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Universal Semantic Layer : The foundation for instant, actionable, agentic analytics
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Analytics at infinite scale Scale

Built to Scale Analytics,
Not Rebuild Them

Consistent, governed analytics as data volumes,
users and business complexity grow—without re-architecture or rework

Quick Read

  • Fast, consistent analytics as data, users and complexity grow
  • Stable performance under high and mixed concurrency
  • Single semantic data model that supports deep, wide analytics
  • Analytics that scales without re-architecture or tuning
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Blazing fast Power BI on 100s of billions of rows

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The Scale Barrier in Enterprise Analytics

Enterprise growth in data, users and complexity strains systems—forcing trade-offs between scale, consistency and manageability

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    Data models fragment as data and teams grow

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    Performance degrades under high concurrency

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    Costs spike as scaling users drives warehouse overprovisioning

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    New use cases add duplication, tuning or re-modeling

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    Governance weakens as analytics spreads across tools and teams

Scale analytics without fragmentation or rework

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    Add new tools or teams without duplicating models

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    Scale adoption across BI and AI without fragmenting definitions or KPIs

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    Scale without lock-in — connect multiple BI tools, no rework

Whitepaper

Universal Semantic Layer for Instant, Actionable Insights

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Built for any data volume and workload

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    Handle growing data volumes without re-engineering analytics

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    Avoid re-modeling for every platform or workload

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    Works across on-prem and cloud data environments

Case Study

Instant responses on 500 billion transactions at an international bank

Read more Instant responses on 500 billion transactions at an international bank

Built for peak concurrency

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    Support thousands of concurrent users

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    No performance cliffs as adoption grows

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    Scale globally without duplicating infrastructure

Case Study

1000s of concurrent users, billions of rows, instant responses

Read more 1000s of concurrent users, billions of rows

Designed for deep, real-world analytics

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    Model complex hierarchies, dimensions and KPIs in one layer

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    Support high-grain, multidimensional analysis at enterprise breadth

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    Expand models without breaking downstream analytics

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    Scale across time — add data, metrics and use cases without resets

Case Study

10,000+ KPIs on a single model

Read more 10,000+ KPIs on a single model

Governance that doesn't break as you scale

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    Governed definitions, access control and lineage across all consumers

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    Scale adoption without losing trust or consistency

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    Eliminate manual tuning, cube management and re-aggregation

Case Study

Fast, governed analytics for a sovereign wealth fund

Read more Fast, governed analytics for a sovereign wealth fund

How Kyvos Fits in Your
Analytics Stack

How Kyvos Delivers Enterprise-Scale Analytics

Kyvos is engineered to sustain growth in data, users and analytical complexity—without fragmentation, rework or loss of trust.

  • Deep, wide semantic data models

    One model scales across growing dimensions, KPIs and analytical complexity without fragmentation.

    Deep, wide semantic data models

  • Virtual models for domain isolation

    Domains scale independently on a shared core, avoiding duplication as teams and use cases grow.

    Virtual models for domain isolation

  • Centralized governance, decentralized access

    Control governance centrally while usage scales enterprise-wide without inconsistency.

    Centralized governance, decentralized access

  • Persistent semantic metadata

    Semantics persist over time, enabling growth without resets or rework.

    Persistent semantic metadata

  • Fully distributed and elastic architecture

    Scales out seamlessly across nodes to handle growing data, users and workloads without re-architecture.

    Fully distributed and elastic architecture

Our Impact

Responses in less than 5 seconds on 500 billion transactions at a global bank
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Global sports betting company migrates SSAS to Kyvos for deeper analytics
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Global fintech ends analytical silos with Kyvos
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Pharmacy chain delivers self-service analytics for 20,000+ suppliers
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FAQs

Does Kyvos replace my data warehouse or lakehouse?
No. Kyvos runs on top of your existing cloud data warehouse or lakehouse and enhances it with semantic intelligence and high-performance execution. Enterprises retain their existing data investments while unlocking scale at the analytics layer.
How does Kyvos help enterprises scale analytics without re-modeling?
Enterprises scale business complexity—more data, domains, KPIs and hierarchies—within a single semantic model, eliminating the need to split models or rebuild analytics. Kyvos prevents the fragmentation and metric drift that typically emerge as organizations grow.
What happens to performance when thousands of users query data simultaneously?
Enterprises maintain consistent, sub-second analytics performance even under massive concurrency. Kyvos is designed so analytics adoption can expand enterprise-wide without throttling or degraded user experience.
Does analytics performance degrade as data volumes grow?
No. With Kyvos, enterprises scale to very large data volumes on cloud data warehouses and lakehouses without increasing latency or incurring runaway compute costs. Performance scales linearly with demand rather than relying on brute-force compute.
Can Kyvos support both BI and AI workloads at scale?
Yes. Enterprises use Kyvos as a single governed semantic foundation to support dashboards, self-service analytics, AI agents and LLM pipelines. Because Kyvos serves both, BI and AI scale together using the same definitions, hierarchies and logic.
Can enterprises use multiple BI tools without duplicating logic?
Yes. Multiple BI tools connect simultaneously to Kyvos without rebuilding models or calculations. This allows teams to scale analytics consumption while avoiding tool-specific semantic silos.
How does Kyvos prevent metric drift as analytics usage expands?
Kyvos enforces centralized definitions, access controls and lineage so all teams and tools rely on the same business logic. Consistency is maintained even as new users, teams and use cases are added.
Does scaling analytics increase operational complexity?
No. Enterprises scale analytics without manual tuning, data model management or performance firefighting. Operational effort remains flat even as data volume, users and query complexity grow.
How does Kyvos support long-term analytics evolution?
Kyvos allows enterprises to continuously add new data sources, dimensions, KPIs and use cases without breaking existing dashboards or workflows. Analytics evolves incrementally instead of requiring disruptive re-platforming cycles.
Is Kyvos suitable for enterprise-wide analytics adoption?
Yes. Kyvos is designed for large-scale enterprise deployments with thousands of users, diverse tools and mixed BI and AI workloads. It supports the transition from departmental analytics to enterprise-wide intelligence.

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