Biopharma insights that others miss

Sleuth helps biopharma teams get to conviction faster, turning complex strategic questions into clear, evidence-backed answers in hours instead of months.

Non-obvious insights from complex data

The most consequential strategic decisions hinge on insights that sit across thousands of data points — connections that span biology, clinical evidence, competitive dynamics, and deal history. Sleuth surfaces the second- and third-order patterns across these domains, revealing undervalued assets, overlooked mechanisms, and competitive gaps.

Question to conviction in hours, not months

When a board question lands or a deal window opens, weeks of traditional analysis isn't an option. Sleuth delivers executive-ready analysis with the urgency the question demands, and keeps it current as the market moves, leadership pushes back, or new questions emerge. Living analyses, not static deliverables.

Analysis that reflects your thinking

Most strategic analysis arrives in a generic framework your team has to translate — remapping someone else's structure to your therapeutic priorities, your science, your competitive set, and your way of evaluating opportunities. Sleuth starts from your context, so the output reflects how your team actually thinks about the problem.

Latest from Sleuth Intelligence

Original biopharma analysis from the Sleuth team, applying our platform to the questions shaping the industry. Competitive landscapes, deal flow, portfolio strategy, and more.

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Platform

Where fragmented data becomes connected knowledge

Sleuth is built on a proprietary knowledge graph that continuously acquires, structures, and links information from across the biopharma ecosystem, so what you get is connected knowledge, not another data dump without context. Purpose-built for biopharma, not a general-purpose model guessing at science.

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A billion relationships, already mapped.

Drugs, trials, targets, patents, companies, and deals — connected and deduplicated across sources.

Comprehensive coverage, continuously updated.

Hundreds of sources refreshed daily. Including sell-side research, China pipelines, and early-stage signals most platforms miss.

Every insight traces to source.

Follow any conclusion back to the underlying document, paragraph, or data point.

Your data stays yours.

Proprietary queries and uploads are never shared or used to train models.

What teams are saying

"What makes Sleuth stand out is its rare combination of speed and credibility. The output is factual, well-structured, and decision-useful, making it a valuable input into internal discussions and executive conversations. It has raised our expectations for how quickly broad complexity can be translated into a clearer, more refined strategic narrative."

Alex Cogdill
Head of Strategic Initiatives
Daiichi Sankyo

“All the strategy that used to be proprietary to pharma — the ‘game within the game’ that actually drives indication decisions — Sleuth made that accessible to a nine-person biotech. I think it's the most disruptive, amazing thing for small organizations like us. A massive, massive unlock.”

Danny Wells
CEO
Epana Bio

How teams work with Sleuth

Sleuth Studio
×
Highest-conviction whitespace emerging across next-generation immune engager platforms
Dataset
Insights
Datasets
TCE Analysis
Complete
Yesterday 2:15 pm
White space × de-risking
Complete
3 hours ago
Pinned Items
Capability Profiles
Executive Summary
Main Takeaway

First-generation TCEs are constrained by toxicity, PK burden, and narrow therapeutic index, creating clear pressure for safer, more selective next-generation platforms.

Adoption of TCE Modalities: Asset Counts by Core Technology

Sleuth Studio

An analytical workspace your team can use every day. Go from a strategic question to bespoke datasets, structured analyses, and evidence-backed frameworks — all in one place, with every conclusion traceable to source.

Take the Tour
Discovery → Development

Traditional discovery dominates despite selectivity limits

Classical methods dominate late-stage; modern techniques remain preclinical

Target Landscape

AML targets are fragmented with few validated leaders

Top 6 targets account for only 16% of 957 programs

957 programs
FLT3
CD33
CD123
CLL-1
CD7
Bcl-2
WT1
NPM1
MSLN
MUC1
>60% clinical
40-60%
<40%
Pipeline Analysis

Cell therapy leads volume but trails in clinical maturity

957 programs across 6 modalities

6 modalities
Small Molecule
n=347
Cell Therapy
n=287
Ab / Bispecific
n=118
ADC
n=89
Degrader
n=38
Other
n=147
Preclinical
Phase I
Phase II
Phase III
Evidence Validation

Selectivity claims lack protein-level validation

Only 47% have protein + functional evidence

957 programs
All AML
957 (100%)
HSPC Evidence
743 (78%)
Protein-Level
641 (67%)
Functional
522 (55%)
Protein + Func
450 (47%)

Sleuth Concierge

A dedicated strategic team working alongside yours. Scoping questions, pressure-testing hypotheses, and producing board-ready deliverables you can stand behind — without the six-week timeline or the seven-figure invoice.

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Which ADC programs are targeting the same antigen as ours, and how differentiated is our linker-payload?
just now
SGet dataset
Count rows in the CSV
Ran a command, used Sleuth MCP integration
Got it — 16 programs across 11 columns. Confidence and a fact/interpretation flag come back per cell, so you can see which calls are sourced and which are inferred. HER2 ADC Landscape — Linker-Payload Differentiation (16 rows × 11 columns) Scope: HER2-directed ADCs in Phase 3 or approved. Excludes discontinued.
her2_adc_landscape.csv
asset_namesponsorstagelinker_payload dardifferentiation_vs_oursevidence
EnhertuDaiichi Sankyo / AstraZenecaApprovedCleavable GGFG tetrapeptide–DXd8.0Same payload class and linker; DAR 8 carries the ILD signalDemonstrated
KadcylaRocheApprovedNon-cleavable MCC thioether–DM13.5Non-cleavable, so no bystander kill in HER2-lowDemonstrated
RC48RemeGen / PfizerApproved (CN)Cleavable mc-Val-Cit–MMAE4.0Auristatin payload; no topo-1 cross-resistanceDemonstrated
SHR-A1811HengruiApproved (CN)Cleavable GGFG–SHR1692656.0Nearest topo-1 comparator; DAR 6 rather than 8Demonstrated
A166Kelun-BiotechApproved (CN)Cleavable Val-Cit–Duo-52.0Beat T-DM1 in Phase 3; positioned post-topo-1 ADCDemonstrated
DB-1303DualityBio / BioNTechPhase 3Cleavable GGFG–P10038.0Closest architectural analogue; China BLA acceptedReported
BL-M07D1Sichuan BailiPhase 3Cleavable cathepsin B–Ed-048.0DAR 8 topo-1; randomised head-to-head vs T-DM1Reported
ARX788Ambrx (J&J) / NovoCodexPhase 3Non-cleavable oxime–AS2691.9Site-specific DAR 2; no bystander effectReported
MRG002MiracogenPhase 3Cleavable vc–MMAE3.8Tubulin payload at low DAR; therapeutic-index playInferred
FS-1502Fosun / LigaChemPhase 3Cleavable β-glucuronide–MMAF2.0MMAF payload; minimal bystander penetrationInferred

Sleuth MCP / API

Access Sleuth's knowledge base, structured datasets, and analytical capabilities programmatically. Build biopharma intelligence into tools that your team already uses - Claude, ChatGPT, and your own agents.

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Custom Datasets

Sleuth builds and maintains custom datasets precisely for your questions. Millions of signals synthesized into columns you can't get anywhere else, fully traceable, delivered in the cadence and format you need.

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See what your team has been missing