Savana

Overview

Industry: Healthcare Technology / Clinical NLP & Real-World Evidence Founded: 2014 Headquarters: Madrid, Spain (with offices in Barcelona and presence in 16+ countries) Employees: 211 (as of 2024) Website: https://savanamed.com/

Savana is an international medical AI company that applies natural language processing and machine learning to unlock clinical value from unstructured electronic health record (EHR) text. Founded in 2014 and headquartered in Madrid, Savana has pioneered the concept of "Deep Real-World Evidence" (dRWE)—extracting actionable insights from the entirety of clinical notes rather than just structured fields. The company serves 22 of the top 50 pharmaceutical companies globally and has enabled more than 200 hospitals to extract insights from over 2 billion medical records across 10+ countries and 6 languages. Savana's proprietary EHRead® technology represents a core competitive advantage in the rapidly growing real-world evidence market, which is driving pharmaceutical research, clinical trial optimization, and regulatory decision-making.

Products & Services

Savana cNLP (Clinical NLP)

Description: API-first Clinical Natural Language Processing trained on multilingual EHRs. Transforms unstructured clinical notes into structured, analysis-ready variables with regulatory-grade precision. Supports extraction and coding to SNOMED CT, MedDRA, ICD-9/10, OMOP, and openEHR standards.
Target market: Pharmaceutical companies, biotech firms, contract research organizations (CROs), and health systems conducting clinical research and real-world evidence studies.
Pricing model: Not publicly disclosed; typically SaaS/API-based licensing with usage-based or subscription models.

Savana Manager Suite (SMS)

Description: SaaS platform for structuring hospital data and managing clinical datasets. Evolved from earlier "Savana Manager" offering, now includes advanced tools for clinical data management and workflow integration.
Target market: Healthcare institutions, hospital networks, and research organizations seeking to systematize and leverage their clinical data repositories.
Pricing model: Recurring SaaS subscription model (not publicly disclosed).

Savana Data Space (SDS)

Description: Federated collaboration platform enabling secure, privacy-preserving data sharing and analysis across multiple healthcare institutions without centralizing sensitive patient data.
Target market: Multi-center research networks, international healthcare consortia, and pharma companies conducting decentralized clinical research.
Pricing model: Enterprise/consortium licensing (not publicly disclosed).

Savana Next Generation Registries (NGR)

Description: AI-powered dynamic clinical registries that generate longitudinal, real-world evidence from EHR data. Enables ongoing, scalable collection of outcome data without manual abstraction.
Target market: Pharma companies tracking post-launch drug safety/efficacy, device manufacturers, and medical societies maintaining disease registries.
Pricing model: Not publicly disclosed; likely project-based or per-registry licensing.

Market Position & Industry Dynamics

Market segment: Clinical Natural Language Processing (NLP) and Real-World Evidence (RWE) solutions for life sciences and healthcare providers.

Estimated market share: Limited public data; Savana is one of 10-15 significant players in the clinical NLP/RWE space globally. Direct competitors include TriNetX, Aetion, LynxCare, Deep 6 AI, and Owkin. Market is highly fragmented but consolidating.

TAM: The broader Real-World Evidence Solutions Market was valued at USD 4.31 billion in 2023 and is projected to grow at a CAGR of 23.5% to reach USD 19.68 billion by 2032 (Grand View Research). The clinical NLP subset within RWE is smaller but faster-growing (25%+ annual growth), driven by adoption of unstructured data extraction.

SAM: Estimated USD 2-3 billion addressable to Savana within pharmaceutical R&D, clinical trial optimization, and regulatory submissions across Western Europe and North America—the company's current geographic focus.

Industry Trends:

Regulatory tailwind for RWE: FDA, EMA, and other regulators increasingly accept real-world evidence for drug approvals, label expansions, and post-market surveillance, creating sustained demand for high-quality RWE generation.
Clinical NLP adoption acceleration: Growing recognition that 80%+ of clinically relevant information in EHRs resides in free-text notes; AI-driven extraction is becoming table-stakes for competitive pharma research.
Federated data networks: Privacy regulations (GDPR, HIPAA) and decentralization trends favoring privacy-preserving platforms like federated learning networks over centralized data warehouses.
Drug discovery and early-stage research: RWE adoption moving upstream from late-stage trials (Phase III/IV) into early drug discovery and patient cohort identification—a nascent high-growth vector.
Consolidation and platform convergence: Large healthcare IT vendors (Epic, Cerner) and data platforms (Truveta, Datavant) are adding RWE capabilities, increasing competitive pressure on pure-play RWE specialists.

