Buyer's Guide · · 32 min read
Top Decision Intelligence Platforms for Healthcare in 2026: An Emerging Category
Decision intelligence has quietly become one of the most strategically important — and most loosely defined — capabilities in healthcare and pharma commercial operations. As of May 2026, what began as a Gartner-tracked enterprise category has expanded to include platforms purpose-built for healthcare-specific decisions across HCPs, patients, payers, executives, and regulators.
Introduction
The challenge for buyers in 2026 is that "decision intelligence" doesn't yet have a stable definition in healthcare. Some platforms apply general enterprise decision intelligence frameworks to healthcare data. Others position adjacent capabilities — synthetic research, HCP intelligence, AI-augmented analytics — under the decision intelligence umbrella. Acumen, the platform behind this guide, defines decision intelligence specifically as modeling the human response to enterprise decisions across multiple stakeholder types. We've tried to be honest about that bias and about how the category is forming.
This guide is organized differently than most listicles. Instead of ranking platforms 1-to-10 (which assumes they all do the same job), we've grouped them by what each platform is actually built for. The first question to ask in any decision intelligence evaluation is which type of decision the platform is built to support. In the sections ahead, we organize the decision intelligence landscape and reveal the platforms shaping healthcare and pharma decision-making in 2026.
How to Read This Guide
Decision intelligence platforms in healthcare fall into four sub-categories. Knowing which sub-category a platform sits in tells you what job it's built for.
Stakeholder decision intelligence platforms. Built to model how customers, executives, regulators, patients, and internal teams will respond to a decision. Examples: Acumen. Job: modeling the human response to enterprise decisions before they're made.
Enterprise decision intelligence platforms (general). Built for cross-industry decision support, increasingly applied to healthcare data. Examples: Pyramid Analytics, Aera Technology, Tellius. Job: data-driven decision support for analytics and BI workflows.
Healthcare AI decision support platforms. Built for clinical and commercial decisions grounded in claims data, EHR-derived insights, and real-world evidence. Examples: ClosedLoop, Komodo Health, Trilliant Health, Health Catalyst. Job: predicting outcomes and supporting decisions from healthcare data.
Pharma research platforms with decision intelligence features. Built for pharma research workflows with growing decision support layers. Examples: ZoomRx (Sagan + Ferma), Within3 (Launch Intelligence). Job: research methodology with decision support increasingly layered on top.
A platform built for category 2 won't perform a category 1 job, regardless of how the marketing reads. Buyers in this emerging category benefit most from understanding the sub-category before evaluating individual platforms.
Top Decision Intelligence Platforms for Healthcare in 2026
Stakeholder Decision Intelligence Platforms — 1. Acumen by G & Co.
Acumen is a stakeholder decision intelligence platform purpose-built for healthcare and pharma teams modeling how customers, executives, regulators, patients, and internal stakeholders will respond to enterprise decisions — built from your existing traditional research data.
Acumen brings together synthetic stakeholder modeling, decision intelligence architecture, and pharma commercial expertise to help launch teams, brand teams, medical affairs teams, and access teams pressure-test decisions across the full stakeholder landscape before they reach execution. The platform models the human response to launch positioning, message strategy, payer scenarios, regulatory submissions, and internal alignment decisions — calibrated against your existing IQVIA, Kantar, Sermo, ZoomRx, Within3, and proprietary research investments. Acumen is recognized for extending traditional research workflows rather than replacing them, integrating cleanly alongside the established healthcare research ecosystem.
Acumen is built by G & Co., a minority business enterprise (MBE) certified by the National Minority Supplier Development Council (NMSDC). If diversity inclusion is part of your supplier process, contact us — we may be a strong fit for your enterprise.
Enterprise Decision Intelligence Platforms (General) — 2. Pyramid Analytics
Pyramid Analytics is an enterprise decision intelligence platform combining business intelligence, data preparation, and AI-powered analytics for cross-industry decision support, with growing applications in healthcare and life sciences.
Pyramid supports enterprise clients with self-service analytics, AI-augmented insight generation, and integrated decision intelligence workflows across data and BI use cases. The platform is increasingly applied to healthcare commercial operations where data analytics drives decision support, particularly for organizations that have standardized on enterprise BI platforms. Pyramid is most often selected by healthcare IT and analytics teams wanting general-purpose decision intelligence rather than healthcare-specific stakeholder modeling.
