Ops + QA
Live anomaly triage
Behavioral drift, latency spikes, and policy regressions grouped by root cause with suggested remediation steps.
AI Control Tower
Best-in-class visibility and metrics
A standard deliverable for every deployment. Real-time visibility into quality, cost, performance, business impact, and risk.
Real-time monitoring
Executive and operator dashboards with live metrics and alerts.
Quality assurance
Evaluation harnesses and regression tests prevent quality degradation.
Business impact
Track ROI, cost optimization, and strategic decision-making metrics.
Optimization
Model and prompt leaderboard
Side-by-side A/B comparisons across quality, cost, and latency to pick the best routing strategy for each intent.
Risk + Compliance
Automated governance evidence
Daily snapshots for guardrail pass rates, PII redaction, and human escalation compliance for audit-ready reporting.
Executive
Revenue opportunity radar
Surfaces high-intent journeys with weak conversion and proposes experiments tied to expected dollar impact.
What It Is
The AI Control Tower is a comprehensive dashboard and measurement system that provides real-time visibility into your AI systems. It includes monitoring infrastructure, evaluation harnesses, regression test suites, and operational dashboards. It's not optional—it's how we ensure deployments stay on track and continuously improve.
Components
- Real-time monitoring dashboards (executive and operator views)
- Evaluation harness and regression test suite
- Alerting and incident management integration
- A/B testing framework for model and prompt optimization
- Monthly executive readout framework
Why It Matters
For Executives
- ROI visibility and business impact tracking
- Risk management and compliance monitoring
- Cost optimization and budget control
- Strategic decision-making with data
For Operators
- Real-time system health and performance
- Incident detection and resolution
- Quality monitoring and regression prevention
- Optimization opportunities and A/B test results
Live Demo Storyboard Northstar Outfitters • Multi-location retail + ecommerce • Last 30 days
Executive cockpit: KPI impact + AI evaluation health in one view
Built for leadership calls: a high-level control tower showing business outcomes and model quality side by side. This sample simulates a retail deployment where the AI assistant handles customer support and product search.
Deployment scope: Chatbot for customer service and product discovery
Revenue influenced by AI
$1.84M
+19.2% vs previous 30d
Sessions with AI touchpoint that converted.
Conversion rate (AI-assisted)
8.7%
+2.1 pts
Compared with non-assisted product sessions.
Support deflection
54%
+11 pts
Resolved without escalation to human agents.
Time to find item
1m 42s
-36%
Median from intent to viewed product detail page.
Aethyrn evaluation framework metrics
Trust, safety, relevance, and response quality signals updated continuously from evaluation harnesses.
Trust & safety score
97.1/100
Policy-safe responses, toxicity, and PII leakage checks.
Answer accuracy
93.4%
Grounded factual correctness on sampled customer intents.
Product ranking NDCG@10
0.89
Ranking relevance for product search and discovery prompts.
Conversation NDCG
0.86
How well multi-turn responses match ideal resolution paths.
Contextual coherence
91.2%
Maintains context over turns and avoids contradictory guidance.
Hallucination rate
1.8%
Responses with unsupported claims or incorrect inventory status.
Guided incident playback
09:10
Drift alert fires
Search relevance drops in women's outerwear after a catalog sync.
Owner: Control Tower agent
09:16
Auto-playbook launched
System runs regression suite, isolates the changed embeddings pipeline, and rolls traffic to last known stable route.
Owner: Reliability workflow
09:31
Experiment promoted
Top-performing prompt variant is promoted for affected intents, restoring ranking quality above baseline.
Owner: LLMOps team
10:00
Executive recap sent
Control Tower posts a one-page summary with impact, fix, and prevented revenue loss estimate.
Owner: Weekly readout bot
Control Tower command center lanes
The demo now mirrors how teams operate in reality: governance, experimentation, and business impact are coordinated in one working surface.
