K Pro for teams
K Pro is an AI scientist for biopharma R&D. It works across multimodal data so your teams can move from question to evidence faster, without writing code.
How you'll use K Pro depends on your role. Find your team below to see the workflows it supports and the kinds of decisions it helps with.
Scientific & Translational teams
This is for you if you work in translational medicine, biomarker sciences, precision medicine, computational biology, or discovery biology, and you sit where early discovery meets clinical development.
What you're working toward
Discovering and validating biomarkers
Prioritizing targets
Connecting molecular biology to clinical outcomes
Where K Pro helps
Test biomarker hypotheses across multiple modalities in hours instead of weeks
Work with curated multimodal data, including MOSAIC real patient data, without spending weeks getting it analysis-ready
Run cross-modality analysis in one place instead of stitching tools together, for example a spatial transcriptomics plot in about a minute rather than three days
Review the literature at scale
Produce publication-ready evidence packages, with outputs traceable back to their source
Challenges this addresses
Exploring a single biomarker hypothesis across several modalities can take one to two months
Data is siloed and slow to bring to the right quality
Workflows are split across fragmented tools with no shared view
Literature review at scale is a bottleneck
Turning analysis into a clear evidence package adds days of effort
Clinical Development & Medical teams
This is for you if you lead clinical development or clinical science, work in medical affairs, or own clinical biomarker strategy, sitting between research and the clinic.
What you're working toward
Increasing probability of success
Accelerating biomarker strategy
De-risking late-stage decisions
Where K Pro helps
Build the multimodal patient evidence to define the right population before a trial starts, anchored on MOSAIC real patient data
Run responder analysis and multimodal feature discovery to understand who responds and why
Connect molecular signals to clinical outcomes without long bioinformatics turnaround
Assemble evidence packages for internal committees faster
Keep outputs interpretable and traceable to source data
K Pro informs and accelerates clinical strategy. It does not replace regulatory validation.
Challenges this addresses
Defining the right patient population often relies on incomplete biological evidence
Linking genomic features to response data can take months
Responder analysis is a bottleneck and expensive to outsource
Early go/no-go calls can feel like judgment calls without a real-world patient baseline
Evidence packages for committees take too long to assemble
Strategy, BD & Portfolio teams
This is for you if you work in portfolio strategy, external innovation, business development, or corporate strategy, bridging scientific teams and executive leadership.
What you're working toward
Maximizing portfolio value by prioritizing the right assets
Sizing the opportunity and validating clinical viability
Building a differentiated, defensible position
Where K Pro helps
Run competitive landscaping, indication expansion, and asset prioritization in one workflow
Turn weeks of manual diligence into structured, biology-grounded analysis
Surface white space by combining scientific, clinical, and competitive signals
Build investment cases faster, with outputs that are traceable, auditable, and ready to present to leadership
Challenges this addresses
Asset diligence is slow and manual, stitching together fragmented sources
True white space is hard to find when signals live in different places
Portfolio prioritization is inconsistent across teams using different assumptions and datasets
Competitive intelligence is reactive rather than continuous
Building investment cases takes weeks
AstraZeneca has licensed K Pro under a three-year agreement to build biopharma agents for competitive intelligence across pharmaceutical targets, assets, and trials.
Data, IT & Digital teams
This is for you if you lead data science, bioinformatics, AI/ML, or sit in data and digital leadership, owning how new platforms get integrated and governed.
What you're working toward
Reducing data friction and time to insight
Deploying AI securely at enterprise scale
Enabling teams to run analyses autonomously
Where K Pro helps
Deploy securely within your existing infrastructure
Integrate with your data stack as an intelligence layer rather than a replacement
Cut data onboarding from weeks to hours
Extend the platform with your own data (BYOD)
Keep work reproducible and auditable, with transparency and control over system behavior
K Pro integrates within enterprise IT infrastructure and decision workflows, as in the three-year AstraZeneca licensing agreement.
Challenges this addresses
Data preparation is the main bottleneck before any analysis can start
Every new tool adds integration, authentication, and governance work
Data science teams are overloaded with repetitive data requests
A lack of standardization across tools and data versions limits reproducibility and scale
Valuable internal datasets stay siloed and underused
Executive Leadership
This is for you if you sit at the top of the organization (CEO, CSO, CMO, EVP/SVP R&D, CDO) with final authority over R&D and AI strategy.
What you're working toward
Maximizing portfolio value and R&D productivity
Accelerating time to market
De-risking pipeline decisions to reduce late-stage failure
Where K Pro helps
Turn fragmented scientific data into faster, higher-confidence decisions, for example up to 30x faster target prioritization
Surface signal earlier to support go/no-go calls
Show enterprise-wide impact rather than isolated use cases
Give leadership a clearer view of where data and AI are improving speed, probability of success, and cost efficiency
Challenges this addresses
R&D productivity is under pressure from rising costs and high clinical attrition
High-stakes portfolio decisions are made under persistent uncertainty
Pipeline value is lost through late-stage failures that better signal could have flagged earlier
AI and digital investments are fragmented and don't always translate into measurable impact
There's no consolidated view of where AI is materially moving the needle
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