We Tested Every Major LLM. Most Failed Our 60-Point Bid Quality Checklist. 

We put the world’s leading LLMs to the test. We ran them against our 60-point bid quality checklist. The verdict? Most failed. 

And that’s the problem. Fluency isn’t enough. Bids don’t win because they sound smooth. They win because they’re compliant, evidence-based, persuasive, and written in your voice. 

That’s why AutogenAI doesn’t just pick a model off the shelf and hope. We test every LLM we use against our 60 proprietary benchmarks, the guardrails that define what a winning bid looks like. 

Why General AI Doesn’t Measure Up 

General-purpose models are trained to predict the next word. They’re good at generating text that sounds plausible. 

But bids aren’t about plausible. They’re about persuasion. They’re about winning. That means every draft needs to be: 

• Structured — following the logical sequence evaluators expect. 
   

• Compliant — directly answering the requirement, no gaps or vague filler. 
   

• Clear — written in plain, direct language evaluators can absorb under pressure. 
   

• Evidence-based — embedding case studies, proof points, and metrics. 
   

• Persuasive — highlighting differentiators and benefits, not just features. 
   

• Evaluator-friendly — scannable, easy to navigate, focused on what matters. 
   

When we ran leading LLMs through this checklist, most collapsed. They could generate text. But they couldn’t generate bids that evaluators would accept, trust, or award. 

How AutogenAI Sets the Bar 

That’s why we built AutogenAI differently. 

• Benchmark-driven testing. Every model is stress-tested against our 60-point checklist. If it can’t deliver on structure, compliance, evidence, clarity, and persuasiveness, it doesn’t make the cut. 
   

• Multiple LLM orchestration. We use up to 20 different LLMs, selecting the right one for the right task at the right time. Need structure? One model excels. Need fluent prose? Another performs better. Need fact-checking? We switch again. If one model goes down, there’s always a fallback. 
   

• RAG for reliability. To reduce hallucination, we pioneered the use of retrieval-augmented generation (RAG). Every draft is grounded in your trusted sources, with clear citations back to your library or validated external content. That means bids are persuasive and defensible. 
   

The combination of benchmarks + multiple models + RAG means every draft is structured, persuasive, and reliable enough to submit with confidence. 

Human-Led, AI-Supported 

And even the best system needs guidance. That’s why AutogenAI emphasises the Train, Direct, Review, Refine cycle: 

• Train: Feed it with your best content and tone. 
   

• Direct: Guide it with context and intent. 
   

• Review: Check compliance, nuance, and accuracy. 
   

• Refine: Polish and improve, then loop back. 
   

This process, combined with our guardrails and model testing, is how raw AI output becomes winning bid content. 

Proof It Works 

This is already driving results: 

• Technology company pilot. Produced 13,000 words in six hours — but the breakthrough wasn’t speed. It was accuracy. The drafts passed internal compliance checks on the first pass. 
  “Generic AI gave us fluent nonsense. AutogenAI gave us drafts we could actually use.” 

• Government outsourcing provider. Achieved 10.4% revenue growth while non-user peers in the same sector fell -19.3%. Rigorous benchmarks turned into measurable market advantage. 

• Healthcare staffing provider. Doubled throughput without adding staff while cutting evaluator pushback to near zero, thanks to drafts grounded in cited, trusted sources. 
  “We’re no longer wasting time fixing errors. We’re focusing on persuasion.” 

• Independent academic research. Across construction, outsourcing, and healthcare, AutogenAI users grew revenue +12.4% (FY23–FY24) while comparable non-users declined -7.1%. 
   

The Truth of It  

Not all LLMs are created equal. Most fail when tested against what evaluators actually care about. 

AutogenAI sets the bar differently. With 60 proprietary benchmarks, the use of multiple LLMs, pioneering use of retrieval-augmented generation, and human-in-the-loop guidance, we make sure every draft isn’t just readable; it’s reliable, persuasive, and built to win. 

Ready to see how AutogenAI outperforms the hype? Book a demo.