationale, key risks, and inline PMID/PMC citations for every row
Structured Evidence

Comparison tables, fully sourced

When the platform compares construct architectures, patient populations, or competitor strategies, every cell is backed by a citation. No paraphrased summaries divorced from their origin.

Hypothesis Card showing the working hypothesis for adult relapsed/refractory B-ALL CAR-T target assessment, with founder rationale citing PMIDs and key questions to answer
Grounded Hypotheses

Your hypothesis, anchored in literature

Hypothesis cards capture indication, modality, treatment line, and founder rationale, with every claim cited, every assumption marked. Inferred fields are called out explicitly so nothing slips through unchecked.

Decision criteria panel listing CAR-T safety and efficacy thresholds: grade 3 CRS rate, MRD-negative CR rate, antigen escape thresholds, each with the published benchmark it was derived from
Verifiable Criteria

Decision thresholds, with their benchmarks

Every kill criterion (safety threshold, efficacy floor, competitive cutoff) is published alongside the benchmark it was derived from. You can audit, contest, or override any value before a gate decision is final.

Agent reasoning trace showing the build_universe gate review, with the universe of candidates, plan progression, and the Findings panel listing competitive landscape outputs with full provenance
Complete Audit Trail

Agent reasoning, visible end-to-end

Watch each agent plan, query, and synthesize in real time. Every intake, retrieval, and decision is logged into the Assets panel, a complete audit trail you can replay, share with collaborators, or hand to a regulator.

Zero hallucination is the destination, not the claim. No system gets to zero on day one. What we promise is the audit trail to detect it, the citation infrastructure to challenge it, and a roadmap that prioritizes accuracy over speed at every gate.

Want to see it on your own target?

Start free with 500 credits

500 free credits to get started. No credit card, no waitlist.

The Problem

Expert validation, out of reach when it matters most.

You’ve spent a decade on a specific biology. You’ve identified the target. Now you need expert eyes to pressure-test it (genetics, expression, safety, competitive landscape, IP, TPP, market model), and you need them before you commit months and capital to the wrong call. The experts are scarce, expensive, and slow to engage.

$50K to $250K
to retain experts to assess
a single target
8 to 16
weeks to get senior experts
engaged and through the work
0
ways to put a full expert bench
on demand, until now

No ISO standard, no FDA guideline, and no consensus SOP has ever defined computational target assessment for founders.

The Solution

From disease to defensible decision.

Start from a disease and modality and the agents identify, evaluate, and rank surface-protein targets into a shortlist, or bring a specific target to assess in depth. Either way, you brainstorm with a team of specialist agents through three decision gates. They do the evidence work; you make the call at every one.

01
Gate 1: Biological Viability
Human and mouse genetics, pathway context, DepMap essentiality, tumor-vs-normal expression across 44+ tissues, antigen density, surface accessibility. Kill early if the biology doesn’t support the target.
02
Gate 2: Strategic Viability
Organ-by-organ on-target/off-tumor risk across heart, brain, lung, liver, and kidney, screened at the highest rigor, because cell-therapy failures are so often safety failures. Plus FAERS cross-modality signals, CRS/ICANS modeling, every trial targeting the same antigen, IP chokepoints, differentiation thesis. Kill if the strategic case doesn’t hold.
03
Gate 3: Economic Viability
Target Product Profile against approved CAR-T benchmarks, COGS, IND-enabling plan, market sizing, capital requirements to PoC, reimbursement pathway. Advance if it’s buildable, fundable, and viable.
“You know the biology. We help you prove the company.”
The philosophy behind Ligant.ai
How It Works

Agents collaborate. Humans decide.

Each agent is a specialist. The orchestrator coordinates them through three decision gates. Your scientists review at every checkpoint. Nothing advances without human approval.