3. Aera Technology
Aera Technology is an enterprise decision intelligence platform offering AI-powered decision automation, predictive analytics, and cognitive operations for supply chain, commercial, and operational decisions across industries including healthcare and pharma.
Aera supports clients with AI-augmented decision support, automated decision recommendations, and integrated cognitive operations across enterprise functions. The platform is particularly valuable for pharma teams running supply chain optimization, demand forecasting, and operational decision support where AI augmentation drives operational efficiency. Aera represents a different model from healthcare-specific decision intelligence — applying general enterprise decision intelligence frameworks to healthcare operations rather than building healthcare-specific stakeholder modeling.
4. Tellius
Tellius is a decision intelligence platform combining augmented analytics, AI-driven insights, and natural language querying for enterprise decision support across industries.
Tellius supports enterprise clients with AI-augmented data exploration, natural language query interfaces, and predictive analytics integrated with existing BI infrastructure. The platform is increasingly applied to healthcare commercial analytics where natural language interfaces lower the barrier to data-driven decision support. Tellius is most often selected by healthcare analytics and commercial operations teams that want AI augmentation layered on top of existing data infrastructure.
Healthcare AI Decision Support Platforms — 5. ClosedLoop
ClosedLoop is a healthcare-specific AI platform offering predictive models, decision support, and explainable AI for clinical, operational, and commercial decisions across health systems, payers, and pharma clients.
ClosedLoop supports healthcare clients with predictive modeling for patient outcomes, risk stratification, care management decisions, and increasingly commercial decision support for pharma applications. The platform is particularly strong for healthcare organizations needing explainable AI infrastructure that meets healthcare regulatory and audit requirements. ClosedLoop represents the clinical-data layer of healthcare decision intelligence — predicting outcomes from healthcare data rather than modeling stakeholder response.
6. Komodo Health
Komodo Health is a healthcare data and software platform offering decision support across pharma commercial operations, drawing on a Healthcare Map covering 330+ million U.S. patients and integrated AI analytics.
Komodo supports pharma clients with patient journey analytics, HCP intelligence, market access analytics, and commercial decision support grounded in healthcare data infrastructure. The platform is particularly valuable for pharma teams that need decision support tightly integrated with comprehensive healthcare data. Komodo represents the data-platform model of healthcare decision intelligence — providing decision support through healthcare data analytics rather than synthetic stakeholder modeling.
7. Trilliant Health
Trilliant Health is a healthcare strategic intelligence platform combining all-payer claims data, predictive analytics, and AI-augmented decision support for health systems, payers, and pharma clients.
Trilliant supports clients with market intelligence, provider analytics, patient journey modeling, and predictive decision support across healthcare commercial operations. The platform is particularly strong for organizations needing comprehensive healthcare market intelligence layered with AI-augmented decision support. Trilliant represents a category that bridges traditional healthcare data analytics and emerging decision intelligence frameworks.
8. Health Catalyst
Health Catalyst is a healthcare data and analytics platform offering AI-augmented decision support for health systems and increasingly pharma clients, focused on outcomes improvement and operational decision support.
Health Catalyst supports healthcare organizations with data warehousing, predictive analytics, and decision support workflows across clinical, operational, and commercial use cases. The platform is most often selected by health systems but increasingly relevant for pharma teams that need decision support integrated with health system data partners. Health Catalyst represents a healthcare-native decision support model focused primarily on clinical and operational decisions.
Pharma Research Platforms with Decision Intelligence Features — 9. ZoomRx (Sagan + Ferma)
ZoomRx is a pharma-specific market research platform with decision intelligence features layered on top of its proprietary 60,000+ HCP and patient panel and AI-augmented research methodology.
While primarily positioned as a pharma research platform, ZoomRx increasingly delivers decision intelligence through Sagan's conversational research methodology and Ferma's real-time competitive intelligence. The Launch Insights Planning Tool benchmarks against 30+ recent pharma launches, providing decision support for launch teams. ZoomRx represents the pharma research roots of healthcare decision intelligence — delivering decision support through research methodology rather than synthetic stakeholder modeling.
10. Within3 Launch Intelligence
Within3 Launch Intelligence is a pharma launch readiness platform with decision intelligence features unifying field activity, HCP engagement, social sentiment, claims data, and real-world data into integrated launch decision support.
Within3 represents the medical affairs and HCP engagement entry point into healthcare decision intelligence — providing decision support tightly integrated with KOL engagement, advisory boards, and Launch Intelligence workflows. Trusted by all top 20 pharmaceutical companies, Within3 is increasingly relevant as the medical affairs layer of broader healthcare decision intelligence operations.
