Content Creation & Marketing
Automated content generation for product descriptions, social media, and marketing campaigns
CreativeObjective LLM Rankings for CPG & Pharma
We test 300+ models with identical prompts and measure exactly what comes back. No vendor bias. No marketing claims. Just data.
The LLM Ranking Consortium brings together industry leaders, researchers, and practitioners to establish common standards for evaluating large language models in enterprise contexts.
Our mission is to create transparent, reproducible benchmarks grounded in real-world use cases—eliminating vendor bias and marketing claims through collaborative, expert-driven evaluation.
Core Principles:
✓ Shared golden datasets and evaluation methodologies
✓ Expert judgment panels for quality validation
✓ Open standards accessible to all members
✓ Continuous improvement through collective research
INDURO.AI's LLM Router is built on the principles of the LLM Ranking Consortium. Just as the consortium defines common standards, golden data, and expert-driven evaluation, the router applies these foundations in a production-ready system.
The router leverages real industry use cases contributed to the consortium, ensuring routing decisions are relevant and business-aligned.
By integrating consortium standards, the router can rank and route queries across multiple LLMs—selecting the right model for accuracy, safety, and cost efficiency.
The router is continuously improved with golden data sets and evaluation methodologies shared within the consortium.
Decisions are traceable, benchmarked, and supported by expert judgment panels, giving enterprises confidence in outcomes.
For CPG and Pharma organizations, INDURO.AI's LLM Router means:
In short, INDURO.AI operationalizes the consortium's mission, turning shared research and evaluation into a live enterprise solution that delivers measurable ROI.
Each use case tested with 30-120 prompts across 6 capability profiles
Some of the cases include:
Automated content generation for product descriptions, social media, and marketing campaigns
CreativeReal-time visibility, demand forecasting, and logistics optimization
OperationalSentiment analysis, trend detection, and consumer behavior prediction
AnalyticalFormulation optimization, ingredient research, and market gap analysis
TechnicalAutomated quality checks, compliance monitoring, and defect detection
RegulatoryChatbots, email response, and customer query resolution
SupportSales prediction, inventory optimization, and trend forecasting
AnalyticalCarbon footprint tracking, packaging optimization, sustainability metrics
RegulatoryMarket analysis, competitor monitoring, and strategic insights
AnalyticalAutomated reporting, data visualization, and insight generation
TechnicalPersonalized recommendations, dynamic content, and customer journey optimization
CreativeTraining content creation, knowledge base management, and onboarding automation
SupportFrom 331 models to production-ready recommendations through rigorous CPG-specific testing
Each use case is assigned a profile that determines how we weight the 10 evaluation criteria
High weights on creativity, storytelling, brand voice
High weights on reasoning, groundedness, data accuracy
High weights on JSON/structured output, instruction following
High weights on safety, latency, multilingual
High weights on reasoning, long-context, task quality
High weights on safety, groundedness, compliance (hard gates)
Why Profiles Matter: A model that scores 96.5% for Creative marketing might score 81% for Operational tasks. Different use cases need different strengths. We test all 331 models across all 6 profiles to find the best fit for each CPG need.
Progressive filtering from 331 models down to production-ready finalists
Fast screening. All models tested with identical inputs. Timeout 30s. Top 30% advance (~150 models).
Domain-specific CPG tasks. 6 profiles tested. Top 20% advance to finals (~30 models).
Production readiness. RAG, JSON, safety gates. Heavy scoring. Final rankings with recommendations.
Testing 331 models × 120 prompts × 20 use cases = 795,600 API calls. The 3-stage funnel reduces this to ~50,000 calls while maintaining accuracy. Stage 0 eliminates obvious failures fast. Stage 1 finds domain fit. Stage 2 ensures production quality. Result: 70% cost savings with 96%+ accuracy.
Weights adapt based on use case profile - Creative, Analytical, Operational, Support, Technical, or Regulatory
Programmatic scoring: word count, structure, keyword matching, professional language. Multi-judge rubric 0-100 scale.
IFEval constraint satisfaction - did the model do exactly what was asked? No more, no less.
GSM8K accuracy for math, multi-step problem decomposition, logical coherence in complex tasks.
RAGAS metrics for RAG tasks, citation checking, TruthfulQA scores. Catches hallucinations.
Schema conformance testing. Critical gate (≥70) for operational use cases. Validates parseable output.
JailbreakBench resistance, RealToxicityPrompts scoring. Hard gate (≥70) for customer-facing applications.
p50/p95 response time metrics. Measured in milliseconds. Critical for real-time applications.
$/task using native token pricing. Normalized and inverted (lower cost = higher score). Free models score 100.
Accuracy degradation testing with long inputs. Measures performance at 4K, 32K, 128K+ token contexts.
FLORES-200 benchmark across 5+ languages. Tests translation quality and cultural appropriateness.
Same input, different outputs, objective measurement - methodology demonstration
Note: This is a theoretical example demonstrating our methodology. In this scenario, Model A scores higher for creative marketing while Model B excels at speed and technical precision. Context matters. Our methodology tests all scenarios across all 331 models to find the best fit for each use case.
We will publish comprehensive rankings after completing our evaluation pipeline
Be the first to receive comprehensive LLM rankings for your CPG use cases. Share your specific needs and we'll notify you when results are available.
Note: The methodology and examples shown on this page are our planned testing framework. We are currently building the evaluation pipeline and have not yet completed full testing of all 331 models across the 20 CPG use cases. The scoring examples demonstrate how our system will work once operational. We test all models with temperature 0.0-0.2 for deterministic, reproducible results. No vendor bias. No cherry-picking. Just objective measurement.