readme

README.md

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 title: Dowser
-emoji: 📉
+emoji: ⏱️
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 colorTo: yellow
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+## Problem
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+AI teams are data constrained, not model constrained and waste millions retraining models on data with little or negative impact.
+
+They spend most of their budget collecting, processing, and labeling data without knowing what actually improves performance.
+
+This leads to repeated failed retraining cycles, wasted GPU runs, and slow iteration because teams lack insights in which datasets improve the model and which degrade it.
+
+## Solution
+
+Influence guided training has been shown to halve the convergence time. [*Dowser by Durinn](http://durinn.ai/)* tells AI teams which training data improves model performance and which data hurts it, democratizing what big model providers are doing.
+
+## Product
+
+[*Dowser*](https://durinn-concept-explorer.azurewebsites.net/) doesn’t just recommend data or provide infrastructure — it directly  benchmarks models to produce confident influence scores, with sub-**2-minute** cached results and **10–30 minute** fresh evaluations across **100 open source datasets** on a 8gb RAM and 2 vCPU host.
+
+## How it works
+
+Teams define a target capability or task → *Dowser* identifies high impact datasets from [Huggingface](https://huggingface.co/) and suggests optimized training directions.
+
+## Why now?
+
+- Training costs are exploding while performance gains are flattening
+- Synthetic data is increasingly contaminating training pipelines
+- Teams need precision, not more data
+- Influence methods are now viable via proxy models and distillation
+
+## Market
+
+- Every company training or fine tuning LLMs
+- 59% of AI budgets go to training data
+- 40% of firms spend over 70% of AI budget on data
+- Initial wedge is small and mid sized model teams