Delineate
AI evidence assembly and quantitative modeling for clinical pharmacology — MBMA databases, Project Optimus dose work, exposure-response
If your bottleneck is evidence assembly rather than modeling software, Delineate is worth a serious look. The proof points are specific — a 300-trial GLP-1/obesity MBMA database delivered NONMEM-ready with BLQ handling and a QC report, and an ADC decision engine on a three-analyte PopPK framework. Wanting your team to run its own statistical review isn't optional here; treat the output as a reviewed input, not a finished answer.
Verified 2d ago · liveness 68/100 · cite: rightaichoice.com/tools/delineate
- Clinical pharmacology and pharmacometrics groups building MBMA databases
- Biostatisticians doing quantitative evidence synthesis across a drug class
- Clinical development teams making go/no-go calls before Phase 3 spend
- Regulatory affairs preparing Project Optimus dose justification packages
- Patients seeking treatment information or trial enrollment
- Teams with no quantitative modeling function to consume model outputs
- Anyone needing a full-featured EDC — Delineate is design and analysis only
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Skip Delineate if you need an EDC or trial-operations system rather than design and analysis, or if no one on your team can consume a NONMEM-ready database or exposure-response framework.
Plot digitization is only as good as the source figure — low-resolution plots with ambiguous error bars can force manual rework that eats into the time you expected to save.
Pricing was not visible in this research pass, so cost fit can't be compared against named peers here. Scope the pilot against the analyst-months it replaces: Delineate's published proof point is a 300-trial GLP-1/obesity MBMA database delivered NONMEM-ready with BLQ handling and a QC report, versus roughly four to five months of internal analyst time. Fit is at the pharmacometrics-group level and above — teams running class-level meta-analyses and Project Optimus dose justification, not
In short
Delineate — AI evidence assembly and quantitative modeling for clinical pharmacology — MBMA databases, Project Optimus dose work, exposure-response. Best for Clinical pharmacology and pharmacometrics groups building MBMA databases, Biostatisticians doing quantitative evidence synthesis across a drug class, Clinical development teams making go/no-go calls before Phase 3 spend. Paid pricing.
What people actually say about Delineate — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
11 mentions across 6 sources (Hacker News, YouTube, Product Hunt, Stack Overflow, GitHub, Lemmy), 44 more we could not attribute · researched Sep 14, 2026.
Weighted by the 55 posts each of 6 sources contributed.
- +Purpose-built for the regulated clinical-trial lifecycle, not a repurposed generic AI writer
- +Dual independent extraction with formal arbitration addresses the classic single-extractor QC gap
- +Full audit trail on every extracted value supports submission defensibility
- +Integrates with Medidata Rave EDC, Veeva Vault Clinical, and CDISC SDTM exports
- +Dose-optimization workflows align with FDA Project Optimus requirements
- −No independent community review exists — all capability claims come from the vendor
- −Name collides with at least three unrelated products, making review-hunting genuinely confusing
- −Not a full EDC, so teams still need separate systems for data capture
- −No published pricing means procurement friction and unknown per-study costs
- −Listed as 'advanced' skill level, implying a real onboarding cost for non-statisticians
- • No public price list — sales-quote process adds procurement time
- • Custom agent builder locked behind the Professional tier
- • Integration setup with Medidata Rave or Veeva Vault likely requires vendor services
Viability Score
How well maintained and how widely used is Delineate? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Assemble evidence from FDA clinical pharmacology reviews
- Assemble evidence from EMA documents including EPARs
- Extract AdCom briefing documents and postmarket commitments
- Extract published literature, conference papers and posters
- Extract data tables and KM curves from source documents
- AI graph digitization of published plots including error bars and variance
- Output a structured, queryable dataset across any drug class
- Run Monte Carlo simulation across agents, indications, subgroups and endpoints
- Optimize dose in line with FDA Project Optimus expectations
- Benchmark exposure-response against the class-level evidence base
- Test whether a signal is differentiated or within class noise
- Harmonize trial-level and arm-level data to consistent endpoint definitions
- Dual independent extraction with formal arbitration
- Verify extracted values against original source documents
- Run cross-study consistency checks for covariates and endpoints
About Delineate