Governance lane
- Policy pass/fail by use case
- Escalation SLA compliance
- Audit export in one click
Experiment lane
- Prompt/model flighting by segment
- Incremental lift confidence scoring
- Auto-rollback on guardrail breach
Business lane
- Conversion and deflection attribution
- Cost-to-serve trendline
- Pipeline-ready KPI digest
Aethyrn-backed metrics vs customer-specific KPIs
Every deployment gets a shared quality baseline from Aethyrn plus a KPI layer mapped to your business model, operating constraints, and executive scorecard.
Aethyrn-backed baseline metrics
-
Trust & safety score
Cross-client baseline: policy-safe behavior, PII leakage, and escalation compliance across standardized eval sets.
-
Grounded answer accuracy
Measures whether answers are supported by retrieved context and approved source systems.
-
Hallucination and refusal quality
Tracks unsupported claims plus whether declines are appropriate and helpful.
-
Latency and cost efficiency
Token, routing, and response-time efficiency normalized across use cases.
Customer-specific KPI layer
-
Cart conversion lift from AI sessions
Retail-specific KPI tied to margin contribution from AI-assisted journeys.
-
Support deflection by intent family
Measures reduced agent workload for returns, sizing, and order status intents.
-
Policy-compliant resolution rate
Tracks success in regulated workflows with custom escalation rules and SLAs.
-
Time-to-value for high-intent journeys
Measures cycle-time reduction from first ask to completed business outcome.
LLM-as-a-judge vs other evaluation methods
We use LLM-as-a-judge as one signal, not the only signal. Reliable AI programs combine rubric judging with deterministic tests, human review, and business outcomes.
LLM-as-a-judge
Best for: Scoring nuanced qualities like helpfulness, coherence, instruction-following, and rubric-based quality at scale.
Strengths: Fast, scalable, and can grade open-ended responses with domain-specific rubrics.
Tradeoffs: Needs calibration and periodic human anchoring to prevent judge drift or rubric misinterpretation.
Deterministic checks
Best for: Verifying exact constraints (format, policy keywords, tool-call schema, prohibited output).
Strengths: High precision and repeatability for objective pass/fail guardrails.
Tradeoffs: Cannot capture nuanced quality or partially-correct long-form answers.
Human review
Best for: Gold-standard sampling for high-risk or ambiguous interactions and final QA sign-off.
Strengths: Best at contextual judgment and edge cases.
Tradeoffs: Expensive and slower; not feasible for continuous full-volume coverage.
Outcome-based telemetry
Best for: Measuring business impact directly via conversions, deflection, CSAT, retention, and cycle-time.
Strengths: Ties model behavior to real-world value and executive KPIs.
Tradeoffs: Lagging indicator; requires robust attribution and experiment design.
Executive scorecard architecture
Built for CFOs, COOs, and digital leaders: this is the operating scorecard that links model quality to margin, efficiency, risk, and strategic confidence.
Objective
Baseline
Current
Target
Executive owner
-
Grow AI-influenced revenue
$1.54M / month
$1.84M / month
$2.10M / month
Chief Digital Officer
-
Reduce cost-to-serve
$4.92 per assisted session
$3.88 per assisted session
$3.40 per assisted session
VP Support + CX Ops
-
Increase policy-safe resolution
89.6%
96.8%
98.5%
Risk & Compliance Lead
-
Protect customer trust
2.9% hallucination rate
1.8% hallucination rate
<1.0% hallucination rate
Head of AI Reliability
Evaluation lifecycle: from benchmark to board confidence
Executive trust comes from repeatability. Each change passes a staged lifecycle so quality, safety, and business impact are validated before and after launch.
Offline benchmark
Before release, candidate models/prompts are tested on curated intent suites for quality, safety, and retrieval grounding.
Shadow + canary
Changes run in shadow mode and then limited traffic slices with strict rollback thresholds on safety and KPI regressions.
Production guardrails
Live monitors enforce refusal quality, escalation behavior, latency SLOs, and policy controls in real time.