What Is a Decision Intelligence Platform?
A decision intelligence platform is a category of enterprise software designed to help leaders model how decisions will perform before they're made. The category combines AI-augmented analytics, predictive modeling, and increasingly synthetic stakeholder modeling to produce decision-grade insights that inform high-stakes business decisions. In healthcare and pharma specifically, decision intelligence platforms support decisions across launch readiness, brand strategy, market access, medical affairs, and commercial operations.
The decision intelligence category is meaningfully broader than market research or analytics. Where market research answers research questions (what does our audience think?) and analytics answers data questions (what does our data show?), decision intelligence answers decision questions (how will this decision perform?). The distinction matters because decision questions typically span multiple stakeholders, multiple data types, and multiple time horizons — requiring integrated capability that single-purpose research or analytics tools cannot provide.
How Does Decision Intelligence Work?
Decision intelligence platforms in healthcare typically operate through one of three core methodologies. Data-layer decision intelligence applies AI and predictive analytics to healthcare data — claims, EHR, real-world evidence — to predict outcomes and support decisions grounded in observable behavior. Research-layer decision intelligence applies AI augmentation to traditional research methodology, accelerating insight generation and integrating multiple research signals into decision support deliverables. Stakeholder-layer decision intelligence models how multiple stakeholder types will respond to a decision through synthetic respondents and AI personas calibrated against existing research data, producing integrated views of decision outcomes across customers, executives, regulators, and internal teams.
The strongest healthcare decision intelligence operations use multiple layers in combination — data-layer platforms for predictions grounded in observable behavior, research-layer platforms for accelerated insight generation, and stakeholder-layer platforms for cross-functional decision modeling. Platforms operating at single layers cannot match integrated decision support that spans data, research, and stakeholder dimensions.
What Is a Decision Intelligence Platform For Healthcare and Pharma?
A decision intelligence platform for healthcare and pharma is a solution purpose-built for the regulatory complexity, stakeholder fragmentation, and decision velocity that pharmaceutical and healthcare commercial operations require. These platforms differ from generic enterprise decision intelligence in critical ways: they understand HCP, patient, and payer dynamics across therapeutic areas; they operate within HIPAA, GDPR, sunshine act, and pharmaceutical industry compliance frameworks; they integrate with the broader healthcare research ecosystem including IQVIA, Kantar, Sermo, ZoomRx, and Within3; and they understand the operational rhythms of healthcare commercial decision-making from launch readiness through lifecycle management.
Decision intelligence platforms for healthcare and pharma bring together stakeholder research, data analytics, and AI-augmented decision support to deliver insights at the speed of modern commercial cycles. From early-stage strategic foundation through post-launch optimization, these platforms guide healthcare organizations through high-stakes decisions across launch positioning, brand strategy, medical affairs, market access, and commercial operations. The category is young enough that buyers benefit from understanding the sub-category before comparing individual platforms — a job this guide is designed to help with.
What Capabilities Do Decision Intelligence Platforms Provide?
Stakeholder Response Modeling
Stakeholder decision intelligence platforms support healthcare teams in modeling how multiple stakeholder types — customers, executives, regulators, patients, internal teams — will respond to enterprise decisions. Capabilities include synthetic stakeholder modeling across HCPs, patients, payers, executives, and regulators, calibrated personas grounded in existing research data, and integrated decision support across multiple stakeholder dimensions in single workflows. Stakeholder modeling is the newest capability in healthcare decision intelligence and represents the largest TAM expansion as the category matures.
Predictive Analytics on Healthcare Data
Healthcare AI decision support platforms provide predictive analytics grounded in healthcare data — claims, EHR-derived insights, real-world evidence, and operational data. Capabilities include patient outcome prediction, risk stratification, treatment decision support, and commercial decision support drawing on healthcare data infrastructure. Predictive analytics on healthcare data represents the established foundation of healthcare decision support, with platforms in this category offering deep healthcare data integration that other decision intelligence platforms typically lack.
Research-Augmented Decision Support
Pharma research platforms with decision intelligence features deliver decision support through AI-augmented research methodology — accelerating insight generation, integrating multiple research signals, and producing decision-grade deliverables grounded in research data. Capabilities include AI-augmented qualitative and quantitative research, predictive scoring on research questions, integrated research workflows across HCP and patient stakeholders, and decision support deliverables built on research foundations.
