From your data to your AI solutions and back.
Built for any AI setup...
Whether training from scratch, fine-tuning, building embeddings, optimizing RAG pipelines, or third-party prompting, hiddenweights can help.
Context optimization
Build effective prompts and select the most influential in-context data for your task.
Fine-tuning and training
Fine-tune models—large or small—to achieve the highest accuracy while minimizing expensive data collection
Embedding, RAG, and search
Get custom task-optimized embeddings, and experience the power of custom rerankers to optimize RAG pipelines.
With the right data...
Maximize the value of your data by task- and
model-aware data curation, synthesis, filtering,
and representation.
Data selection
Identify useful and harmful data for your model training and get insights on the most effective/useful data sources to access.
Data synthesis
Fill in the gaps in your training data by synthesizing the high-quality data your models need the most.
Data actions
Take actions on data to fix errors, forget concepts, enhance performance or remove brittleness of AI outcomes.
And actionable observability.
Attribute model outputs back to data, identify data gaps to inform data actions.
Attribution
Link each outcome to your data, for auditing, explanation and insights.
Diagnosis
Develop faster by avoiding costly trial and error and go directly to the root cause.
Insight and provenance
Achieve effective data governance in the the new complex AI setup that adapts to changes to data and solutions.
Our Team
University of Waterloo Professor, former director / distinguished engineer at Apple, co-founder of Tamr and inductiv. Fellow of the Royal Society of Canada, ACM, and IEEE.
Former director of engineering at Google, principal engineer at AWS, and researcher at Microsoft Research.
CMU Professor, former MIT PhD (Sprowls thesis award winner) and Stein Fellow at Stanford.
