STEP 01
Describe Your Use Case
Define your business context, policies, and success criteria via plain-language chat. Luminary identifies missing information and asks clarifying questions.
Find the right AI model for your business
Convert your business needs into many realistic test journeys, run them on all models you care about, and identify the best one based on key metrics.
Business-driven
Test journeys generated from your actual use case, not generic benchmarks.
Simulation-powered
Digital-twin runs simulate real customer interactions, backend systems, and tool calls.
Fully auditable
Every test is reproducible, exportable, and explainable to non-technical stakeholders.
Context
The AI market has 2.9 million models. Picking the right one is harder than it looks.
Most teams make model selection decisions based on leaderboards, marketing, and gut instinct. The result: expensive models that underdeliver, or cheap ones that fail silently in production.
01
Benchmarks don't reflect real use cases
MMLU and similar benchmarks test academic tasks, not your customer support agent or e-commerce workflow. A model that tops the leaderboard might fail your business logic entirely.
02
SOTA models are expensive overkill
Decision-makers default to the most-hyped models. But a smaller, well-matched model often outperforms on your specific task at a fraction of the cost.
03
Cost-per-token tells you nothing
A cheaper-per-token model that takes 20 steps to complete a task likely costs more in practice than a model that finishes in 10, even if its listed price looks lower.
Definition
The intelligent model selection layer for your enterprise AI stack.
Multiverse Computing Luminary connects to any OpenAI-compliant inference provider. Feed it your business context, get a clear champion model backed by evidence.
IT IS
- A business-driven model evaluation and selection platform
- Compatible with any OpenAI-compliant inference provider
- A digital twin simulation engine for realistic workflow testing
- A source of auditable, reproducible test evidence
IT IS NOT
- A generic benchmark or leaderboard tool
- Tied to any specific model or vendor
- A chatbot or end-user-facing application
- A replacement for your existing AI models
Mechanics
Five steps from business brief to champion model.
Each step is automated, auditable, and tuned to your specific business requirements. The entire process can be completed in days.
STEP 02
Generate User Journeys
Luminary generates golden-path, edge case, and adversarial test journeys, fully compliant with your stated business rules and policies.
STEP 03
Simulate Workflows
A digital twin simulates the full interaction: customer, backend systems, tool calls, and function executions — for every model in your evaluation set.
STEP 04
Evaluate Models
Head-to-head comparison across all models on cost per task, success rate, latency, and policy compliance — not benchmark leaderboards.
STEP 05
Select Your Champion
A clear winner emerges from the data, with full evidence to back the decision for technical and non-technical stakeholders.
Metrics that matter
Business metrics, not benchmark scores.
Luminary evaluates models on what your business actually cares about — not what makes a good academic paper.
Cost per completed task
Not cost per million tokens. Total cost across all model steps to complete one real business task end to end.
Use-case success rate
What percentage of test journeys did the model complete correctly, including edge cases and adversarial inputs designed to break it?
Policy compliance
Does the model respect your business rules? Does it escalate to humans when required? Does it handle multi-language inputs without hallucinating?
Latency per job
How many steps does the model take? A model needing 15 turns costs more and is slower than one finishing in 10, harming cost and user experience.
Full auditability
Can you explain why the model succeeded or failed at each step? Luminary produces exportable test traces your compliance team can sign off on.
Where it fits
Built for teams deploying AI agents at scale.
Luminary works for any business deploying LLM-powered agents — before and after production.
Before deployment
Customer support agents
Test which model handles your specific support scenarios, multi-language inputs, and escalation policies.
E-commerce and retail agents
Evaluate models on order workflows, returns, pricing rules, and complex business logic.
Financial services automation
Validate model behaviour against compliance requirements, approval thresholds, and audit trails before going live.
HR and internal operations
Compare models on structured workflows with policy constraints and human-in-the-loop requirements.
Model selection · Risk reduction · Use-case validation
Ongoing operations
Model upgrade evaluation
Before switching providers or versions, validate the new model against your existing production test suite.
Cost optimisation
Periodically re-run evaluations to find smaller, cheaper Multiverse Computing models that now match your quality bar.
Regression testing
Add new test journeys as your use case evolves. Catch regressions before users do.
Multi-vendor comparison
Evaluate Multiverse Computing models alongside any third-party provider side by side.
Regression testing · Cost optimisation · Vendor comparison
Find the right model for your business. In days, not months.
Luminary connects to your inference provider, learns your use case, and delivers a clear, evidence-backed model recommendation. Ready to deploy.