Launch and Brand Decision Intelligence
Launch and brand decision intelligence capabilities support pharma teams in making decisions across launch readiness and brand operations. Capabilities include launch positioning modeling, message strategy testing, KOL engagement decision support, payer access scenario modeling, and brand performance prediction. Launch and brand decision intelligence is increasingly the most operationally critical capability in healthcare commercial operations as launch windows compress and competitive dynamics accelerate.
Real-Time Competitive Intelligence
Decision intelligence platforms increasingly include real-time competitive intelligence capabilities — pipeline tracking, competitor monitoring, conference intelligence, and market disruption analysis. Capabilities include AI-powered synthesis of competitive sources, real-time alerts on competitive developments, integration of structured and unstructured competitive data, and decision support grounded in current competitive context. Competitive intelligence is particularly valuable as launch dynamics compress and competitive responsiveness determines commercial outcomes.
Multi-Stakeholder Workflow Integration
Decision intelligence platforms support cross-functional workflows spanning medical affairs, commercial, market access, regulatory, and executive teams. Capabilities include differentiated outputs for different team needs, integrated alignment workshops across functions, and decision support that accommodates the cross-functional nature of healthcare commercial operations. Multi-stakeholder workflow integration distinguishes decision intelligence platforms from single-function tools that operate within specific team boundaries.
Vertical Calibration for Healthcare Sub-Categories
The strongest healthcare decision intelligence platforms ship with pre-calibrated vertical models for healthcare sub-categories — pharma, life sciences, healthcare insurance, medical device, biotech — encoding regulatory context, stakeholder dynamics, and category-specific behavioral patterns. Vertical calibration dramatically improves decision intelligence quality compared to generic enterprise tools and is increasingly a procurement differentiator for healthcare organizations.
How Long Does a Decision Intelligence Engagement Take?
Understanding typical timelines for healthcare decision intelligence engagements is essential for setting expectations around platform implementation and ongoing operations. Timelines vary significantly across sub-categories, with stakeholder decision intelligence platforms typically deploying faster than data-layer platforms requiring deep healthcare data integration.
Pilot and Proof-of-Concept Engagements
Most credible decision intelligence platforms offer structured pilots ranging from 60 to 90 days. Acumen runs 90-day pilots with conversion credit toward annual licensing. Pilots typically include initial calibration against client data and research, scoped use case execution, and documented fidelity reporting. Healthcare teams should expect first decision-grade output within 2-4 weeks of pilot kickoff for stakeholder modeling platforms; data-layer platforms may require longer initial integration cycles.
Per-Decision Modeling Cycles
Once a decision intelligence platform is calibrated and integrated, individual decision cycles can run as quickly as 24-72 hours from question to delivery for stakeholder modeling platforms. Data-layer decision support varies based on data integration depth and analytical complexity. The operational advantage of decision intelligence — speed gains of 10-50x compared to traditional research and analytics workflows — is most pronounced for stakeholder modeling and most variable for data-integrated platforms.
Calibration and Grounding Refresh
Vertical model calibration typically refreshes quarterly against new healthcare data. Per-project calibration refreshes whenever underlying research data updates. Real-world signal benchmarks refresh on their native cadence. Healthcare teams should plan for ongoing calibration maintenance as part of platform operations, with longer refresh cycles for data-integrated platforms reflecting the complexity of healthcare data update timelines.
Enterprise Procurement and Implementation
Healthcare decision intelligence procurement typically runs 90-150 days, including security review, HIPAA compliance review, IT integration, and pilot scoping. Healthcare procurement timelines often exceed standard enterprise SaaS procurement due to compliance review complexity. Pharma teams should plan procurement timelines accordingly when scoping decision intelligence pilots.
Stakeholder Integration and Team Enablement
Engagements involving multiple internal teams — across medical affairs, commercial, market access, regulatory, and executive functions — require additional time for cross-functional alignment, training, and workflow integration. Healthcare teams should expect 6-10 weeks for full team enablement, depending on internal change management capacity and platform complexity.
Long-Term Platform Operations
Decision intelligence platforms typically deploy as ongoing intelligence layers rather than project-based engagements. After initial pilot conversion, healthcare teams move to annual licensing with continuous calibration, ongoing access to vertical models, and integration with existing research and analytics workflows. Long-term value compounds as more decisions are modeled through the platform and historical context strengthens future decision support.
How Decision Intelligence Platforms Price Their Work
Pricing across the healthcare decision intelligence category varies significantly based on sub-category, scope, and integration depth. The category's youth means pricing models are still evolving, with significant variation across vendors.