Delineate is built for pharma and biotech teams that need quantitative, evidence-based answers to high-stakes clinical development questions. It creates models from the totality of available evidence, in any therapeutic area, so development, regulatory and commercial groups act on quantitative insight in a fraction of the time. The homepage reports four of the top 10 pharma companies rely on it, and the company is backed by Y Combinator, MIT Sandbox and NIH Seed. The work is organized into six value streams: assemble evidence, model and simulate, optimize dose, benchmark asset, submission outputs and digitize plots. Evidence assembly pulls and structures FDA clinical pharmacology reviews, EMA documents including EPARs, published literature, AdCom briefing documents, conference papers and posters, data tables and KM curves into a queryable dataset across any drug class. Monte Carlo simulation runs across combinations of agents, indications, patient subgroups and endpoints so go/no-go calls happen before Phase 3 resources are committed. Dose work aligns with FDA Project Optimus expectations — characterizing efficacy and toxicity curves to find the dose that captures achievable efficacy while staying on the right side of the toxicity curve. Outputs include model diagnostics, exposure-response plots, indirect comparison frameworks and briefing memos formatted for FDA briefing documents, EMA scientific advice packages and HTA value dossiers. Underneath sits a graph-digitization pipeline that pulls trial-level PK, efficacy and safety data — including error bars and variance — straight from published figures. Published case studies cover a 300-trial GLP-1/obesity MBMA database delivered NONMEM-ready with BLQ handling, missing data flags, a data dictionary and a QC report, plus an ADC decision engine on a three-analyte PopPK framework (intact ADC, total antibody, free payload). It fits clinical pharmacologists, biostatisticians, clinical development teams, regulatory
Behind the Verdict
We'd reach for Delineate when the question is quantitative and the clock is short: is our exposure-response signal actually differentiated, or is it inside the noise the class already delivers? That's the exposure-response benchmarking job, and doing it manually means months of extraction before anyone can even start modeling. The same logic applies to Project Optimus dose justification, where you need efficacy and toxicity curves characterized well enough to defend a dose choice to FDA or EMA. The QC layer is where this tool earns trust or doesn't. Dual independent extraction with formal arbitration, source-level verification against the original documents, and an audit trail on every extracted value — that's the part reviewers should be probing in a demo. Ask specifically how conflicts between extractors get resolved and how a covariate mismatch across studies surfaces. Pass if your group has no quantitative modeling function to consume the outputs. A NONMEM-ready dataset is only useful to someone who runs NONMEM. Also pass if you need a full EDC — this is design and analysis, not data capture. Teams that want a single LLM to summarize a folder of PDFs will find the structured-dataset output heavier than they need. Compared with generic AI research assistants, the difference is the deliverable format: a data dictionary, missing data flags, BLQ handling and a QC report rather than prose. Compared with a CRO or internal evidence-synthesis group, the case is speed and reproducibility — though you still own the statistical review and the regulatory narrative built on top of it. One caveat worth stating plainly: the platform's value scales with the messiness of your target class. A drug class with few published trials and sparse figures gives the extraction pipeline
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Real-world workflow fit
Concrete scenarios for the personas Delineate actually fits — and what changes day-one when you adopt it.
Ingest FDA clinical pharmacology reviews, EMA documents and published literature across the competitive class, run dual independent extraction with arbitration, then digitize published PK and efficacy figures including error bars to assemble a trial-level database.
Outcome: A NONMEM-ready MBMA database with BLQ handling, missing data flags, a data dictionary and a QC report — the same artifact pattern as Delineate's published 300-trial GLP-1/obesity case study — ready for your own model fit in weeks rather than months.
Characterize efficacy and toxicity curves across the class evidence base while overlaying your own Phase 1/2 PK and efficacy data on the exposure-response landscape to test differentiation.
Outcome: An exposure-response plot set and dose rationale that identify the dose capturing achievable efficacy on the right side of the toxicity curve, packaged as a briefing memo for an FDA briefing document or EMA scientific advice package.
Assemble a cross-asset evidence base spanning FDA reviews, EMA documents and conference posters, then run Monte Carlo simulation across agents, indications, patient subgroups and endpoints.
Outcome: A living competitive-class model showing which indications carry a genuinely differentiated signal and which sit inside the noise the class already delivers, supported by indirect comparison frameworks you can carry into lifecycle planning.
Use Cases
- Build a NONMEM-ready MBMA database across a competitive class to support a Phase 3 go/no-go decision.
- Identify the dose that captures achievable efficacy while staying on the right side of the toxicity curve for a Project Optimus dose justification.
- Overlay your Phase 1/2 PK and efficacy data on the class exposure-response landscape to test whether your signal is genuinely differentiated.