Agent
Intake
Capture target, indication, modality
→
Agents
Gate 1: Biology
Genetics, expression, essentiality
→
Human Gate
Review & Advance
Approve, pivot, or kill
→
Agents
Gate 2: Strategy
Safety, competition, IP
→
Human Gate
Review & Advance
Approve, pivot, or kill
→
Agents
Gate 3: Economics
TPP, COGS, market, plan
→
Human Gate
Evidence Summary
Cited, shareable export
Why Agentic AI

What makes agents different from a chatbot

Chatbot
You ask a question, you get an answer. No tools, no verification, no audit trail. You copy-paste into your next tool and start over.
Agentic AI
Agents use tools, query TCGA, GTEx, HPA, UniProt, PDB, ClinicalTrials.gov, and 17 more databases, pass structured results to each other, and produce verified outputs with full provenance. Every step is logged and traceable.
01

Every claim has a source

Agents don’t guess. They query TCGA, GTEx, HPA, UniProt, PDB, and 18 more authoritative databases. Every finding is cited to its source. When evidence is insufficient, agents flag uncertainty instead of fabricating.

02

Every action is logged

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.

03

Every decision is human

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.

04

Every synthesis is critiqued

A dedicated critique agent argues against the synthesis agent’s conclusions. Claims that fail citation verification are flagged, not delivered. Disagreement is a feature.

Under the Hood

23 databases. Tiered AI models. Full transparency.

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.

•

Data source transparency

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.

•

Built to reduce hallucination

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.

•

Graceful degradation

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.

•

Layered verification architecture

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.

Scientific accuracy is not a feature we added. It’s the architecture we built everything on.

Integrity-hashed audit trails

Append-only, cryptographically-integritied audit trails. Every query, every score, every human decision logged with timestamps and provenance. Your data never trains our models.

Built For

For everyone exploring adoptive cell therapy

Whether you’re building a company, evaluating a target, advising a team, or simply learning the field, Ligant gives you a room of expert agents to explore, question, and pressure-test ideas with.

Founders & scientific teams

Validate a target hypothesis and build the cited evidence to back it, from biology through strategy and economics, before you commit time and capital.

Researchers, students & the curious

Explore modalities, targets, safety, and the competitive landscape. Learn the field by brainstorming with expert agents, with every claim traceable to its source.

Investors & advisors

Run fast, defensible diligence or a rigorous second opinion, with a complete evidence trail you can stand behind.

Discovery & translational teams

Go from a disease to a ranked target shortlist, or pressure-test an internal candidate in depth across 23 biomedical databases.

Focused on adoptive cell therapy: CAR-T, TCR-T, and CAR-NK, with CAR-T as our deepest-validated modality. The agents do not cover gene editing or gene therapy.

About Us

Why we started Ligant.ai

Ligant.ai started in conversations with scientists in adoptive cell therapy. The pattern was always the same. Hours spent combing through PubMed, ClinicalTrials.gov, and a dozen other databases, hunting for the evidence behind a single target call. No standard process. No shared workflow. Just spreadsheets, browser tabs, and judgment under pressure.

The frameworks existed (GOT-IT, target product profiles, decision rubrics used at top pharma), but no infrastructure to actually run them end-to-end. The best practices lived in slide decks, not software. So scientists rebuilt the wheel on every target, every time.

We took the best of those frameworks and built a multi-agent workflow around them. Every claim traced to its source. Every assessment scored against the same rubric. Every decision backed by evidence, provenance, and confidence scientists can stand behind, not in weeks, but on a regular basis.

This is what Pharma 5.0 looks like to us. Human-machine collaboration that gives scientists the speed, accuracy, and confidence to move the science forward. We’re building agentic systems for adoptive cell therapy, target by target, to advance the field in the years ahead.

A.B. Modi, Founder of Ligant.ai
A.B. Modi
Founder

A.B. founded Ligant.ai after watching adoptive cell therapy scientists lose weeks of every program to manual evidence work. He started the company to give those scientists a rigorous, reproducible workflow for computational target assessment, and to put agentic AI to work on the problems that matter most in adoptive cell therapy.