Post-deploy learning loop
Outcome telemetry and sampled human audits feed next-week experiment plans and monthly strategic reprioritization.
Operating cadence executives can rely on
The Control Tower is not just a dashboard. It is an execution rhythm connecting daily operations to weekly optimization and quarterly strategic decisions.
Daily
Ops reliability standup
Outputs: Incident queue triage, drift root-cause status, rollback/rollforward actions, SLA exceptions.
Weekly
LLMOps optimization review
Outputs: Prompt/model leaderboard decisions, shipping candidates, guardrail deltas, cost optimization actions.
Monthly
Executive KPI readout
Outputs: Business impact summary, risk/compliance posture, spend efficiency, approved roadmap bets.
Quarterly
Board-level strategy checkpoint
Outputs: Value realization assessment, governance maturity score, capital allocation recommendations.
Boardroom questions answered in one surface
Designed for senior stakeholders who need concise, defensible answers about value creation, risk exposure, and investment prioritization.
Are we creating durable business value or short-term metric lift?
Control Tower separates conversion spikes from sustained value via cohort tracking, margin-adjusted revenue, and 60/90-day retention deltas.
Where are we taking unacceptable risk?
Risk heatmaps isolate intents with elevated policy breaches, poor escalation behavior, or low confidence, then trigger gated routing.
Do we trust the measurements?
All key metrics are triangulated across LLM-judge rubrics, deterministic checks, human QA samples, and outcome telemetry before executive reporting.
What should we fund next quarter?
Opportunity radar prioritizes experiments by expected EBIT impact, implementation complexity, and confidence intervals from prior tests.
Metrics Categories
Quality
Measures of AI system accuracy, reliability, and task completion success.
- Task success rate
- Groundedness/citation rate (for RAG systems)
- Refusal rate
- Tool-call success rate
- Error taxonomy and frequency
- User satisfaction scores
Performance
System speed, throughput, and availability metrics that impact user experience.
- Latency (p50, p95, p99)
- Throughput (requests per second)
- Uptime and availability
- Queue depth and wait times
- Response time distribution
Cost
Financial metrics tracking AI system operational costs and efficiency.
- Cost per task
- Cost per user
- Model mix and routing efficiency
- Caching hit rate
- Token usage and optimization
- Infrastructure costs
Business
KPIs that measure real business impact and ROI of AI deployments.
- Deflection rate (support use cases)
- Average handle time (AHT) reduction
- Cycle time improvement
- Conversion lift
- Revenue impact
- Adoption rate
- User engagement metrics
Risk
Metrics that track security, compliance, and operational risk factors.
- PII detection and redaction rate
- Policy violation incidents
- Access control audit coverage
- Data retention compliance
- Incident frequency and severity
- Security event logs
How Aethyrn Uses It
Weekly Performance Reviews
We review quality, cost, and performance metrics weekly, identifying trends and optimization opportunities. Issues are flagged and addressed proactively.
A/B Testing & Optimization
The Control Tower enables systematic A/B testing of models, prompts, and routing strategies. Results are tracked and winning approaches are deployed.
Regression Prevention
Evaluation suites run continuously, catching regressions before they impact users. Quality gates prevent deployment of degraded models.
Cost Optimization
We track cost per task, model mix efficiency, and caching hit rates. Monthly optimization recommendations reduce costs while maintaining quality.
Monthly Executive Readout
Every month, we deliver an executive readout that summarizes system performance, business impact, and optimization opportunities.
Readout Outline
- Executive Summary: Key metrics, trends, and business impact
- Quality Metrics: Task success, error rates, user satisfaction
- Performance: Latency, throughput, uptime
- Cost Analysis: Cost per task, optimization opportunities, budget status
- Business KPIs: Deflection, cycle time, conversion lift, revenue impact
- Risk & Compliance: Security events, policy violations, audit status
- Recommendations: Optimization opportunities, scaling plans, risk mitigation
Ready to deploy AI with built-in visibility?
Request a KPI framework walkthrough for your AI Control Tower rollout.