Key differentiators:

Multilingual, multi-center scale: Structured 5+ billion clinical documents across 6 languages (English, Spanish, French, German, Portuguese, Italian) and 10+ countries—claimed as the largest AI-enabled, multi-language, multi-center network globally.
Proprietary EHRead® technology: In-house NLP/ML engine trained exclusively on EHR data, providing regulatory-grade precision and domain-specific performance superiority over general LLMs.
Deep Real-World Evidence (dRWE) concept: Pioneered and trademarked "deepRWE," extracting insights from full clinical narrative complexity rather than structured fields alone; demonstrated at scale with GSK asthma study.
Scientific rigor: Methods are peer-reviewed, results published, and regulatory bodies engaged early—positioning Savana as a scientific/clinical partner rather than just a software vendor.
Pharma footprint: 22 of top 50 global pharma companies use Savana; strong traction in drug discovery and post-launch surveillance.

Positioning: Savana positions itself as the science-backed, multilingual RWE pioneer enabling faster, more accurate clinical research through AI-powered EHR analytics. Marketing emphasizes unstructured data richness, regulatory credibility, and global clinical network scale.

Leadership Team

Name	Title	Notable Background
Jorge Tello Guijarro	Founder & CEO	Co-founder with vision to unlock hidden clinical value in EHRs; drives strategic direction.
Miriam Rodríguez González	CEO (current)	Currently serving as CEO of Savana as of 2024, reinforcing leadership for global growth.
Alberto Giménez	Co-founder & CFO	Co-founder; oversees financial and operational strategy.
Ignacio Hernández Medrano (Dr.)	Co-founder & CMO	Chief Medical Officer; brings clinical and scientific credibility to RWE methodology validation.
Sara Martínez	Hospital Manager SE and Latam	Leads hospital partnerships and Latin American expansion.
Pablo Miner	CFO	Chief Financial Officer; financial management and investor relations.

Financials

Revenue: USD 36.6 million (2024, estimated per Getlatka). Prior year revenue data limited in public domain.
Funding: Total raised USD 71.7 million across 6+ funding rounds:
- Series A (Aug 2018): USD 4.42 million
- Series B (Oct 2020): USD 15 million, led by Cathay Innovation
- Series C (Apr 2022): USD 25 million, led by Conexo Ventures; co-investors: Knuru, Aldea Ventures, Cathay Innovation, Seaya
- Recent funding (2025): CDTI Innovación jointly with Seaya Ventures and Conexo Ventures: EUR 4.56 million (approx. USD 5 million) for clinical data platforms and AI development
- Other investors: Buenavista Equity Partners, Startup Creasphere
Valuation: Not publicly disclosed; estimated USD 200-300 million post-Series C (2022) based on typical VC fund sizing.
Profitability: Not publicly disclosed; company is likely pre-profitability or early-stage profitable given ongoing capital raises and 211-person headcount.

Competitive Landscape

Direct competitors:

TriNetX — Operates world's broadest federated RWD network (137 healthcare organizations, 17 countries as of Aug 2024); 2,025 academic citations, significantly more than competitors. Stronger in federated network scale; weaker in unstructured NLP depth.
Aetion — RWE analytics and study design platform; strong in observational study methodologies; smaller pharma footprint than Savana.
LynxCare — Focused on RWE and clinical NLP; directly comparable to Savana in NLP capabilities; smaller geographic footprint.
Deep 6 AI — Clinical trial AI and NLP specialist; strong in patient cohort identification; narrower scope than Savana's multi-application RWE platform.
Owkin — Federated learning platform for drug discovery and diagnostics; strong in privacy-preserving data science; different business model (federated learning vs. centralized NLP extraction).
Atropos Health — Newer AI RWE player (founded 2019) leveraging agentic AI and EHR-embedded evidence at point-of-care; positioning as real-time clinical decision support; recent USD 33 million raise signals strong momentum.
Truveta — Data platform company; broader healthcare data interoperability focus; adding RWE capabilities to compete with pure-play RWE vendors.

Competitive advantages:

Largest demonstrated multilingual, multi-center clinical data network (5+ billion structured documents).
Proprietary EHRead® NLP engine optimized for EHR/clinical domain.
Scientific credibility and regulatory traction (peer-reviewed publications, regulatory body engagement).
Strong pharma relationships and proven track record (22 of top 50 pharma companies; GSK partnership flagship).
Early mover advantage in dRWE concept.

