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Case Studies

Reference Scenarios

Customer case studies are published only with each customer's approval, after their pilot results are in. Until then, these reference scenarios show how teams like yours use Lionhouse and what we measure together.

Reference scenario · not a customer story

Fintech & Digital Banking

The challenge

Strict regulatory audits, high-volume transaction data, and no tolerance for data leaks.

The approach

trustlynce collects PCI DSS and SOC 2 evidence continuously; aiAxonIQ traces every transaction across services; TrustIvan inventories and rotates machine credentials and API tokens.

What the pilot measures

  • Hours spent preparing PCI DSS and SOC 2 evidence
  • Machine identities without an owner
  • Observability cost per transaction traced

Target outcome: audit evidence collected continuously, and observability storage costs reduced through ClickHouse compression.

Reference scenario · not a customer story

HealthTech & BioInformatics

The challenge

HIPAA compliance, patient data privacy (DPIA), and need for grounded technical AI assistants.

The approach

MITHUNAI answers medical documentation questions with cited sources; TrustIvan secures containerized ML models; trustlynce tracks BAA vendor agreements continuously.

What the pilot measures

  • Share of AI answers with a verifiable source
  • ePHI sent to third-party services (target: none)
  • Time to complete a HIPAA access review

Target outcome: every AI answer cited to source documents, with models running entirely on-premise.

Reference scenario · not a customer story

Fast-Growing Cloud SaaS

The challenge

Engineering team scaling rapidly, container sprawl, secret leakage, and unmanageable telemetry bills.

The approach

aiAxonIQ replaces per-host APM with OpenTelemetry on ClickHouse; TrustIvan blocks secrets in GitHub pull requests; MITHUNAI answers onboarding questions from internal docs.

What the pilot measures

  • Monthly observability spend before and after
  • Secrets blocked in pull requests
  • Time from alert to likely cause

Target outcome: faster anomaly detection and lower monitoring spend through high-compression telemetry storage.

Scenarios by Problem

Interested in becoming a published case study, or comparing notes with a team in your industry? Tell us in your demo request.

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Open Standards: OpenTelemetry, MCP & SAML / OIDC