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See how Tnuki can 10x your sales

Sales intelligence for life sciences

Simple chat.
Deep intelligence.

Ask Tnuki anything about your life sciences market. Behind the chat, our purpose-built agents have integrated proprietary B2B datasets and 40+ scientific databases. In minutes, you get a table of who you should sell to, how to reach them, and what to say.

Start for free No signup, no credit card.
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Every data source you need, in one chat

Tnuki integrates proprietary B2B datasets, 40+ scientific & regulatory sources, and searches the live web, news, and filings.

1B+ Professional profiles Automatically verified email and phone
60M+ Companies Firmographics, funding, hiring
Live web and Press Releases GlobeNewswire, institutional directories, social
ClinicalTrials.gov Global clinical trial registry PubMed 37M+ biomedical citations NIH RePORTER NIH-funded research & grants openFDA FDA drug & safety data Drugs@FDA Every FDA-approved drug See all sources →
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Deep account research,
run in minutes

1

Chat in plain English

Start with a single sentence. Tnuki asks a couple of quick questions to zero in on your ideal customer — no filters, no database setup.

2

Sourced, enriched, and verified

Tnuki queries 40+ scientific and regulatory databases, proprietary B2B data, and the live web, then synthesizes a researched account and contact picture — and verifies every email and phone, automatically.

3

Every morning: the accounts heating up

Tnuki reads 1,000+ signal sources while you sleep, then surfaces the accounts moving in your market — the trigger, the context, and the decision-maker to reach. Your research engine never stops running.

tnuki.com
Proteomics for AI drug discovery
I sell high-plex proteomics and am looking for AI drug discovery companies
Researching high-plex proteomics... Gathering targeting details...
Which buyers should I target first?
Head of Platform Head of Discovery Head of CompBio Head of AI
Or type your own response...
I sell high-plex proteomics and am looking for AI drug discovery companies
Q: Which buyers should I target?
Head of AI, Head of CompBio
Q: What company segment?
Series B/C biotech
B2B database Web search Enriched 18
Found 18 AI & CompBio leads across Series B/C biotech.
Message Tnuki... Send
#CompanyPersonEmail Draft
Your enriched list will appear here
#CompanyPersonEmail Draft
1
Insitro
Insitroin
South San Francisco, CA
201-500↗ Growing
Machine-learning-driven drug discovery
Laura Whitfield
Laura Whitfieldin
South San Francisco, CA
l.whitfield@insitro.com
Head of AI
  • Builds ML models for target discovery
  • Curates multi-omics training data
  • Drives data-acquisition strategy
High-plex proteomics for your ML models
Hi Laura — Insitro's models are only as good as their training data. High-plex proteomics adds a functional protein layer to your multi-omics sets, surfacing targets genomics alone can miss...
2
Valo Health
Valo Healthin
Boston, MA
201-500↗ Growing
End-to-end AI drug discovery platform
James Nakamura
James Nakamurain
Boston, MA
j.nakamura@valohealth.com
Head of CompBio
  • Leads computational target ID
  • Integrates omics into models
  • Owns the data platform
Protein-level features for target ID
Hi James — As Valo scales computational discovery, high-plex proteomics gives your pipelines thousands of protein readouts per sample — richer signal than transcriptomics for predicting response...
3
VantAI
VantAIin
New York, NY
60-80↗ Growing
Generative AI for protein design
Priya Malhotra
Priya Malhotrain
New York, NY
p.malhotra@vant.ai
(212) 555-0193
Head of ML Platform
  • Runs the ML data platform
  • Sources high-dimensional data
  • Feeds protein-interaction models
Proteomic data for your platform
Hi Priya — VantAI's protein-design models thrive on dense protein data. High-plex proteomics profiles thousands of proteins per run, adding the functional signal structure-only models lack...
4
Insitro
Insitroin
South San Francisco, CA
201-500↗ Growing
Machine-learning-driven drug discovery
Sofia Reyes
Sofia Reyesin
South San Francisco, CA
s.reyes@insitro.com
VP, Machine Learning
  • Trains disease-prediction models
  • Evaluates new data modalities
  • Partners on data acquisition
A new data modality for Insitro
Hi Sofia — Curious whether proteomics is on your modality roadmap. High-plex panels produce model-ready protein matrices that complement your imaging and genomics for disease-state prediction...
5
Valo Health
Valo Healthin
Boston, MA
201-500↗ Growing
End-to-end AI drug discovery platform
Rachel Kim
Rachel Kimin
Boston, MA
r.kim@valohealth.com
(617) 555-0247
Head of Data Science
  • Builds target-discovery pipelines
  • Curates proteomic & genomic data
  • Validates model predictions
Validating predictions with proteomics
Hi Rachel — When a model nominates a target, high-plex proteomics confirms protein-level changes fast — closing the loop between prediction and validation across Valo's programs...
6
VantAI
VantAIin
New York, NY
60-80↗ Growing
Generative AI for protein design
Alex Torres
Alex Torresin
New York, NY
a.torres@vant.ai
Director, AI Discovery
  • Develops protein-interaction models
  • Needs deep proteomic features
  • Scales training datasets
Deeper proteomic features for discovery
Hi Alex — Generative protein models need rich training signal. High-plex proteomics delivers high-dimensional, quantitative protein data at scale — the features that sharpen molecular-glue discovery...
6:00 AM

Intelligence Brief

Spatial Biology · US West Pharma & Biotech

72 stories, 18 LinkedIn and X posts analyzed

FRI, APR 10

Sidewinder Therapeutics raises $137M Series B to push bispecific ADC pipeline toward IND
Apr 10 — Oversubscribed (Frazier + Novartis) backs a 3-program bispecific ADC pipeline led by SWT012. BioPharma Dive
Gilead to acquire ADC specialist Tubulis for up to $5B
Apr 8 — Validates ADC investment; combined oncology portfolio reshapes the tools budget picture over the next two quarters. Endpoints News
Genentech secures FDA label expansion for flagship oncology drug
Apr 9 — Expanded indication will pressure companion-Dx volume; three senior Dx hires posted this month confirm the staff-up. FDA label update
C4 Therapeutics × Roche expand DAC collaboration with $20M upfront
Apr 7 — TPD meets ADCs: Roche's DAC expansion is a near-term driver of target-expression and tissue selectivity studies. BioSpace
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Explore real demos

Each of these came from one simple Tnuki chat. Click any demo to explore the live result.