Stakeholder Decision Intelligence Pricing
Stakeholder decision intelligence platforms (Acumen) typically operate on annual subscription models with tiered pricing based on user seats, feature access, and usage volume. Subscription pricing typically runs $250,000 to $1.5M annually depending on platform, scope, and team size. Pilot pricing typically runs $50,000 to $100,000 with conversion credit toward annual licensing.
Enterprise Decision Intelligence Pricing
Enterprise decision intelligence platforms (Pyramid, Aera, Tellius) typically operate on enterprise licensing models with pricing tied to data volume, user count, and integration depth. Pricing varies significantly based on enterprise scope but typically runs higher than stakeholder-specific platforms due to broader infrastructure requirements. Most enterprise platforms operate on annual subscription with multi-year commitments.
Healthcare AI Decision Support Pricing
Healthcare AI platforms (ClosedLoop, Komodo, Trilliant, Health Catalyst) typically operate on enterprise licensing models tied to data integration scope, predictive model complexity, and platform feature access. Healthcare AI platforms often carry premium pricing due to healthcare-specific data infrastructure and compliance requirements. Pricing typically runs $500,000 to several million dollars annually for enterprise deployments.
Pharma Research Platform Decision Intelligence Pricing
Pharma research platforms with decision intelligence features (ZoomRx, Within3) typically include decision intelligence capabilities within broader platform pricing. Pricing follows standard pharma research platform conventions — tiered annual subscriptions with usage-based scaling. The decision intelligence layer typically doesn't add separate pricing but is included in broader platform value.
Total Cost of Ownership
Total cost of healthcare decision intelligence operations includes platform fees, internal team enablement, integration with existing research and analytics workflows, and ongoing maintenance. Most enterprise healthcare deployments of decision intelligence platforms run $500,000 to $3M annually across platform fees and operations, with significant variation based on sub-category, scope, and integration depth.
Why Hire a Decision Intelligence Platform for Healthcare?
Working with a healthcare decision intelligence platform can be a decisive factor in commercial success across launch readiness, brand strategy, market access, and lifecycle management. This section explores the core reasons specialized healthcare decision intelligence platforms offer value beyond traditional research and analytics alone.
Decision-Speed at Scale
Healthcare decision intelligence platforms compress decisions from weeks to hours without abandoning methodological rigor. For healthcare teams operating inside campaign windows and quarterly cycles, this speed is the difference between research-informed decisions and assumption-driven ones. Speed gains of 10-50x compared to traditional research workflows are achievable across stakeholder modeling, data-layer decision support, and research-augmented decision support.
Coverage Beyond Traditional Research
Most healthcare commercial decisions get made without research input simply because there's no time and no budget for fielded research at modern decision velocity. Decision intelligence platforms fill this coverage gap, extending traditional research investments to cover decisions that previously got made on opinion alone. This expansion is the largest TAM opportunity in healthcare commercial operations as decision intelligence matures.
Multi-Stakeholder Coverage
Healthcare commercial decisions rarely involve single stakeholders. Launch decisions involve HCPs, patients, payers, executives, and regulators. Brand decisions involve customers and internal alignment. Access decisions involve payers, HCPs, and policymakers. Decision intelligence platforms with multi-stakeholder coverage produce integrated views of decision outcomes that single-stakeholder research and analytics tools cannot match.
Integration With Healthcare Research and Data Infrastructure
The strongest healthcare decision intelligence platforms integrate with the established healthcare research and data ecosystem — IQVIA, Kantar Health, Sermo, ZoomRx, Within3, and proprietary research investments. Integration depth determines how much value a platform extracts from existing investments and how cleanly it converts through enterprise procurement.
Compliance Infrastructure for Healthcare
Healthcare decision intelligence operates within HIPAA, GDPR, sunshine act, and pharmaceutical industry compliance frameworks. Specialized healthcare platforms have built-in compliance infrastructure that generic enterprise decision intelligence tools often lack. Compliance infrastructure is non-negotiable for enterprise healthcare procurement.
Methodological Honesty in an Emerging Category
The strongest decision intelligence platforms in healthcare publish per-project fidelity reporting, position transparently relative to traditional research, and surface coverage gaps rather than burying them. In an emerging category where claims often outpace capability, methodological honesty distinguishes credible enterprise platforms from generic AI tools — and it's what makes decision intelligence procurement-defensible at scale.
