Valiance Health Secures Undisclosed Pre‑Seed Funding Led by Gobi Partners
•February 11, 2026
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Why It Matters
Standardised, interoperable data unlocks cost transparency and outcome benchmarking, essential for Malaysia’s shift to value‑based healthcare. Investors see the ‘boring’ infrastructure as the high‑leverage lever for system‑wide efficiency.
Key Takeaways
- •AI pipelines transform fragmented health records into a unified model
- •Pre‑seed funding targets Malaysia’s data‑infrastructure gap
- •Cost leakages identified, saving RM85,000 annually for Avisena
- •Standardised data enables episode‑level costing and benchmarking
Pulse Analysis
Malaysia’s healthcare ecosystem has long wrestled with siloed systems, inconsistent coding and legacy platforms that impede reliable analytics. Valiance Health’s AI‑powered platform tackles this fragmentation by ingesting raw clinical, operational and financial feeds, then applying automated cleaning, mapping and de‑duplication to produce a single, standards‑based repository. Backed by Gobi Partners’ SuperSeed II Fund, the pre‑seed round underscores a growing investor appetite for foundational health‑tech infrastructure rather than flashy consumer apps.
The real business impact emerges when the unified data layer fuels value‑based care initiatives. With trustworthy cost and outcome metrics, hospitals can benchmark procedures, negotiate better procurement contracts and accelerate claim adjudication. Avisena Healthcare’s pilot illustrates the potential: Healthproximate surfaced hidden inefficiencies, trimming appendicectomy expenses and delivering roughly US$18,000 in annual savings. Such transparency not only curbs waste but also equips payors to design reimbursement models that reward quality over volume.
Globally, giants like AWS HealthLake, Google Cloud Healthcare and Snowflake offer comparable data‑integration services, yet Valiance’s local focus addresses Malaysia’s unique fragmentation challenges. By aligning with international standards while tailoring to regional coding practices, the startup bridges a critical gap that many multinational solutions overlook. As AI continues to permeate clinical decision support, imaging and revenue‑cycle automation, the demand for clean, interoperable data will only intensify, positioning Valiance as a strategic enabler for the next wave of health‑tech innovation.
Deal Summary
Malaysia‑based AI healthcare startup Valiance Health announced an undisclosed pre‑seed round led by Gobi Partners’ SuperSeed II Fund, with participation from Jati Growth and Artem Ventures. The capital will accelerate its standardized healthcare data platform that uses AI to unify clinical, operational and financial data across hospitals.
Article
Source: e27
Valiance Health co-founders Dr Lutfi Fadil (L) and Dr Ridhwan Hassan
Prominent VC firm Gobi Partners has led an undisclosed pre-seed investment in Valiance Health, a Malaysia-based AI healthcare company building a standardised healthcare data platform.
The round was done through Gobi’s SuperSeed II Fund, with participation from Jati Growth and Artem Ventures.
Gobi is positioning the deal as more than a bet on another hospital dashboard. In its framing, this is a swing at the infrastructure layer — the plumbing that determines whether Malaysia can measure outcomes, compare costs, and move away from fee-for-service incentives.
“Malaysia’s healthcare system is at an inflection point,” said Jamaludin Bujang, Managing Partner at Gobi Partners. “Valiance is addressing the core data problem that has long slowed the shift to value-based care. The team is building the foundational infrastructure the healthcare ecosystem needs, not just analytics dashboards.”
How Valiance standardises fragmented healthcare data using AI
Valiance’s origin story is built around a familiar pain point for hospitals and payors: fragmentation everywhere. Co-founder Dr Lutfi Fadil traced the issue back to his doctoral research at Harvard in 2019, where he identified data fragmentation as the main barrier to scaling value-based healthcare (VBHC). Co-founder Dr Ridhwan Hassan saw the same problem from the inside as a Data Analytics Manager at KPJ Healthcare: turnover, inconsistent coding, and legacy systems made benchmarking billing and clinical data painfully difficult.
Valiance’s approach is to aggregate raw clinical, operational, and financial data from hospital systems and run it through AI-driven pipelines that “clean, map and standardise” the information into an “internationally recognised model”, producing a unified repository for analytics and reporting.
In practical terms, standardising messy healthcare data typically means doing several jobs at once:
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Normalising codes and terminology (for example, mapping different internal labels into standard clinical and billing vocabularies).
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Cleaning and de-duplicating records, so the same patient journey is not counted multiple times across systems.
