ARCHITECTURE

Architecture

How QuantumVerifi turns an input into an evidence-backed verdict — and how it can be deployed.

01PIPELINE

Input

A URL, Git repository, API spec, or your own requirements/docs.

Analysis engine

Detects language, framework, and structure; comprehends the codebase and generates tests grounded in your inputs.

Isolated sandbox execution

Generated tests compile and run in dedicated, ephemeral sandboxes — destroyed after each run, with restricted network access.

SHA-256 evidence chain

Every test case, execution, and result is linked into a tamper-evident, canonical-JSON audit trail.

Report & verdict

A confidence-scored verdict plus exportable evidence reports (JSON / HTML / PDF).

Per-tenant storage

Results and artifacts are isolated per tenant with row-level security; source code is not stored permanently.

A model-routing layer sits alongside the analysis and execution engines, selecting between base models and (on the Scale tier) your per-tenant fine-tuned adapter per operation.

02ISOLATION

Isolation & data handling

—Multi-tenant isolation with row-level security (RLS) at the database layer.

—Ephemeral sandboxes with restricted network access; resource limits enforced per run.

—Source code cloned temporarily during analysis, not stored permanently; results retained 90 days.

—Per-tenant model adapters and training data are isolated and never shared.

See the Security page for the full controls list.

03DEPLOYMENT

Deployment options

—Cloud SaaS — runs on our infrastructure, zero setup.

—Private tenant — dedicated, isolated environment.

—Self-hosted Kubernetes — deploy into your own cluster.

—Restricted-network — offline LLM inference via Ollama; air-gapped available by enterprise agreement.

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