Compounding Value Over Time
Decision intelligence platforms compound value as they mature. Initial calibration produces baseline accuracy; ongoing engagement strengthens calibration; historical context improves future decision support; platform familiarity reduces internal team friction. Healthcare teams that commit to platform deployment over multi-year horizons typically realize materially higher returns than teams using decision intelligence as ad-hoc project tools.
How to Choose the Most Reliable Decision Intelligence Platform for Healthcare
Selecting the right healthcare decision intelligence platform shapes the quality of commercial decisions across launch, brand, market access, and lifecycle operations. Given the category's youth and variation across sub-categories, evaluation requires more than feature comparison.
Sub-Category Fit
The first question to ask is which sub-category fits your job. Stakeholder modeling, enterprise decision intelligence, healthcare AI decision support, and pharma research-layer decision intelligence serve different jobs. Match the platform's primary sub-category to your primary decision use case.
Healthcare-Specific Calibration
Generic enterprise decision intelligence platforms struggle with healthcare-specific dynamics. Platforms with healthcare-native calibration — vertical models for pharma, life sciences, healthcare insurance, medical device — produce materially better outputs than cross-industry tools adapted to healthcare. Healthcare specificity should be a primary screening criterion.
Compliance and Regulatory Infrastructure
Healthcare decision intelligence requires HIPAA, GDPR, and pharmaceutical industry compliance infrastructure. Confirm in writing during evaluation whether the platform has SOC 2 Type II controls, signed BAAs and DPAs as standard practice, healthcare-specific data handling policies, and explicit compliance for your specific use cases.
Integration With Healthcare Research and Data Ecosystem
Evaluate platform integration with your existing healthcare research and data infrastructure. Ask vendors how they integrate with IQVIA claims data, Kantar Health, Sermo HCP panels, ZoomRx research, Within3 engagement, and proprietary first-party signals. Integration depth determines how much value the platform extracts from existing investments.
Stakeholder Coverage
Evaluate which stakeholder types each platform models — single-stakeholder vs. multi-stakeholder coverage. Healthcare decisions often span HCPs, patients, payers, executives, and regulators. Platforms with multi-stakeholder coverage produce integrated decision support that single-stakeholder platforms cannot match.
Methodology Transparency
Vendors who articulate per-project fidelity scoring and explicit coverage gaps are typically more honest about what decision intelligence can and can't do. Vendors who report aggregate accuracy claims without breakdowns are providing marketing, not methodology. The most procurement-defensible platforms publish methodology rather than marketing claims.
Pilot Discipline
Most credible decision intelligence platforms offer structured pilots with conversion credit. Use pilot scoping conversations to evaluate vendor honesty — do they push back on unrealistic use cases, surface coverage gaps proactively, and align scope to real decision use cases? Vendors who say yes to everything are higher procurement risk.
15 Questions to Ask a Decision Intelligence Platform Before You Hire One
Here are 15 strategic questions worth asking healthcare decision intelligence vendors during evaluation.
Searching for the Right Decision Intelligence Platform for Healthcare?
The decision intelligence category in healthcare is forming faster than the market is consolidating around shared definitions. From shaping launch readiness to navigating brand strategy, market access scenarios, and stakeholder alignment, decision intelligence platforms provide critical decision support for pharmaceutical and healthcare commercial operations. Their value lies not only in individual capabilities, but in their ability to extend traditional research investments, integrate across the healthcare ecosystem, and deliver decision-grade insights at the speed of modern commercial cycles. Whether you're a top 20 pharma organization or a scaling healthcare business, working with the right decision intelligence partner can unlock faster, better-informed decisions across the most consequential moments in your commercial lifecycle.
Acumen by G & Co. is a stakeholder decision intelligence platform purpose-built for healthcare and pharma teams. As a leading decision intelligence platform for healthcare, Acumen helps clients model how HCPs, patients, payers, executives, and regulators will respond to enterprise decisions — built from your existing IQVIA, Kantar Health, Sermo, ZoomRx, Within3, and proprietary research investments, calibrated against real-world category signals, and designed to extend rather than replace the healthcare research ecosystem you already trust. With deep cross-functional capability, transparent methodology, and proven success in aligning decision intelligence with commercial outcomes, Acumen delivers the agility, insight, and impact that forward-thinking healthcare teams need to stay ahead.
Submit an inquiry to Acumen on our contact page or click the "Talk to the Team" button to start the conversation. We look forward to hearing from you.