Your AI isn't governed. It's archived.
Governance rules that exist only in documents don't govern. We map your pipeline, classify every node T0–T4, and export what can run without inference. Backed by the Tri-Layer Quality Gates methodology.
Three governance gaps we see in every AI-native organization
No Systematic Auditor
Your AI systems have governance rules, but nobody checks whether they're followed. Audits are manual, episodic, and miss the patterns that matter. When a violation is found, it's already been in production for weeks.
No Export Pipeline
You know some nodes could run without an LLM — deterministic checks, rule-based classifiers, pre-computed lookups — but there's no process to identify and export them. Every inference runs at full cost, even when it shouldn't.
No Runtime Governance
Your governance exists in documents, not in code. There's no Circuit Breaker, no automated enforcement at inference time. When an agent generates a compliance violation, nobody stops it — the violation ships.
Every inference node belongs to a tier
Click any tier to expand. Each level has a different cost profile, auditability impact, and export potential.
Not sure what tier applies to your pipeline? Start with a Governance Triage assessment.
We practice what we preach.
These interactive tools run entirely client-side with zero LLM calls. They demonstrate our Zero Token methodology in practice — every calculation is deterministic code. Built by Mudra Knight. Zero inference. Zero API calls. Zero runtime cost.
Two approaches. One architecture.
Deterministic Governance
Export nodes out of inference entirely. T0–T1 nodes run at zero token cost — deterministic code, compiled classifiers, pre-computed lookups. No LLM invocation.
- 4 levers (A1–A4): deterministic export, classical ML, distillation DB, deterministic MCP
- Zero inference cost after export
- Complete auditability (every decision is deterministic)
- Circuit Breakers on remaining T4 nodes
Inference Economics
Reduce tokens on nodes that must stay in inference. Routing, caching, compression, and model selection — no zero claim, just measured reduction.
- 13 levers (B1–B13): routing, caching, retrieval, compression, governance
- ~56% token reduction (modeled on pilot case study)
- ~80% cost reduction with model routing
- Compliance Carveout for regulated outputs
Governance platforms check outputs. We fix the pipeline.
| Dimension | Governance Platforms | Mudra Knight |
|---|---|---|
| Deployment Model | Cloud-coupled (SaaS) | Client bare metal — air-gapped |
| Data Residency | Vendor-managed cloud | Client infrastructure — zero data leaves |
| Compliance Approach | Bolt-on feature | Core architecture — Tri-Layer Quality Gates |
| Quality Assurance | Manual or basic automated checks | Deterministic (L1) + Vector (L2) + LLM-Judge (L3) |
| Audit Trail | Vendor-managed, limited retention | Immutable, client-owned, full retention |
| Multi-Jurisdiction | Limited (US/EU only) | EU AI Act, TRAIGA, NIST, HIPAA, SEC, Colorado AI Act |
| Export Pipeline | Not available | T0–T4 systematic export framework |
| Self-Healing | None | L1–L4 incident protocols with automated recovery |
28 days from audit to doctrine sign-off
Phase 1: Illegibility Audit
Map the target pipeline end-to-end. Identify all inference points, data flows, and governance gaps. Produce the T0–T4 classification.
- Pipeline topology map
- T0–T4 classification of every inference node
- Circuit Breaker gap inventory
Phase 2: Auditor Classification
Run the deterministic auditor on each classified node. Validate classification accuracy. Identify immediate export candidates.
- Auditor run results per node
- Export candidate shortlist (T0/T1 prioritization)
- Drift risk assessment per candidate
Phase 3: T0/T1 Export Sprint
Build and deploy deterministic exports for all eligible T0 and T1 nodes. Regex patterns, compiled classifiers, deterministic MCP tools.
- Production deterministic exports per node
- Parity test suite (≥98% agreement target)
- Deployment runbook
Phase 4: Parity Validation + Circuit Breaker Wiring
Validate that deterministic exports match LLM output within threshold. Wire Circuit Breakers on remaining T4 nodes.
- Parity validation report
- Circuit Breaker deployment (one per T4 node)
- Rollback procedure documented
Phase 5: Handover
Transfer institutional capability. Document every export, every decision, every runbook. Train the team.
- Doctrine document (governance rules + export reasoning)
- Runbooks (export maintenance, drift detection, re-export procedure)
- Team training session(s)
Phase 6: Doctrine Sign-Off
Final review with sponsor. Sign off on governance doctrine. Establish ongoing maintenance cadence.
- Signed doctrine document
- Maintenance schedule (drift checks, re-export cadence)
- Escalation procedure for governance violations
From triage to enterprise governance
Governance Triage
1 week1 FDE Engineer
- Illegibility Audit (focused — governance scope)
- T0–T4 classification of target pipeline
- Circuit Breaker gap analysis
- Readiness scorecard with prioritized export candidates
- Recommendation: continue to full audit or stop here
Best for: Teams that want a rapid assessment before committing to a full governance engagement.
Single-System Audit
4 weeksTeam of Two (Delta + Echo)
- Full T0–T4 classification of one production pipeline
- T0/T1 export sprint (zero-inference nodes extracted)
- T2/T3 optimization recommendations
- Circuit Breaker implementation on T4 node(s)
- Parity validation report (≥98% LLM-vs-export agreement)
- Handover: doctrine document + runbooks + team training
Best for: Organizations with a single high-impact AI pipeline ready for governance hardening.
Enterprise Governance Program
8–12 weeksTeam of Two + rotating specialists
- Multi-pipeline T0–T4 classification across the organization
- Full export program (all eligible T0/T1 nodes across pipelines)
- Distillation DB deployment (shared across pipelines)
- Custom Circuit Breaker framework
- Institutional governance scorecard (tracked quarterly)
- Systemized Toolbox primitives extracted from your patterns
- Team training + certification program
Best for: Enterprises with multiple AI pipelines requiring coordinated governance across the organization.
These tiers cover deterministic governance engagements (Prong A — ZTA). For token reduction across your inference pipeline (Prong B — IEA), see our Inference Economics service. Pricing is per-engagement; retainer options available for ongoing governance maintenance.
Tri-Layer Quality Gates
We borrowed Quality Engineering methodology from Six Sigma and applied it to AI governance. Three layers of enforcement, each catching what the previous layer misses. In our market analysis, no competitor uses quality engineering language in AI governance.
Deterministic Regex
Zero hallucination. Pure code. Regex patterns, hardcoded rules, lookup tables. Catches the obvious violations before they reach any model. This is the export target for T0 nodes.
Vector Grounding
Retrieval-augmented. Every LLM output is grounded in actual regulation text, policy documents, and governance rules. If the output contradicts a grounded source, it's flagged.
LLM-Judge
A local model performs final human-like review. Non-deterministic, but catches semantic violations that regex and vector grounding miss. The last gate before output is released.
Common questions about deterministic governance
Audit. Export. Govern.
Your AI pipeline already has nodes that can run without inference. We'll find them, export them, and wire Circuit Breakers on the rest.
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