Our Mission

Scientists building for scientists

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.

Our Vision: Pharma 5.0

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.

01

Accelerate assessment timelines

Compress months of manual target evaluation into hours of automated, reproducible computation. Speed without sacrificing rigor.

02

The dry lab as first pass

Validate hypotheses computationally before committing resources to the wet lab. The wet lab becomes a faster execution and confirmation step, not the starting point.

03

Human-AI collaboration with agentic systems

AI agents handle data retrieval, scoring, and synthesis. Scientists review, direct, and decide. Neither replaces the other.

04

Solving an industry problem

No standard workflow for computational target assessment has ever existed. We are committed to building and validating the first one, with the scientific community.

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Product updates, scientific deep-dives, and what we’re learning as we build the first systematic workflow for computational target assessment.

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Public Preview

Public preview: start free with 500 credits

The platform is now open to everyone, with no application or waitlist. Create an account and your 500 free credits are enough to get going: frame your hypothesis, generate your hypothesis card, and see the agents’ plan to execute the assessment. Start from a disease for a ranked shortlist, bring a target to assess, or explore the field and learn, then start brainstorming with the agents in minutes. What the public preview means →

What you get

  • 500 free credits to get going: frame your hypothesis and see the agents’ plan to execute the assessment
  • A working hypothesis card for your target and indication
  • An interactive workspace to brainstorm with a team of specialist agents
  • Bring your own data: upload prior screenings or docs (CSV, PDF, Excel) to factor into the analysis
  • Every claim cited to its source, with a complete audit trail
  • Decision briefs at each gate you reach: Biological, Strategic, Economic Viability
  • Shareable, cited evidence export when you’re ready

How it works

  • Create your account, no credit card required
  • Enter a disease and modality for a ranked shortlist, or a specific target to assess
  • Optionally upload your own data to factor into the analysis
  • Work through the gates with the agents, reviewing and deciding at each one

Start free today

500 free credits, no credit card, no waitlist. After that, pay-as-you-go credit packs, no retainer, no minimum. The agents are live now.

Start free with 500 credits

Prefer to talk first? Contact us.

Timeline

Two years in the making

From an idea about AI agents in life sciences to the first end-to-end pipeline for computational target assessment. Built methodically, validated rigorously.

July 2024
The Idea
AI agents for life sciences
H2 2024
AI Agent Experiments
Exploring agentic workflows for life sciences
H1 2025
AI Model Research
Domain-specific model strategy and evaluation
H2 2025
Agent Framework
Multi-agent orchestration, early benchmarks
Q1 2026
Target Assessment
Pipeline architecture and 23-database integration
Feb 2026
Ligant.ai Incorporated
The company is officially born
March 2026
Research Preview
500-credit preview with founder cohort
June 2026
Public Preview
Now live: 500 free credits, open to everyone
Q3 2026
Compliance & Validation
SOC 2, GxP audit trails
Late 2026
Production Launch

Now in public preview

The platform is live and open to everyone. Start free with 500 credits. No credit card, no waitlist.

Start free with 500 credits
Data Foundation

Built on the data foundations of biomedicine

Every claim in a Ligant.ai assessment traces back to an authoritative public source. These are the databases our agents query at runtime.

Open Targets

Genetics, drug-target associations, evidence aggregation across human disease biology.

Human Protein Atlas

Tumor-vs-normal expression across 44+ tissues, with protein-level evidence for safety calls.

DepMap

Genome-scale essentiality screens across cancer cell lines to validate tumor dependency.

ClinicalTrials.gov

Every CAR-T trial worldwide, competitive landscape mapping, active program intelligence.

cBioPortal

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.

Get Started

Step into the room.

Bring a target, a disease, or a question, and start brainstorming with a room of expert agents. 500 free credits to get going, with every claim traced to its source.

Start free with 500 credits

No credit card required. Want to talk first? Contact us