Competitive vulnerabilities:

Smaller federated network scale vs. TriNetX (137 healthcare orgs vs. Savana's 200 hospitals); TriNetX's network effects and citation volume create research advantage.
Limited public US healthcare presence vs. competitors (70% of revenue from Europe/Latam, inferred from geographic focus).
Dependency on pharma partnerships; less diversified revenue than horizontally-integrated platforms (Truveta, Epic).
Newer competition (Atropos, Owkin) with strong funding and AI/ML hype momentum.
Language-specific NLP training limits scalability beyond 6 supported languages.

Strategic Assessment

Strengths

Proprietary multilingual NLP at scale: EHRead® trained on 5+ billion clinical documents across 6 languages and 10+ countries represents unmatched depth and breadth in real-world evidence extraction. Regulatory-grade accuracy and SNOMED CT/MedDRA coding provide competitive moat vs. general-purpose LLMs.
Deep pharma relationships and footprint: 22 of top 50 pharma companies actively using Savana; portfolio includes GSK, Sanofi, and other tier-1 life sciences firms. Established revenue generation and recurring contracts reduce customer acquisition risk.
Scientific credibility and methodological rigor: Peer-reviewed publications, regulatory body engagement, and "deepRWE" methodology trademark position Savana as a clinical partner, not just software vendor. Supports premium pricing and regulatory defensibility.
Consistent funding momentum and investor confidence: USD 71.7 million raised with continued investor backing (CDTI 2025, Cathay, Conexo, Seaya). Strong investors provide strategic value beyond capital.

Weaknesses

Smaller federated network scale vs. TriNetX: 200 hospitals and 10+ countries is significant but materially smaller than TriNetX's 137 healthcare organizations with 2,025 academic citations. Affects research volume and market perception of scale.
Geographic concentration and limited US healthcare presence: ~70% of identified customer engagement in Europe/Latam (inferred from office locations, Barcelona Health Hub partnership). Weak direct sales into top-tier US academic medical centers compared to US-based competitors.
Likely pre-profitability or low margins: 211 employees, USD 36.6 million revenue (2024) suggests operating margins under 10%; continued reliance on venture funding signals path to profitability is uncertain.
Language-only depth in 6 languages: Multilingual capability is strength but also constraint; no indication of Asian language support (Mandarin, Japanese) limiting addressable market in high-growth regions.
Product portfolio fragmentation: Multiple product lines (cNLP, Manager Suite, Data Space, NGR) create implementation complexity and sales cycles; unified platform narrative weaker than integrated competitors.

Opportunities

Upstream drug discovery and preclinical applications: Current focus on Phase III/IV trials and post-market surveillance; expanding into early-stage drug discovery (patient cohort identification, phenotyping, biomarker discovery) represents high-growth vector. Pharma focus on AI-driven discovery creates pull.
Real-world evidence for regulatory submissions: FDA, EMA increasingly accepting RWE for accelerated approvals and label expansions. Savana positioned to support this trend; growth vector is strong but execution-dependent.
Genomics and biomarker integration: Series C funding explicitly targeted adding genomics, biomolecular, and imaging data layers. Genomic+clinical data fusion is nascent, high-value capability; early-mover advantage possible if executed well.
International geographic expansion: Strong Europe/Latam footprint; significant whitespace in Asia-Pacific (China, Japan, Singapore) and other international markets. Localization investments would unlock massive TAM expansion.
Expansion into medical devices and diagnostics: Core cNLP methodology applicable to post-market device surveillance and diagnostic tool validation; underexploited adjacent market.
Consolidation and M&A as acquirer: Strong funding, scientific IP, and pharma relationships position Savana as acquisition target for large healthcare IT platforms (Epic, Cerner, Veradigm) or data brokers (Datavant, Truveta), OR as acquirer of complementary AI/RWE capabilities.

Threats

TriNetX and federated network dominance: TriNetX's 2,025 academic citations and broader healthcare network (137 organizations vs. Savana's ~200 hospitals) create research data advantage and network effects. Risk that TriNetX becomes standard in academic/hospital RWE generation.
Large platform consolidation: Epic, Cerner, and other EHR vendors are incorporating RWE analytics natively. Structural commoditization risk as EHR vendors add Savana-like capabilities to core platform, undermining standalone RWE vendor premium.
Atropos Health and newer competitors with strong funding: Atropos (USD 33 million raised recently, agentic AI at point-of-care) and Owkin (federated learning model) represent well-funded competitive threats. Atropos especially concerning due to positioning in physician workflow integration and Microsoft partnership.
LLM commoditization of NLP: General-purpose LLMs (GPT-4, Claude, etc.) increasingly capable of clinical text extraction; open-source clinical models (BioBERT, BlueBERT) democratizing NLP. Savana's proprietary EHRead® differentiation at risk of erosion.
Regulatory uncertainty on RWE validity: If regulators tighten RWE evidentiary standards or reject major RWE-based submissions, demand for RWE platforms would contract. Recent FDA skepticism on certain RWE applications creates downside risk.
Macroeconomic pharma R&D contraction: If pharma R&D budgets shrink or shift away from RWE-focused programs, Savana's primary revenue streams (pharma partnerships) would be impacted.

