Five phases. Provable compliance.
Cryptographic audit trail.
SpanForge is the AI compliance platform for every team — structured around RFC-0001 SpanForge, the open event-schema standard for AI governance. Each phase has defined compliance obligations, auditable exit gates, and evidence that satisfies EU AI Act, GDPR, SOC 2, ISO 42001, and NIST AI RMF.
Comply. Prove. Scale.
From compliance baseline to production proof.
Is AI right for this?
Compliance begins before the first line of code.
What should we build?
Architecture decisions made with compliance evidence.
Build it to the standard.
Compliance-first CI/CD. No shortcuts.
Make it accountable.
T.R.U.S.T. Framework. Regulatory evidence.
Prove compliance in production.
RFC-0001. Immutable audit trails. Live.
The T.R.U.S.T. Framework
Every SpanForge-certified AI system satisfies five dimensions of responsible deployment. The T.R.U.S.T. Framework is not a checklist — it is the governance standard operationalised as technical controls, regulatory evidence packages, and cryptographically signed audit trails.
Explore the Framework →Transparency
Customers, regulators, and employees understand how AI affects them. AI behaviour is made intelligible to all affected parties — not just technical teams.
Responsibility
A named human is accountable for every AI system. AI cannot be deployed without a designated owner who carries accountability for its behaviour in production. Responsibility extends to cost: the Cost Intelligence Layer makes infrastructure spend visible at Design time and measures actual token costs in production via the SpanForge llm.cost.* namespace — ensuring accountable owners understand the financial implications before committing to them.
User Rights
Consent, transparency, and recourse for every individual AI affects. Users have the right to understand how AI decisions affect them and to seek redress where required.
Safety Guardrails
Technical constraints embedded in architecture, not just policy. Safety mechanisms are built into the system — not left as aspirational guidance or documents.
Traceability
Every AI decision must be traceable to its source data, model version, and configuration state. Full audit trail. No black boxes. Every decision is logged with an immutable, timestamped, cryptographically signed record — ready for regulators, auditors, and post-incident review.
Compliance frameworks and intelligence layers
A five-stage, evidence-based lifecycle that replaces ambiguity with defined transition criteria at every stage of AI delivery.
ResearchFailure Funnel™Where AI initiatives are typically lost across the lifecycle, grounded in S&P Global, Gartner, and McKinsey research.
EconomicsCost Model™The true cost of pilot purgatory — separating the visible costs on the budget from the hidden costs that compound below the surface.
MeasurementGate Readiness Score™A structured 0–100 evidence assessment across six dimensions. A score below 70 blocks gate reviews until remediation is complete.
RiskPilot Risk Index™A five-failure-mode taxonomy mapped to each gate. Each gate is designed to prevent one specific, documented failure pattern.
Cost Intelligence LayerCost IntelligenceDesign-time infrastructure estimates before any resource is committed, unified with runtime token cost tracking across all LLM providers via the SpanForge llm.cost.* namespace.
Start with your compliance baseline.
Not sure where you stand against EU AI Act, GDPR, or SOC 2? The Discover phase maps your obligations before you commit to architecture.
Start with Discover →