Stop bleeding on LLM inference costs.
A 24-day diagnostic-led engagement that maps every inference node in your pipeline, applies 17 token-reduction levers across two prongs, and hands over institutional capability — not just a report.
Three patterns we see in every AI-native company bleeding on inference
Agentic Spam Loop
Your agents call each other in endless loops. Agent A generates output that Agent B reviews, which triggers another Agent A call. Every cycle costs tokens. You're paying for the loop, not the output.
3–8 LLM calls per task (avg)
1–2 LLM calls per task (with routing)
Savings: 40–70%
Model Sprawl
Every team picked their own model. Every pipeline uses the most expensive model available, even for trivial tasks. There is no routing policy — every request gets the flagship model, regardless of complexity.
Average: flagship model for all tasks
Right-sized model per task complexity
Savings: 50–85%
Re-Inference Cost
The same LLM call runs multiple times because nobody cached it. Same system prompt. Same user query. Same output — generated fresh every time. You're paying for the same tokens on repeat.
~30% repeat queries uncached
Semantic caching covers ~50% of repeats
Savings: 20–50%
17 levers. Two prongs. One architecture.
Click any lever to expand. Each one targets a specific token-reduction opportunity in your pipeline. Prong A exports nodes to zero inference. Prong B reduces tokens on what stays.
Routing
Caching
Why Mudra Knight vs observability platforms and LLM proxies
| Capability | Mudra Knight | Observability Platforms | LLM Proxies |
|---|---|---|---|
| Deterministic Export (Zero Token) | ✅ Systematic T0–T4 classification + export pipeline | ❌ Not available | ❌ Not available |
| Token Reduction (Modeled) | ✅ 17-lever audit with measured deltas | ⚠️ Usage tracking only — no optimization | ✅ Routing + caching (subset of levers) |
| Compliance Carve-Out | ✅ 4-class compliance matrix (A–D) per node | ❌ Not available | ❌ Not available |
| Full Pre-Engagement Audit | ✅ 24-day engagement with 6 phases | ⚠️ Dashboard monitoring (no engagement model) | ❌ Self-service configuration only |
| Cost Reduction w/ Routing | ✅ Model routing + fallback chain + intent routing | ❌ Not available | ✅ Core feature (but no compliance layer) |
| Institutional Handover | ✅ Doctrine + runbooks + team training | ❌ Not available | ❌ Not available |
| Air-Gapped Deployment | ✅ Client bare metal — zero data leaves | ❌ Cloud-hosted | ⚠️ Self-hosted option available |
24 days from diagnosis to handover
Phase 1: Diagnosis
Analyze LLM spend from telemetry or raw logs. Identify the top cost drivers. Classify every inference node by prong eligibility.
- Spend analysis report
- Top-3 quick wins
- Prong A/B opportunity sizing
Phase 2: Full Audit
Run the 17-lever auditor on the target pipeline. Every node gets a lever-by-lever assessment. Compliance carveout applied for regulated nodes.
- Full audit report (17 levers)
- Export candidate shortlist
- Compliance restriction map
Phase 3: Prong A Sprint
Build and deploy deterministic exports for all eligible T0/T1 nodes. Regex patterns, classical ML substitutes, distillation DB entries.
- Deterministic exports per node
- Parity test suite (≥98%)
- Deployment runbook
Phase 4: Prong B Optimization
Deploy token-reduction levers on remaining inference nodes: routing, caching, compression, schema caps, batch execution.
- Optimized routing configuration
- Caching layer deployed
- Schema constraints applied
Phase 5: Parity Validation
Validate that optimized pipeline matches original output quality. Run the full test suite. Verify compliance gates still pass.
- Parity validation report
- Regression test results
- Compliance gate audit
Phase 6: Handover
Transfer institutional capability. Doctrine document, runbooks, team training, maintenance schedule.
- Doctrine document
- Operations runbook
- Team training
- Maintenance schedule
From diagnosis to enterprise program
Burn Diagnosis
1 week1 FDE Engineer
- LLM spend analysis (from telemetry or raw logs)
- Quick-wins report (top 3 token-reduction opportunities)
- Prong A vs Prong B opportunity sizing
- Recommendation: proceed to full audit or stop here
Best for: Teams that want a rapid spend assessment before committing. 50% of the fee credits toward a full Audit+Export engagement.
Audit + Export
3 weeksTeam of Two (Delta + Echo)
- Complete inference audit (17-lever analysis)
- Prong A export sprint (eligible nodes exported to zero inference)
- Prong B optimization (routing, caching, compression deployed)
- Parity validation report (≥98% agreement target)
- Institutional capability handover (runbooks, training, doctrine)
Best for: Organizations with a single high-impact pipeline ready for token-cost reduction. Most common engagement type.
Enterprise Multi-Pipeline Program
8–16 weeksTeam of Two + rotating specialists
- Multi-pipeline audit across the organization
- Full export program (all eligible T0/T1 nodes)
- Spend-attribution infrastructure (B13 deployed across teams)
- Custom routing policies per pipeline
- Quarterly optimization reviews for 12 months
Best for: Enterprises with multiple AI pipelines requiring coordinated token-cost reduction across the organization.
Pilot results: ~56% token reduction, ~80% cost reduction
Token reduction
Cost reduction (with routing)
Prong A (Zero Token) contribution
Prong B contribution
Token reduction (~56%) and cost reduction (~80%) are measured separately and independently. Cost reduction includes model routing effects (cheaper models for simpler tasks), which compound token reduction but are not the same metric. Token reduction is the direct result of deterministic export (Prong A, ~15pp) and inference optimization (Prong B, ~41pp). These figures are from a single pipeline pilot; results vary by pipeline architecture and existing optimization level.
Read the full pilot case studyCommon questions about inference economics
Once your tokens are under control, lock it down
Token reduction is the first step. The second is deterministic governance — export eligible nodes to zero inference and wire Circuit Breakers on the rest.
Audit. Optimize. Hand over.
Stop guessing what your inference costs. We'll map every node, apply 17 levers across two prongs, and hand over a pipeline that costs measurably less.
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