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Structuring unstructured inputs, where AI can help extract usable signals from free text and inconsistent fields.
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Detecting anomalies in pricing, coding, and claims patterns that often hide inside operational noise.
Valiance is betting that AI can take what is currently manual, inconsistent work, and turn it into repeatable, governed pipelines that scale across hospital groups and administrators.
One application built on top of this foundation is Healthproximate, Valiance’s analytics interface for hospitals to analyse cost structures, identify trends, and surface operational bottlenecks. But the company’s central pitch is that the data layer is the real product: once the foundation is standardised, multiple applications (for providers, insurers, third-party administrators, and policymakers) can finally speak the same language.
What a unified data platform changes for Malaysia’s healthcare system
A unified healthcare data platform is not glamorous. It is also potentially transformative.
Malaysia’s healthcare chain has been operating with built-in friction: insurers struggle to adjudicate claims quickly without consistent evidence; providers face delayed reimbursements; regulators often rely on manual reporting; and patients have limited visibility into what they are paying for. When cost and outcome data cannot be compared reliably across facilities, VBHC becomes a slogan rather than a measurable model.
Also Read: Digital health: Malaysia leads in powering ASEAN’s transformation
Valiance claims its platform can introduce a common data standard that reduces friction and enables benchmarking. The early example cited in the press release points to tangible operational impact: Avisena Healthcare said Valiance helped it detect cost leakages and inefficiencies that were difficult to spot manually.
“Avisena and Valiance have worked together since the company’s early days,” said Puan Elina Nadia Omar, Group CEO of Avisena Healthcare. “Valiance helped us identify cost leakages and inefficiencies that were previously difficult to detect through manual processes. Using Healthproximate, we reduced average appendicectomy costs and achieved approximately RM85,000 (US$18,000) in annual savings, while also identifying opportunities to optimise device procurement without compromising clinical outcomes.”
That kind of savings matters not only because it cuts waste, but because it reveals what unified data makes possible: episode-level costing (what a procedure truly costs end-to-end), procurement optimisation, and real operational benchmarking across departments and sites.
How Valiance supports the shift to value-based healthcare
VBHC lives or dies on measurement: outcomes, costs, and the operational drivers behind them. Without trustworthy standardised data, hospitals cannot confidently compare performance, and payors cannot design payment models that reward quality over volume.
Valiance’s proposition fits VBHC in three ways:
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Cost transparency: giving providers and payors a clearer picture of what care episodes cost, rather than relying on inconsistent billing artefacts
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Outcome-linked benchmarking: enabling comparisons across facilities, clinicians, or care pathways once data is standardised
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Faster, evidence-led decisions: supporting claims adjudication and reimbursement workflows with cleaner, more comparable inputs
Dr Lutfi summarised the problem — and the intended fix — in blunt terms: “Value-based healthcare has been discussed in Malaysia for years, but it cannot exist without trustworthy, standardised data,” he said. “Hospitals are not short of data. They are overwhelmed by fragmented systems, inconsistent coding and manual processes that prevent meaningful analysis at scale.”
Global alternatives, and how AI is disrupting healthcare worldwide
Valiance is not alone globally in pursuing “healthcare data infrastructure” as the wedge. Internationally, hospitals and payors often turn to a mix of:
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Cloud health data platforms (such as AWS HealthLake or Google Cloud’s healthcare data services)
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Interoperability and integration layers (often built around standards like HL7 FHIR)
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Enterprise data platforms used in healthcare (including Databricks- or Snowflake-based stacks)
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Large EHR ecosystem tooling (where available) that standardises data within specific vendor environments
What distinguishes Valiance’s local pitch is focus: building a standardised platform for Malaysia’s ecosystem, where fragmentation, inconsistent coding practices, and legacy systems are not edge cases but are the default.
Also Read: AI in healthcare: The future arrived early—but is it ready?
Zooming out, AI is already disrupting global healthcare on multiple fronts beyond data wrangling: clinical decision support, imaging analysis, automation in revenue cycle and claims workflows, hospital operations optimisation, and even drug discovery. But nearly all of these high-impact use cases share the same dependency: high-quality, interoperable data.
That is why investors are increasingly willing to fund the “boring” layer. Because in healthcare, boring infrastructure is often where the real leverage and outcomes begin.
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Image Credit: Valiance Health.
The post Valiance Health raises pre-seed to fix Malaysia’s data fragmentation appeared first on e27.
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