Public Sentiment

Overall sentiment: Mixed-to-Positive — Savana is well-regarded in pharma and RWE communities but operates in lower public awareness; no major brand perception issues. Limited public reviews suggest strong B2B reputation but minimal B2C presence.

Customer sentiment: Limited public review data available (Glassdoor reviews are for different "Savana Inc." financial software company). Inferred from case studies and partner announcements: Pharma customers express satisfaction with RWE quality and cNLP accuracy (GSK partnership public endorsement). No significant public complaints or customer churn signals identified.

Employee sentiment: Glassdoor rating 3.9 / 5.0 (45 anonymous reviews for Savana healthcare company). Employee ratings:

Work-life balance: 3.8 / 5
Culture & values: 3.5 / 5
Career opportunities: 3.1 / 5 (lowest-rated dimension)
Compensation & benefits: 3.3 / 5
Business outlook: 64% positive

Mixed sentiments on decision-making empowerment; some reviews note founders as bottlenecks. 69% would recommend to a friend, indicating net positive employee experience but some friction on career development and autonomy.

Analyst sentiment: Limited public analyst coverage (company is private). Implied positive sentiment from funding momentum (CDTI 2025, consistent VC backing), Barcelona Health Hub partnership, and industry publication coverage (pharmaphorum, tech.eu). No identified critical analyst reports or public skepticism.

Sentiment Drivers

Date	Event/Action	Impact	Direction
2025-06	CDTI Innovación investment (EUR 4.56M)	Validates continued investor confidence in RWE/AI strategy; signals European tech ecosystem backing.	Positive
2024-03	Barcelona Health Hub partnership and office opening	Strengthens European healthcare innovation positioning; enables better access to Spanish hospital network.	Positive
2024	Savana Manager Suite launch	Product modernization signal; positions as SaaS-native company, aligns with market expectations.	Positive
2023-2024	Atropos Health visibility increase and USD 33M raise	Direct competitor momentum with agentic AI/point-of-care positioning threatens market perception.	Negative
2022	Series C funding completion (USD 25M)	Strong capital raise despite market downturn; investors signal confidence in dRWE and drug discovery expansion.	Positive
~2020	FDA/EMA early regulatory skepticism on RWE	Regulatory uncertainty creates headwind for entire RWE category; Savana mitigates through scientific approach.	Negative

Growth Vectors

Stated strategy: Savana's publicly communicated strategy centers on three pillars: (1) expanding Deep Real-World Evidence (dRWE) applications across pharma drug discovery, development, and post-market surveillance; (2) internationalizing presence beyond Europe/Latam into North America and Asia-Pacific; (3) integrating genomics, biomolecular, and imaging data layers alongside EHR NLP to create multimodal evidence generation capabilities.

Existing Market Expansion

Geographic expansion into North America: Establish direct pharma partnerships and hospital networks in USA/Canada. Initial US presence exists (Series B 2020 funded US expansion), but penetration of top-tier US hospital systems and CROs is underdeveloped vs. international footprint. US pharma is concentrated and accessible; opportunity to grow market share within existing customer base.
Upsell within pharma customer base: Expand services from single-indication studies (e.g., asthma) to multi-indication RWE platforms serving entire pharma R&D portfolio. Increase contract value and switching costs.
Healthcare provider/hospital segment: Expand beyond pharma into healthcare systems and hospital networks seeking to monetize their EHR data or improve clinical outcomes. Lower penetration than pharma; emerging revenue stream.

New Market Opportunities

Drug discovery and preclinical RWE: Move upstream from late-stage trials to early-stage drug discovery, patient cohort identification, biomarker discovery, and target validation. Series C explicitly targeted this vector; nascent but high-growth segment in pharma.
Genomics and multimodal data integration: Fuse genomic/biomolecular/imaging data with EHR-derived clinical phenotypes to enable precision medicine and complex phenotyping. Series C funded this capability; positioning for next-generation RWE.
Medical devices and diagnostics RWE: Apply cNLP methodology to post-market device surveillance and real-world diagnostic performance tracking. Underexploited adjacent market.
Asia-Pacific expansion: Extend multilingual NLP to Asian languages and establish partnerships in China, Japan, Singapore, Australia. Massive TAM but requires localization investment and regulatory relationships.

