How We Benchmark Extract on CUAD
Higher accuracy at 60% lower cost on legal contract extraction
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No Model Training on Your Data at ScaleDown
Your data trains your competitors’ models. At ScaleDown, it doesn’t.
Self-Hosted Deployment of ScaleDown
ScaleDown can run entirely within your infrastructure. Same models, same API, same performance hosted on your hardware, inside your network, under your control.
Task-Specific Models are the New Frontier
For the past two years, the default enterprise AI strategy has been the same everywhere.
Zero Data Retention at ScaleDown
At ScaleDown, ZDR means one specific thing “your prompt data is never written to disk.” It exists in memory for the duration of the request and nowhere else.
How We Benchmark Summarize on QMSum
How we got competitive ROUGE scores with our Task Specific Language models at 93% lower cost than the cheapest GPT baseline
How We Benchmark Classify for Model Routing
A task-specific small model outperforms GPT-5.4 Nano and Mini on intent classification, at 200x lower cost.
Intent Classification for AI Financial Planners
You Don’t Need a Frontier Model to Route a Query. Task-specific classification SLMs solves this bottleneck in financial planning AI.
Context Compression for Data Security and Privacy Platforms
Why extractive SLMs for pruning long contexts are a natural fit for code-to-cloud data intelligence
Extraction for E-Commerce Chatbots and Voice Assistants
How task-specific extraction models cut 80%+ off the most expensive call in your customer service AI pipeline