Try out our life sciences tools

Free to start

Start instantly for free. No sign-up, no credit card.

Tnuki Apollo Clay
Data freshness Real-time, live web Cached DB On-demand via providers
Coverage 1B+ people, 100M+ companies ~250M people Requires other providers
Setup time Minutes Weeks Weeks
Usable free tier Access for free
Fully conversational (chat-first)
AI email drafts Manual setup
Trigger-grounded personalization Manual setup

Competitor pricing and estimates reflect public pricing pages and vendor-disclosed figures.

Frequently asked questions

Common questions about Tnuki and deep account research. (Last updated July 2026)

What is Tnuki?
Tnuki is the sales intelligence platform built for life sciences — the depth of an institutional-grade research team, at the speed of a chat. Purpose-built agents read 40+ scientific and regulatory data sources (PubMed, ClinicalTrials.gov, openFDA, NIH RePORTER, and more), the open web, and fresh buying signals (funding rounds, clinical readouts, FDA actions, executive hires), then hand biotech and pharma sales, BD, and market intelligence teams a built target list, the accounts heating up, and the decision-makers to meet — plus account dossiers and outbound emails drafted in your voice. Tnuki is augmented, not autonomous: a human reviews and sends every email. Free to start, no signup, no credit card.
How is Tnuki different from using ChatGPT for sales research?
General assistants like ChatGPT are great at language, but for life sciences sales research they’re limited to the open web and their training data — no integrated scientific databases, no real-time clinical or regulatory signals, no verified contacts, and no memory of your accounts. Tnuki is a purpose-built research engine: its agents read 40+ scientific and regulatory data sources (PubMed, ClinicalTrials.gov, openFDA, and more) alongside the public web, monitor 1,000+ sources for buying triggers, verify decision-makers, and compile account dossiers and drafted outreach — all grounded in sources you can click to verify. You get the ease of a chat with depth a general model structurally can’t reach.
How is Tnuki different from Apollo, Clay, ZoomInfo, or AI SDRs?
Two camps, one gap. Apollo, ZoomInfo, and Clay are database-first: you build filters and workflows on top of a single generic dataset that has firmographics but not science — it can’t tell you a company’s lead asset, indication, trial phase, or latest readout. AI SDRs like 11x.ai ($5,000+/month) and Artisan are autonomous black boxes that optimize for volume on that same generic data. Tnuki is research-first, augmented, and built for life sciences: purpose-built agents read 40+ scientific and regulatory databases alongside the public web for the depth horizontal tools miss, while your team sees every search, enrichment, and draft and reviews and sends every email. You get an institutional-grade research team’s depth and AI speed without giving up control.
Is Tnuki only for life sciences?
Yes — Tnuki is purpose-built for life sciences sales. Coverage is deepest across biotech and pharma, and it extends naturally to the ecosystem that sells into them: diagnostics, lab tools and instruments, CROs and CDMOs, and scientific software. The agents are tuned to the data and buying signals that decide deals in this market — clinical and regulatory milestones, FDA actions, grant and funding activity, publication and KOL movement — so the research reflects how life sciences actually buys, not a generic firmographic profile.
What data sources does Tnuki use?
Tnuki goes well beyond a single database. Its agents read 40+ integrated scientific and regulatory data sources — PubMed, ClinicalTrials.gov, NIH RePORTER, openFDA, Semantic Scholar, and more — that horizontal tools like ZoomInfo or Apollo don't carry, alongside proprietary B2B data (1B+ professional profiles and 60M+ companies, with contacts verified across 10+ providers), the open web, news, funding databases, and 1,000+ live buying-signal sources. Everything is queried in real time, so the data is fresher and deeper than a static database, and every result links back to its source so your team can verify it.
Can Tnuki do market intelligence, not just prospecting?
Yes. Prospect lists are one output, but Tnuki is a deep-research engine for life sciences market intelligence too. The same purpose-built agents compile account dossiers, build market maps of a therapeutic area or modality, lay out competitive landscapes, and map the decision-makers and buying committees inside target accounts — in addition to producing a built target list, the accounts heating up, and ready-to-send outreach. It’s the depth of a research team, at the speed of a chat, whether you're building pipeline or sizing a market.
What is the Intelligence Brief?
The Intelligence Brief is a recurring, AI-generated email that monitors your life sciences market and target accounts. Tnuki's agents scan news, LinkedIn posts, job postings, SEC and FDA filings, funding databases, clinical-trial registries, and 40+ scientific and regulatory sources, then send you a concise executive briefing organized into what matters most: competitive moves, deals and funding, regulatory shifts, people moves, and general market news. Think of it as a research team that never sleeps, delivering the news that matters straight to your inbox — daily, weekly, or on your preferred cadence.
Is Tnuki free to use?
Yes. Tnuki has a free tier — you can use the Tnuki chat to research accounts, build a target list, verify decision-makers, and draft outreach, plus receive daily plays at a regular cadence. No signup, no credit card required. Paid plans (with higher-volume usage and more frequent plays) will be announced separately.

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