Growth Vector Assessment

Vector	Description	Evidence	Feasibility
US geographic expansion & pharma penetration	Increase direct sales into USA/Canada pharma and CROs; deepen relationships with existing customers.	Series B 2020 funded US expansion; presence is weak vs. international; 22 of top 50 pharma are global, not US-centric.	High — US is largest pharma market; existing customer relationships are expansion lever. Execution risk is sales capacity and localized regulatory knowledge.
Drug discovery RWE applications	Expand services upstream into early-stage drug discovery, patient phenotyping, biomarker identification.	Series C explicitly stated goal to "expand RWE presence into drug discovery space." Pharma spending in discovery is growing. Limited competition in this segment.	High — pharma demand is clear; Savana's cNLP and scientific credibility are assets. Requires new use case development and pharma sales skills.
Genomics and multimodal data fusion	Integrate genomic, biomolecular, imaging data layers alongside EHR NLP.	Series C funding explicitly targeted this. Genomic medicine demand is accelerating. TAM for multimodal RWE is nascent but high-growth.	Medium — Genomic integration is technically feasible; regulatory/privacy requirements are complex. Requires partnerships with genomic data providers and bioinfo expertise.
Asia-Pacific geographic expansion	Establish cNLP capabilities in Mandarin, Japanese, other Asian languages; form healthcare partnerships.	Company supports 6 languages; no announced Asian-language development. Asia-Pacific TAM is massive but requires localization and regulatory relationships.	Medium — Language development and localization are resource-intensive; regulatory complexity (China healthcare) is high. Long ROI timeline.
Medical devices and diagnostics RWE	Apply cNLP to post-market device surveillance and diagnostic performance tracking.	No public initiatives announced; methodology is transferable. Device surveillance is growing regulatory priority. Adjacent market with less competition than pharma.	Medium — Requires new go-to-market (device manufacturers, MDOs); smaller TAM than pharma. Less evidence of demand pull.
Platform consolidation and M&A exit	Acquire complementary AI/RWE capabilities or become acquisition target for large healthcare IT platforms.	Strong funding, IP, pharma relationships attractive to acquirers. No public M&A signals.	Medium — VC funding history suggests growth/IPO path more likely than near-term exit. If exit occurs, likely acquirer is large healthcare/data platform.

Discovered Entities

People

Jorge Tello Guijarro | Co-founder & Former/Founding CEO | https://www.linkedin.com/in/jorge-tello-guijarro/ (inferred)
Miriam Rodríguez González | CEO (Current as of 2024) | https://es.linkedin.com/in/miriam-rodr%C3%ADguez-gonz%C3%A1lez-pharma
Alberto Giménez | Co-founder & CFO | [No public URL identified]
Ignacio Hernández Medrano (Dr.) | Co-founder & CMO | https://theorg.com/org/savana/org-chart/ignacio-h-medrano
Sara Martínez | Hospital Manager SE and Latam | [No public URL identified]
Pablo Miner | CFO | [No public URL identified]

Competitors

TriNetX | Operates world's largest federated real-world data network (137 healthcare organizations, 17 countries); dominates academic citations (2,025 vs. Savana's regional presence); stronger network effects but more healthcare provider-focused than pure NLP depth.
Aetion | RWE analytics platform specializing in observational study design and statistical methodologies; strong regulatory track record; smaller pharma footprint.
LynxCare | Direct clinical NLP and RWE competitor; smaller geographic footprint; comparable technical capabilities.
Deep 6 AI | Clinical trial and patient cohort identification AI; narrower scope than Savana's multi-application RWE platform; strength in trial optimization.
Owkin | Federated learning platform for drug discovery and diagnostics; privacy-first data science approach; different business model and market positioning.
Atropos Health | Newer RWE competitor (founded 2019) with strong funding (USD 33M); positioned at point-of-care with agentic AI and EHR-embedded evidence; Microsoft partnership signals momentum; direct threat in physician workflow integration.
Truveta | Healthcare data platform with broad interoperability and analytics focus; adding RWE capabilities to compete with pure-play vendors; horizontal platform positioning creates long-term competitive risk.
Epic Systems / Cerner / Veradigm | EHR vendors increasingly adding RWE and clinical NLP analytics natively; structural consolidation threat through vendor bundling.