- Digitize trial-level PK, efficacy and safety data from published figures, including error bars and variance.
- Assemble a cross-asset evidence base from FDA reviews, EMA documents and literature before modeling begins.
- Prepare exposure-response plots and indirect comparison frameworks for an FDA briefing document or EMA scientific advice package.
- Build an indication-prioritization model across a competitive class for commercial and lifecycle planning.
- Construct a three-analyte PopPK framework for an ADC asset where standard antibody frameworks do not apply.
Limitations
- Delineate covers trial design and analysis, not clinical trial operations — it is not an EDC and does not collect patient data or manage sites.
- The outputs are model inputs and submission artifacts: a regulator still reviews the model, and internal statistical and pharmacometric review stays your responsibility.
- Extraction quality depends on the source documents being available and legible, and published figures with poor resolution or ambiguous error bars are the hardest input — digitized variance is only as good as what the figure shows.
- The value only materializes if your organization has people who can consume NONMEM-ready databases and exposure-response frameworks; a team without that function will get artifacts it cannot use.
as of 2026-10-09
Verification history
We have re-verified Delineate 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Delineate's pricing actually pencils out — and where peers do it cheaper.
Pricing was not visible in this research pass, so cost fit can't be compared against named peers here. Scope the pilot against the analyst-months it replaces: Delineate's published proof point is a 300-trial GLP-1/obesity MBMA database delivered NONMEM-ready with BLQ handling and a QC report, versus roughly four to five months of internal analyst time. Fit is at the pharmacometrics-group level and above — teams running class-level meta-analyses and Project Optimus dose justification, not
Setup time & first value
How long it actually takes to get something useful out of Delineate — broken out by persona, not the marketing-page minute.
For a pharmacometrics group with an existing modeling environment (NONMEM or R), first value comes from pointing Delineate at a bounded source set — a single class, a defined document list — and validating extracted values against the originals. Teams defining a broader class build across hundreds of trials should expect a longer evidence-assembly phase before the first model run. Groups without
Switching to or from Delineate
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual literature extraction in Excel: hand Delineate the source PDFs and figures you would have transcribed, and keep the audit trail per value instead of a spreadsheet of citations.
- →From an internally curated class database: re-extract the same class with dual independent extraction and compare against your existing values to quantify what the manual pass missed.
- →From outsourced evidence synthesis vendors: run a bounded class through Delineate and benchmark turnaround and QC documentation against the vendor's prior deliverable.
- ↗To an in-house modeling-only stack: export the NONMEM-ready database, data dictionary and QC report and continue fitting models in your existing environment.
- ↗To a full clinical data platform: keep Delineate for evidence assembly and design analysis while moving patient-data collection and site management into an EDC.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Delineate”, and we withheld 6: 6 could not be judged, because “Delineate” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Delineate.
Official links
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Featured Head-to-Head Comparisons
Delineate vs Isomorphic Labs
If you need to create clinical trial protocols faster with compliance and integration into eClinical platforms, Delineate is the clear choice. Isomorphic Labs is for large pharma seeking AI-driven drug discovery partnerships—not a tool you can buy off the shelf. Choose based on your stage: protocol authoring (Delineate) vs. molecule design (Isomorphic).
Delineate vs Codametrix
CodaMetrix and Delineate serve entirely different healthcare domains: CodaMetrix is an enterprise medical coding automation platform for large health systems, while Delineate is a freemium AI tool for clinical trial design. Your choice depends solely on whether you need to optimize revenue cycle operations (choose CodaMetrix) or accelerate protocol creation (choose Delineate). There is no direct overlap.
Delineate vs Rapidsos
RapidSOS and Delineate serve entirely different markets—emergency response vs. clinical trial design. Neither is a substitute for the other. Choose RapidSOS if you are a public safety agency or enterprise needing real-time emergency intelligence with deep 911 integration. Choose Delineate if you are a clinical researcher aiming to accelerate protocol creation with AI compliance checklists. For cross-domain buyers, consider both tools for separate workflows.
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Owkin
Owkin's K Pro is an autonomous AI scientist agent for biopharma R&D, turning multimodal patient data into drug discovery and clinical trial
Insilico Medicine
Generative AI drug discovery suite spanning target ID, molecule design, biologics and clinical trial prediction.
Flatiron Health
Oncology real-world evidence platform combining high-fidelity patient data with AI-assisted cancer research insights.
Frequently Asked Questions
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